The market for Covid-19 vaccines and the tipping point to herd immunity

by Ajay Shah.

Many firms are developing Covid-19 vaccines. Enormous resources have to be deployed, up front, to develop a vaccine and to build manufacturing capacity. It is likely that many vaccines will get through to approval in mature regulatory regimes. Not all vaccines will work identically for all situations, e.g. some vaccines may work better for an elderly person than others.

It is commonly assumed that the global market size for a Covid-19 vaccine is about 6 billion people. In this article, we argue that this might not be the case. Let's think about the situation in the market once one or more vaccine reaches the market.

The buyers perspective before vaccine sales have commenced

The private gain for an individual from buying a vaccine are shaped by the probability of getting sick when leading an unconstrained life. This is shaped by the extent to which Covid-19 has burned through the communities that the person plans to engage with. As an example, in the slums of Bombay or Delhi, herd immunity has set in. A person living there knows that few people in her circles are now getting sick, and she feels relatively safe. Well known factors such as age and co-morbidities will also shape the threat perception of each person. Therefore, for her, the gains from a vaccine are relatively modest, and the willingness to pay is small.

In each city of the world, there is a different numerical value for the attack rate (the fraction of people who are infectious) and the extent of immunity. The state of the epidemic in Pune is different from that in Bombay. As time passes, each city is inching towards herd immunity, and the passage of time thus diminishes interest in paying for a vaccine. Vaccine IP and manufacturing facilities are wasting assets.

It it were possible to develop a combination of tests that add up to an `immunity passport', then the price of this test and the odds of coming out positive would shape the demand function for the vaccine.

Progress on immunisation and herd immunity

Into this world, let us imagine that the sale of multiple vaccines commences. At first, there would be a rush of demand and high prices. As immunisation progresses, the attack rate would go down and the gains from buying the vaccine would further go down. In places like Bombay and Delhi, where a considerable proportion of the population has already been exposed to the disease, when a modest fraction of the population is vaccinated, this could tip the population over into herd immunity, and the disease could die down.

In such a world, vaccine makers face the prospect of a short hot market. At first, vaccine demand will be high and the factories will not be able to keep pace. Competition will come about and that will exert pressure on prices. In a city like Bombay, with about 20 million people, after (say) 5 million persons buy the vaccine, this may significantly change the threat perception in the eyes of the average individual. Vaccine demand would then decline.

Under such numerical values, the market potential in Bombay is not roughly \$50 $\times$ 20 million people or \$1 billion, but perhaps more like \$25 $\times$ 5 million people or about \$125 million.

All of this reduced revenue potential will go to the first few firms that get 5 million doses into the Bombay market. Competition would exert downward pressure on the price, demand would tail off as herd immunity sets in, and there would be a price crash. The late comers would flood the market with output but would obtain low revenues in return.

The vaccine demand collapse in a simple model and in the real world

We have always known that a vaccine is not just a private good; there is a positive externality. The novel idea of this article is about tipping points.

Consider a simple model in which herd immunity is achieved at 60%. Suppose 50% of the population is already immune and knows it. The first 10% that gets the vaccine tip the system over to $R_0<1$ and then the fires start dying out. Once the fires start dying out, the attack rate goes down, the threat perception changes, and the incentive for private people to buy the vaccine drops a lot. Under these conditions, the positive externality imposed by vaccine purchase by the early vaccine buyers, upon the overall system, is particularly large.

A key factor that drives behaviour in this model is that when a person is immune, she knows it and then has no incentive to buy a vaccine. In the real world, people don't know whether they are immune, and would be more inclined to buy a vaccine just to be safe. In the limit, the veil of ignorance is complete, nobody is able to assess the threat, and everyone wants to buy a vaccine.

In the real world, the veil of ignorance is not complete. At every place, people do have a personal judgement about the threat level based on the extent to which their friends and family are getting sick (or not) per month. Age and co-morbidities will also shape vaccine demand. As a general principle, it is always wise to think that humans are sentient optimising creatures. Individuals have a noisy estimator of the threat that they face and this will shape their willingness to pay for a vaccine.

Wall street tells Main street what to do

These problems feed into the thought process of private firms and shape the commitments of capital to the problems of vaccine development and manufacturing when faced with a novel epidemic. 

Numerous vaccines are under development. The process of vaccine approval is necessarily slow. At present, we generally think that over time, one by one, many of these vaccines will get through to the market. By the reasoning of this article, the first few will get through, within a few months the market will collapse, and all funding will be yanked for other projects. This will be a bit reminiscent of how funding for vaccines against Sars-Cov-1 was abruptly yanked when the funders realised that Sars-Cov-1 had reached $R_0<1$.

The numerical values used here (e.g. 60% for herd immunity, 5 million immunised in Bombay to tip over into herd immunity, $50, etc.) are of course purely illustrative. To translate these ideas into practical calculations requires data on the extent to which immunity has come about. In many places worldwide, there are good estimates of the persons who have antibodies, but there is more to immunity than measured antibodies. In India, the information available about the state of the disease in (say) Bombay is rather poor.

If we take this dynamics of the vaccine market seriously, vaccine makers have an incentive to create such datasets. Alongside the construction of such datasets, there is a need for derivatives trading on underlyings such as the fraction of Bombay residents who have antibodies.

The argument of this article is a special case of the long-standing problems of incentives for vaccine development. An effective pathway for state intervention, and philanthropic capital, lies in offering contracts for R&D and manufacturing which change the incentives of private persons to engage in these activities.

Implications

To the extent that this reasoning is correct, individuals will at first face a vaccine market with high prices and shortages. For many individuals, particularly for low-risk persons, there is a tradeoff between paying more to get the vaccine early versus paying less to get it late or even to not get vaccinated if the pandemic has subsided.

For firms with a vaccine under development, this article paints a winner-takes-all scenario, where the first few vendors who get output on scale will capture all the revenue. To the extent that this reasoning is correct, plodding along to the finish line late will induce low revenues.

For policy makers and philanthropic capital, it is important to avoid a `coronavirus winter', a collapse in coronavirus research of the kind which happened after the SARS epidemic achieved $R_0<1$. There is enormous knowledge, and capable teams, which has been created by the early gold rush of building vaccines against SARS-Cov-2. This knowledge should not be lost. As an example, it would be nice if research groups will publish research papers and release code before they put out the lights. We need to think of the sustainable frameworks, where we achieve a new normal of high R&D into pathogens that can trigger pandemics.

Watching markets work: Structural change in the Nifty implied volatility

by Surbhi Bhatia, Anjali Sharma, Susan Thomas.

When options are actively traded, a new fact about the economy is revealed in option prices: what option traders think that the market volatility is going to be. This derived volatility forecast is called 'implied volatility' (IV). The Nifty IV measures the Nifty volatility that market participants forecast for the coming two to four weeks.

This was not visible without derivatives trading. Nifty options trading began in year 2000. This gives us close to 20 years of data on market volatility forecasts in 2018. We used this history to calculate the daily IV time series. We used the official daily settlement prices of eight at-the-money options, for both the call and put options.

In the early years, the options market was quite illiquid, so the IV every day could be quite noisy. On some days, the computed IV was wrong in overstating the views of market participants, and on other days, the computed IV was wrong in understating the true views of market participants. If we wish to know the IV at a point in time, we have to bring options market liquidity integrally into the calculation. For the present purpose, we find it useful to use monthly averages, which we feel are adequately reliable. Our estimates of IV are protected, first by the averaging that goes into the official settlement price, and second when we average the daily values across the month.

Such a time series allows us to examine the views of traders about future volatility through these years. Were the traders' expectations of market volatility the same today as when the markets started? Did the market volatility rise to reflect the turmoil in the markets during the global financial crisis? How do IV values compare across the two banking crises, of 2001/2002 and 2017/2018?


Our first step is to look for structural breaks. If there are different IV regimes in the series, this test will identify time points at which the regime shifted from one to another. We use the Perron-Bai algorithm as implemented by Achim Zeileis et. al. (the `strucchange' R package). This yields the following result:

This shows four phases of the story of Nifty IV:

June 2000 to April 2006: The first regime is from the outset till April 2006. This period started after the nuclear tests of 1998, when Nifty had gone to its lowest value (887). IV surged when the UPA won the elections and the stock market crashed on 17 May 2004. The average IV in this period was 21.1%.

April 2006 to August 2009: The second period ran for around three years, from April 2006 till August 2009. Roughly speaking, this corresponds to the global crisis. In this period, the average volatility was 35.4%, with a peak value of about 70%. This value of 70% reminds us of the peak value seen with the S&P 500 implied vol, in October 2008, where the VIX touched 87% intra-day.

August 2009 to June 2012: The market returned to an average IV of 21.9%, which is quite close to the value seen in the previous period.

June 2012 to May 2018: The final period is one of calm, starting from Jun 2012 onwards, where IV has averaged a level of 15.2%.


The IV is the market's perception of future volatility. It is interesting to think about what changed in the economy that changed the views of financial market participants across each of these break dates. Why was the IV low in the first period, then high, then low again? Why is this latest period the lowest?

This was the period of demonetisation and Donald Trump. The world is beset with economic and geopolitical risk. In India, the trailing P/E has risen to historically high levels. There is a banking crisis and a large fraction of the equity index is banks. The market seems to have conquered these fears and thinks that the future Nifty volatility will be low. This is not only true for Indian equity: the US VIX also achieved its lowest values ever (about 9%) in November 2017.

It is interesting to look back at previous periods of calm and of stress. For instance, we see that in 2007, implied vols were unusually low. Once again, this was similar to the behaviour of the US VIX before the crisis as well. There are concerns about the extent to which market participants are good at thinking about one security at a time vs. being good at macro forecasting.

Expressing a view on a Trump win

by Parikshit Kabra.

A spectre is haunting global capitalism - the spectre of Donald Trump. A big question looming over the global financial system today is: Will Donald Trump make it? Each of us have views about whether he will. What will the short term impacts upon financial markets be, if Trump wins? What position should one adopt, if one believed that Trump would win?

Decision markets

The simplest answer is: One should trade on a decision market to express the `Long Trump' view. This is hard to do for people constrained by capital controls. And, for most people, the problem is not so much about expressing a Long Trump view as the problem of understanding the vulnerabilities in their portfolios.

Impact on the USD

The USD has risen by 2 to 12% in the year following all US presidential elections from 1980 onwards. A Trump win could boost the USD due a 'safe haven' effect. With fears about the world economy, many investors could withdraw their investments in Emerging Markets.

If Trump loses, there could be two effects working in opposite directions. Reduction about fundamental risk in the US would be good for the dollar, but this would not be augmented by a safe haven effect.

Gold

A Trump win will likely lead to a rise in Gold prices, as buyers of gold are passing a vote of no-confidence in civilisation. When George W. Bush won the 2nd time, there was a sharp surge in the price of gold and it rose to an all-time high.

Conversely, if Trump loses, gold prices may go down somewhat as that fear subsides.

Nifty

If Trump wins, there will be greater uncertainty for the world economy, VIX will go up, flows into EMs will go down, and that's bad for Nifty. Conversely, if Trump loses, there would be a small positive impact upon Nifty.

The Indian IT industry

The Indian IT industry is vulnerable to changes in immigration laws in the US. With Trump threatening to make immigration more difficult, make visa applications harder and also possibly introducing an outsourcing tax, a Trump victory should imply a fall in the IT stocks across the board.

The adverse impact of Trump would be greater for low value firms that do more body shopping. One way to setup a trade would be to sort the IT companies into top and bottom quartile by revenues per employee. The trade to employ would be to be long the high revenue-per-employee firms and short the low revenue-per-employee firms. This would yield a hedged portfolio where all other macroeconomic and industry news cancels out, and does well if Trump wins.

Pharmaceutical companies

While many American analysts believe that a Clinton presidency will mean a drop in pharma stock prices due the price pressure she might bring, the case might be the opposite in India. Some traders believe that the Indian pharma companies, which provide generic drugs for the US market, are gaining from the introduction of Obamacare (which has been pushing for greater use of the cheaper, generic drugs). Trump, who has promised to repeal this Act, may reduce the pressure to use generics and thus hurt Indian pharma companies.



Parikshit Kabra is curious about financial markets at Bain Consulting.

The changing landscape of equity markets

by Nidhi Aggarwal and Chirag Anand.

The arrest of a London based algorithmic trader, Navinder Singh Sarao, on charges of triggering the US flash crash of 2010 has once again brought regulatory concerns on high frequency trading (HFT) to the forefront. With the underlying fear that the use of high speed complex algorithms can pose systemic risk, regulators worldwide are considering actions to tighten their grip on HFT. The Indian securities markets have not remained immune to such concerns, and the securities market regulator, SEBI, has indicated that steps will be taken to keep the level of algorithmic trading (AT) in check. Very recently, even RBI in its annual Financial Stability Report expressed its concerns regarding high levels of algorithmic orders in the Indian securities market.

Despite all the fears and the measures that are being taken to curb HFT, one needs to note that the evidence regarding how HFT (or AT) hurts the market is yet to be established. Concerns such as higher percentage of algorithmic orders creates higher level of systemic risk in the financial system are not backed by strong empirical evidence. Studies examining AT/HFT trading only find evidence contrary to this popular notion (Brogaard et al., 2015; Thomas and Aggarwal, 2014). Other studies (Biais and Faoucault, 2014) examining the overall effect of AT/HFT on market quality find that higher levels of AT/HFT improves market quality by increasing liquidity and price efficiency. In spite of this overwhelming evidence on the effect of AT, regulatory fears on how increased market complexity can disrupt the financial markets remain.

An analysis at the Finance Research Group, IGIDR aims to provide a few insights on the proliferation of HFT (or AT) in the Indian markets. Using a unique tick by tick orders and trades dataset from one of the most liquid stock exchanges in the country, the National Stock Exchange (NSE), we examine how AT/HFT has changed the equity market structure in India. In addition to the usual details of price and volume, the data contain details of whether an order was sent by an AT or a non AT, and whether the order was a new order, or an old order that was modified or cancelled. A clear demarcation of orders sent by AT versus non AT, enables us to examine the characteristics of how AT's trade in the markets vis-a-vis non AT.

We analyse two periods: a low AT period (November-December 2009) and a high AT period (November-December 2013). Few points emerge:

• Between the two periods, percentage of orders entered by algorithmic traders increased from 11.36% to 62.76% on equity spot, from 38.93% to 93.72% on single stock futures (SSF), and from 21.29% to 86.79% on single stock options (SSO).
• On the most liquid segment of NSE, that is the Nifty options, the percentage of new orders entered by AT increased from 19.60% to 93.56%.
• On Nifty futures contract, it increased from 21.57% to 91.23%.

The values indicate that a large proportion of the orders that are entered on NSE today are by algorithmic traders (AT). A majority of these orders are limit orders, indicating that instead of going for the special orders that the exchange offers, AT prefer the traditional limit orders which offer them greater flexibility to manage their orders.

Do AT supply liquidity or demand liquidity?

The increase in percentage of AT orders in the market raises the concern on whether that increase corresponds to a similar increase in liquidity supply, or, whether they consume liquidity from non algorithmic traders. For each segment on NSE, we analyse the percentage of trades in which AT supplied liquidity versus the trades where they demanded liquidity. When an order that comes to the market trades against an existing order in the book, the new order is said to have taken (demanded) liquidity, while the existing order is said to have provided (supplied) liquidity.

The graph above indicates the share of AT orders in total liquidity demanded increased across all the segments between the two periods. However, this matches with their share of orders in total liquidity supplied to the market in all except the Nifty options market. We further break this analysis into who supplies liquidity to whom. This is depicted in the following graph.

In the above graph, the top-left panel indicates the percentage of trades in which AT demanded liquidity from another AT. On the spot market, for example, AT took liquidity from other AT in 6.34% of trades in 2013. The top-right panel indicates the percentage of trades in which non AT demanded liquidity from AT. The bottom left panel indicates the percentage of trades in which AT demanded liquidity from non AT. Finally, the bottom right panel indicates the percentage of trades in which non AT demanded liquidity from non AT.

A difference in the values in the bottom right panel from 100 indicates the AT-intensity, that is the percentage of trades that occurred on NSE in which AT was either on one or both sides of the trade. For example, on the spot market, in 2013, the percentage of trades in which AT were present atleast on one side of the trade was (100 - 44.3)% = 55.7%.

Of particular interest are the top-right and bottom-left graphs. These two graphs indicate non AT demand for liquidity from AT, and AT demand for liquidity from non AT, respectively. The values in the graph reinforce the observation that AT demand as much liquidity from non AT as they supply to them for all except the Nifty options market.¹ This suggests that the concern that AT consume liquidity from non AT does not hold.

We now proceed on to examining how the order placement strategies of AT have changed the market structure on NSE.

Changing market structure due to high speed access

Q:1 How have order placement strategies changed after faster market access? With a majority of the orders coming from AT, we first examine if there has been a change in the order placement strategies by market participants. Specifically, we examine if the increase in the number of orders has translated into a larger number of trades, or are most of the orders that are entered are eventually cancelled?

The table below indicates the percentage of orders that get traded and cancelled by AT and non AT.

All values as % of total orders entered
	Spot		SSF		SSO		Nifty futures		Nifty options
	2009	2013	2009	2013	2009	2013	2009	2013	2009	2013
AT	12.42	62.19	39.18	93.30	20.56	84.89	11.11	87.84	21.71	93.38
Traded	3.91	12.37	1.59	2.20	0.74	2.61	3.02	7.73	1.49	7.47
Cancelled	8.31	49.73	37.52	90.91	19.65	82.03	7.99	79.88	20.15	85.88
Non AT	87.58	37.81	60.82	7.70	79.44	15.11	88.89	12.16	78.29	6.62
Traded	56.11	25.69	14.17	3.00	24.95	6.03	45.37	8.18	32.76	4.43
Cancelled	21.75	7.24	44.88	3.20	44.05	6.52	39.67	2.70	43.22	1.63

The first row in the table indicates the percentage of orders entered by AT. As discussed eariler, the share of AT in the total number of orders sent to the NSE has risen significantly. The second row in the table indicates the percentage of AT orders that got traded.

The table shows that the increase in percentage of new orders entered by AT is not matched with a higher percentage of orders that got traded. Instead, we see a decline in the percentage of traded orders across all the five segments (spot, SSF, SSO, Nifty futures, Nifty options). For example, on the spot market, the percentage of orders that got traded declined from 60.02% in 2009 to 38.06% in 2013. We also find a significant increase in the percentage of orders that got cancelled in the high AT period (2013). Of the total unique orders that came to NSE, the percentage of orders that got cancelled increased from 30.06% in 2009 to 56.97% in 2013 on the spot segment, from 82.40% to 94.11% on the SSF and from 63.70% to 88.55% on the SSO. On Nifty futures, this percentage increased from 47.66% to 81.58% and on Nifty options from 63.37% to 87.51%.

While there could be legitimate reasons for such cancellations (Hasbrouk and Saar, 2009), the increase in the percentage of cancelled orders raises concerns about phantom liquidity (also known as spoofing, flickering quotes, or fleeting liquidity), that is, the fear that high speed access allows the trader to post an order for everyone to see, but withdraws it before anyone can act on it. We examine this concern in the next question.

Q:2 Do order cancellations occur at very short intervals? Higher percentage of order cancellations, by itself is not a matter of concern. The concern instead is that these orders might be getting cancelled in such short a time that other traders, who do not have the advantage of fast market access, are unable to execute their orders against such orders. Or, these orders could be sending signals of false liquidity. In order to pin down these concerns, the evidence of cancellations needs to be combined with evidence of speed of cancellations - or the lifespan of the orders. If a majority of the orders are cancelled in very short time intervals, then it could be suggestive of phantom liquidity in the markets.

Cancelled orders as a percentage of total orders entered on Nifty options

Cancelled orders as a percentage of total orders entered on SSF

The graphs above indicate the percentage of orders that got cancelled in less than a second on the two most liquid NSE segments: Nifty options and single stock futures (SSF). The graphs suggests that in the high AT period (2013), more than 70% of the orders entered on the SSF and about 54% of the orders entered on the Nifty options market got cancelled within a second.² These values are substantially higher than the values in the low AT period of 2009, during which 7.83% and 14.96% of orders got cancelled within one second on SSF and Nifty options.

A useful question to ask is how these numbers compare with the global markets. A similar analysis for the US equity markets by SEC indicates that 45.9% of the orders were cancelled within a second during Q2 2013.³
 
Q:3 Is fast too fast? The analysis above indicates that high speed access has made cancellations too fast. The next question that becomes important to ask is, ``Is this too fast''? To characterise the intensity of what is fast, we use the SEC's approach. In a speech in April 2014, by the then Associate Director of SEC, Gregg Berman, noted:

``If the speed of cancellation is much quicker than the speed at which those quotes can be accessed, then I would say quote cancellations are not only fast, but perhaps they are too fast. However, if market participants can lift quotes just as quickly as others can cancel them, I would say that the cancellations might be fast, but not necessarily too fast."

And its relevance in informing the policy debate:

``If quote cancellations are indeed too fast for the rest of the market to keep up, it might make sense to slow down this particular aspect of the markets, perhaps with some sort of minimum quote-life requirement. But it the data shows that at least some market participants can access quotes just as quickly as they can be canceled, this suggest that both sides of the market are very fast and if you want to slow down the market -- in a way that does not bias one side, you would need to not only address the speed of quote cancellations, but also the speed at which liquidity is taken."

We examine this by comparing the lifespan of cancelled orders with that of the traded orders. We once again restrict our discussion to the two most liquid segments on NSE: Nifty options and SSF.

The graph above shows the results for Nifty options for cancelled (top-panel) and traded (bottom-panel) orders. A shift from the red to yellow region indicates increase in the speed of order cancellations or execution. In 2009, while about 30% of all cancelled orders remained in the book for less than a second, about 55% of all traded orders were the result of some trader hitting limit orders within that same time period. These numbers rose to 65% and 80% respectively in 2013. Also noticeable is that the number of modifications on these cancelled orders is in the range of 0-5. This suggests two features of trading activity on the Nifty options market:

1. A majority of the orders that get cancelled do not undergo large number of modifications.
2. Access to speed has indeed increased the speed of order cancellations, but this speed is lower than the speed of execution.

The Nifty options inferences do not however hold for the SSF. In 2009, the percentage of cancelled orders within a lifespan of less than a second on SSF was almost negligible, while the percentage of traded orders within the same lifespan was less than 40%. The numbers changed dramatically in 2013. The graph shows a shift from red to yellow region for cancelled orders, but only a shift from red to orange region for traded orders. The percentage of cancelled orders with a lifespan of less than a second was about 75%, while the percentage of traded orders within the same lifespan was about 45%. This indicates that the speed of order cancellations surpassed the speed of trade executions in 2013.

Summary

In a nutshell, the findings can be summarised as:

1. The share of algorithmic orders in total orders that come to the market has risen significantly.
2. Except for the Nifty options market, the share of algorithmic traders in liquidity demand matches with their share in liquidity supply.
3. A large majority of the orders on NSE are cancelled, with most of them occurring within a second of order entry.
4. The speed of order execution is higher than the speed of order cancellations on Nifty options. This is however not true of the SSF segment of the NSE.

The above analysis does imply that the order placement activities have changed significantly with a lot of cancellations occurring within short time-frames. However, to analyse whether this degree of cancellations could be hurting the other market participants, it is critical to examine the where these quote cancellations occur? If most of these cancellations are occurring around the best bid and ask prices (or even the upto level 5 depth of the market), such cancellations could be a cause of concern. Further analysis aims to capture this aspect.

References:

 
High-frequency trading and extreme price movements by Brogaard J, Carrion A, Moyaert T, Riordan R, Shkiklo A and Sokolov K, 2015, Working Paper

The causal impact of algorithmic trading on market quality by Susan Thomas and Nidhi Aggarwal, 2014. IGIDR Working Paper.

HFT and market quality by Biais B and Foucault T, 128, 2014 in Bankers, Markets and Investors, p. 5-19.

Technology and liquidity provision: The blurring of traditional definitions by Hasbrouck J, Saar G, 2009. Journal of Financial Markets, Volume 12, Issue 2, May 2009, p. 143-172.

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Footnotes

1. The reason for the difference in the nature of AT liquidity demand and supply on the options market needs further investigation.
2. We record similar values for the rest of the market.
3. The findings are also comparable to studies investigating fleeting orders. For example, Hasbrouck and Saar (2009) find that 36.69% of the limit orders get cancelled in less than two seconds on INET.

The imprecision of volatility indexes

by Rohini Grover and Ajay Shah.

A remarkable feature of options trading is that it reveals a forward-looking measure of the market's view of future volatility. This was first done by CBOE in 1993 with the S&P 500 index options, with an information product named `VIX' which reveals the market's view of future volatility of the US stock market index. CBOE computes and disseminates VIX every 15 seconds. VIX is often termed a `fear index' as it conveys the fears of the market. It has found numerous applications:

1. Time-series econometrics processes historical data to help us make statements about the future. VIX brings a unique forward-looking perspective into time-series analysis.
2. VIX measures uncertainty in the economy e.g. when examining the effect of macroeconomic shocks (Bloom, 2009).
3. The international finance literature has emphasised the role of VIX in shaping capital flows to emerging markets [example]. E.g. it is interesting to look at what happened in India on the days in which a very large rise or fall of the VIX took place.
4. Trading strategies can be constructed which employ VIX as a tool for making decisions for switching between positions.
5. VIX based derivatives offer methods to directly trade on VIX. In the US, CBOE introduced VIX futures and options on March 26, 2004 and February 24, 2006 respectively. In 2012, the open interest for these contracts was at 326,066 contracts and 6.3 million respectively. In India, futures on VIX have been launched at NSE, but the contract has not taken off.

In this field, we treat VIX as a hard number -- we talk about a value of VIX such as 24.53 as if it is known precisely. But VIX is computed from a set of option prices. These option prices suffer from microstructure noise and from the limits of arbitrage. Each option price is only an imprecise reflection of the thinking of the market. This raises concerns about the extent to which imprecision in option prices spills over into imprecision of VIX.

In a recent paper, The imprecision of volatility indexes we offer a method for measuring the imprecision of VIX, and find that the measurement noise is economically significant.

VIX is a statistical estimator working on a dataset of option prices. Different estimators exist (e.g. the old VIX vs. the new VIX). Regardless of what estimator is used, the foundation — the option price — suffers from microstructure noise and is shaped by limits of arbitrage. Noise in option prices will induce noise in VIX. The only question is that of understanding how imprecise is the VIX.

For an analogy, consider estimation of LIBOR. Each dealer reports a reading of LIBOR. We recognise that each value obtained is a noisy estimator of the true LIBOR. This is aggregated using a simple robust statistics procedure to obtain an estimate of LIBOR. Bootstrap inference is used to obtain a confidence interval about how imprecise our estimator of LIBOR is. [Cita and Lien, 1992, Berkowitz, 1999, Shah, 2000].

A similar strategy can be applied to the measurement of VIX. Each option should be seen as a noisy estimator of the implied volatility. Bootstrap inference can then be used to create a distribution of the vega-weighted VIX (VVIX). This yields a confidence interval for VVIX.

As an example, for a sample of end-of-day S&P 500 options, on 17th September, 2010, the at-the-money (ATM) options with 29 days to expiry show significant variation and take values between 15% and 21%. Our methods yield a distribution of the estimated VVIX:

As the graph above shows, the point estimate for the VVIX is 21.53% and this is what all of us are used to talking about. But the noise in option prices has induced an economically significant imprecision in our estimated VVIX. The 95% confidence band, which runs from 20.8% to 22.32%, is 1.5 percentage points wide. This turns out to be an economically significant number: the one-day change in VVIX is smaller than 1.5 percentage points on 62% of the days. This suggests that on 62% of the days, we know little about whether VVIX went up or down when compared with the previous day.

To conclude, we got a huge step forward when options trading improved our knowledge of the universe by giving us a forward looking estimator of future uncertainty: a quantitative peek into the structure of expectations of traders. However, market microstructure noise and the limits of arbitrage hamper this. VIX is not a hard number; there is economically significant imprecision in our observation of VIX. This insight may make a difference to many applications of VIX.

Once we start using the term `estimation' about VIX, we must pursue improvements in the estimation of VIX in order to address these issues of microstructure noise and the limits of arbitrage. One first step in this direction is Grover & Thomas, Journal of Futures Markets, 2012.

Concerns about individual investors on the Indian equity derivatives market

by Nidhi Aggarwal, Rohini Grover, Susan Thomas.

A recent article in the Business Standard by Praveen Chakravarty and T. V. Somanathan questions the quality of the Indian equity derivatives market. India is ranked next only to South Korea in terms of both the intensity of derivatives to spot traded volumes and the dominance of retail participation in derivatives trading. It is argued that complex financial instruments are better suited to the requirements of sophisticated institutional investors.

Korea has tried to reduce the large fraction of retail participation in their derivatives markets by increasing the minimum contract size twice between 2012 and 2014. The authors suggest that India consider taking similar action, or increase securities transactions taxes, to deflect retail interest in equity from derivatives trading to spot trading.

Facts about retail investors and their dominance in equity derivatives trading in India

The article says there are "97% retail speculators", and later says there are 83-87% of both retail and proprietary trading. What are the facts?

Exchanges record every trade as a pair of buy and sell orders originating from a specific participant category. There are three broad categories of participant. Custodian trades which mark trades by institutions. Proprietary trades which mark trades by brokers for their own account. The remainder, which are Neither custodian or proprietary trades, are recorded as the retail investor, which include individual investors along with others. Not all that is not an institutional trade is a trade by an individual investor.

We focus on the fraction of the derivatives trade the options, both options on Nifty and single securities. In trading on these instruments, the shares are:

• Nifty options where daily traded volumes are around Rs.1200 billion
  
  • Institutions = 20%
    
  • Proprietary = 48%
    
  • Neither (retail) = 32%
• Stock options where daily traded volumes are just under Rs.100 billion
  
  • Institutions = 17%
    
  • Proprietary = 42%
    
  • Neither (retail) = 41%

Thus, retail individual investor participation are just 30-40 percent of the options trading in Indian equity.

Korean thinking on reducing retail participation

A series of research papers using trade data from Taiwan and Korea found some evidence that individual investors consistently made losses on their trades, and that institutional investors made profits at their expense on average. Such evidence led Korean regulators to explore interventions to reduce the participation of individual investors in these markets and thus, minimise their losses. Their solution to the problem was to increase the minimum contract size so that individual investors find it more expensive to participate in the market, and reduce the positions they take.

Is this malady present in India? We don't know. Would the Korean treatments pass the cost-benefit analysis as required by the Handbook? We don't know.

In order to carry out a regulatory intervention to improve customer protection, it is first important to establish a market failure. We need to establish that there are such investors who are consistently losing. In order to do consumer protection, we must:

1. Understand whether the Korean empirical regularities hold in India;
   
2. Establish WHY this is so;
   
3. Establish a set of optimal alternative regulatory interventions;
   
4. Do a cost-benefit analysis of each a la the Handbook; and
   
5. Do a phased roll-out of the interventions and post-hoc analysis to see if the correct effect is achieved in terms of market outcomes.

The Koreans had the first step done for them (establishing that there is a problem), and by the looks of it, are still searching for solutions. The present state of knowledge on household finance in India does not answer these questions. We don't know if the malady is present, so the question of the treatment cannot arise.

What ails institutional participation?

The BS piece presents the participation of various players on the Indian derivatives markets as based on choice of both institutional investors, and the others. However, institutional investors have often been kept out of these markets by regulation. Examples:

• IRDA has given in-principle approval but has not given operational clarity for equity derivatives trading by insurance companies.
  
• Banks are not permitted to do equity derivatives trading by RBI.
  
• Equity mutual funds lack operational clarity on critical sub-components of equity derivatives trading.
  
• FII participation has been hampered by capital controls and the messy transition into the FPI framework. Trading in the overseas OTC derivatives market (the "PN" market) is hampered.
  
• The onshore OTC equity derivatives market is banned.
  
• Position limits are tiny and do not address the requirements of institutional investors.

This market does not have institutional investors because they are being systematically kept out by regulators.

On a related note, the regulation of currency derivatives is also riddled with mistakes.

The large ratio of derivative to stock traded volume

The discomfort of a much larger traded volume in the derivative compared to the spot is an age-old one, based on fears are that (a) derivatives markets lead to higher volatility, (b) there is market abuse deriving from the leverage of derivatives, and (c) small investors get consistently and persistently hurt.

These fears are not borne out by the facts. Over the entire period that India has had equity derivatives, the volatility has come down, there has been little evidence of a larger incidence of market manipulation in stocks with derivatives, and liquidity has improved.

A research paper from the Finance Research Group analysing the single stock futures markets in India offers a possible explanation for these large derivatives volumes. This paper suggests that in markets where there are severe funding constraints there is a larger participation in derivatives, because these leveraged products allow traders to preserve the efficiency of their trading capital. These funding constraints can be for several reasons. Partly, it could be because emerging economies have a shortage of capital. Partly, it could be because institutional investors who have the capital are forcibly kept out of participating in securities markets. Other mistakes of regulation which are shaping this outcome include the failures on securities lending which hampers short selling.

The paper finds that the Indian markets have the highest dominance of price discovery in equity derivatives compared to what has been recorded in all the other literature on this subject. Information is flowing from the derivatives into the spot prices in Indian equity.

Conclusion

We should be do thorough homework before introducing regulations that interfere with the freedom of private persons. Most of the ills of Indian finance derive from weak financial economics, and lack of due process, at regulators. The solution lies in fixing the regulatory process and not in further reducing freedom.


Finance Reseach Group, IGIDR, Bombay

Analysis of recent regulations on currency futures

by Anjali Sharma.

Background

India has a successful equity market and has done poorly in other aspects of organised financial trading. There is a natural opportunity to use the knowledge and institutional capabilities of the equity market in order to improve other areas. One priority in this evolution has been the currency market. Almost everything that happens on the currency market would work better on exchange, and SEBI/NSE/BSE know how to make trading on exchange work. By importing the good practices of exchange-traded equities trading, we can make easy progress.

Trading in Exchange Traded Currency Derivatives (ETCD) began in India in August, 2008 [link]. From 2008 till June 2013, this market did reasonably well. In Apr-Jun 2013, the traded volume in this segment had reached USD 5.2 billion (daily average) and the maximum open interest (OI) was USD 5.1 billion. Position limits were set at a percentage of open interest(OI) and were determined at client and member level. Since the OI was building up, these limits were blocking big firms but for others it looked okay. For a while, it looked like we were making progress.

In July 2013, a set of measures were taken by RBI and SEBI which sharply restricted the exchange traded market:

1. Banks were disallowed from taking proprietary positions by RBI (Risk Management and Inter-bank dealings, July 08, 2013), and
   
2. Position limits on ETCD were brought down to USD 10 mn for clients and USD 50 million for members, by SEBI. In addition, initial and extreme loss margins were increased by 100%.

As a result of these measures, in the quarter of Oct-Dec 2013, the ETCD market volume dropped to USD 3.2 billion and the OI dropped to USD 1.06 billion. For an analysis of the impact of these restrictions, see Impact of restrictions on the trading of currency derivatives on market quality by Rajat Tayal, IGIDR FRG, October 2013.

Recent actions

On 20th June, 2014, RBI issued two major notifications with respect to participation rules for Exchange Traded Currency Derivatives. These are the most significant actions taken in this segment after July, 2013. One notification addresses participation rules for Foreign Portfolio Investors (FPI ETCD notification). The other is for residents and banks (Residents/Banks ETCD notification). These induce four changes:

1. FPIs are allowed in ETCD for the first time.
   
2. ETCD rules have been aligned with OTC currency market rules. All positions beyond USD 10 million can be taken only after demonstrating underlying rupee exposure.
   
3. For any participant, the sum of its ETCD + OTC positions cannot exceed its underlying exposure.
   
4. AD category I banks are allowed back in ETCD subject to Net Open Position Limits (NOPL). They can net-off their ETCD and OTC positions.

What these rule-changes imply

Foreign investors and NRIs face three choices:

1. To use the overseas market, where there are no documentation requirements or hedging requirements.
   
2. To use the onshore OTC market, where there is a documentation requirement and a hedging requirement.
   
3. Now, for the first time, to use the exchange, and face the same documentation and hedging requirements.

Further, unregistered foreign investors are able to effortlessly use the overseas market, while in India there is the additional hurdle of becoming an FPI without which the onshore exchange market is barred off.

Hence, we may expect that the RBI action will be irrelevant. The biggest asset that India has, in competing for the global market for the rupee, is the efficiency of the exchange. The exchange remains barred off for foreign investors. For domestic participants, underlying rupee exposure has to be demonstrated through a traditional RBI way of thinking about currency exposure. This way of thinking about the currency exposure of firms is analytically wrong.

RBI has also taken this opportunity to hurt non-bank financial intermediation. Any position beyond USD 10 million can only be taken through AD category I banks as members. This will cause the agency business to move from non-bank members to banks. Clients who look for a comprehensive solution, ETCD and OTC and small and large positions, will prefer AD category I banks.

This is not progress

1. In the interim budget for 2014-15 presented in February, 2014, para 66 says:
   

   To deepen and strengthen the currency derivatives market to enable Indian companies to fully hedge against foreign currency risks.

   The RBI actions appear to be checkbox compliance which frustrates the true objective.
   
   
2. In the Budget speech for 2013-14 presented in February, 2013, para 95 says:
   

   FIIs will be allowed to participate in the exchange traded currency derivative segment to the extent of their Indian rupee exposure in India.

   The RBI actions are late: they come after the year 2013-14 has ended. And, they are checkbox compliance which frustrates the true objective.
   
   
3. The issuance of these regulations is in violation of the Handbook regulation making progress. If the due process in the Handbook had been followed, the quality of regulations would go up. The formal process of identifying the market failure, stating a clear objective, doing the cost benefit analysis and consultation would have caught the mistakes.
   
   
4. In 2013, in his inaugural statement on taking charge as RBI governor, Raghuram Rajan had said:
   

   But for our financial markets to play their necessary roles of providing risk absorbing long term finance, and of generating information about investment opportunities, they have to have depth. We cannot create depth by banning position taking, or mandating trading only on well-defined legitimate needs.

   The recent action runs in the opposite direction.
   
   
5. In July, 2008, the RBI-SEBI Standing Technical Committee on Exchange Traded Currency Futures had recommended that over time, once the exchange traded currency derivatives segment stabilizes, OTC markets rules may be changed to align with them. What's being done now is completely the opposite: the bad practices of the OTC market are being brought into the exchange traded market.

Once the draft Indian Financial Code is enacted, such regulations would be struck down when faced with judicial review. The fear of judicial review would strengthen the staff work and process manuals for the regulation making process.

Conclusion

Rupee-cash settled currency futures are the simplest imaginable financial product. After protracted delays, trading began in a small way in 2008. It was expected that we would make progress. Instead, we have steadily moved in the opposite direction. The damage to the exchange traded market from 2013 onwards is not a bunch of small accidents. Deeper institutional change is required in order to break the barriers to progress.

The attack on the market for the rupee is a mistake

I have a column in the Economic Times today titled The attack on the market for the rupee is a mistake.

You may like to also see:

• Ila Patnaik in the Indian Express yesterday on macroeconomic fundamentals.
• Do not mourn rupee fluctuations, 11 June.
• An editorial in the Indian Express today.
• Shankar Acharya in the Business Standard.

Fluctuations of the rupee

I have a column in the Economic Times today titled Do not mourn rupee fluctuations.

The methodology for identifying dates of structural change in the exchange rate regime is from Zeileis, Shah, Patnaik, 2010.

You may find The rupee: Frequently asked questions, 1 December 2011, to be of interest.

The changes in taxation of transactions in futures on equity and commodity underlyings

Taxation of transactions in India began with the equity market in 2004. Prior to 2008, the securities transaction tax (STT) was allowed as a rebate against tax liability against Section 88E of the Income Tax Act. This treatment was withdrawn by the 2008 Budget announcement. After that, STT became a substantial influence on the equity market. In understanding the consequences of the STT, there is an absolute perspective and there is a relative perspective.

In absolute terms, suppose you embark on a spot-futures arbitrage and do an early unwind. In this, you buy shares (pay 10), sell futures (1.7) and then reverse yourself (10). Your tax burden is 21.7 basis points. This is a lot of money when compared with the typical bid-offer spread of the Nifty futures which is around 0.5 basis points. The dominant cost faced in doing spot-futures arbitrage is taxation.

In relative terms, there are two issues. The first is an intra-India comparison between equities and commodities. When activity on the equity market was taxed, eyeballs and capital moved to commodities trading. Commodity futures trading has grown by 3.5 times after 2008, while equities activity has stagnated. Most policy makers think this was an undesirable effect, particularly given the fact that India can free ride on global price discovery for non-agricultural commodities but must foster liquid markets in its own equities.

And then, there is an international dimension. When the activities of non-residents in India are taxed in any fashion, they favour taking their custom to places like Singapore, which practice `residence-based taxation' where the tax base comprises the activities of residents only. We got a sharp shift in equities activity towards locations outside India.

Putting these absolute and relative perspectives together, from 2008 onwards, equity market liquidity has fared badly. This yields an elevated cost of equity capital.

The budget speech has done two things. First, it has dropped the STT rate on futures on equity underlyings from 1.7 basis points to 1 basis points. This is helpful for certain kinds of trading strategies but not for others (e.g. the spot-futures arbitrage described above will gain little). HF strategies that do not involve the spot market will particularly benefit - e.g. imagine an options market maker who does delta neutral hedging on the futures market. Second, it has introduced taxation for non-agricultural commodity futures on an identical basis to the equity futures (i.e. at 1 basis points).

This will have the following interesting implications:

1. Capital and labour in securities firms will be less inclined to be in non-agricultural commodity futures. It will tend to move towards agricultural commodity futures, currency futures and equity futures.
2. The comparison between offshore venues and the onshore market will move in favour of the onshore market for certain kinds of trading strategies.
3. The bias in favour of equity options will reduce; some business will move to equity futures.
4. The pricing efficiency of futures will go up.

In this environment, there seems to be a fair arrangement between the equity futures and commodity futures. Conditions seem to be unfair with the equity spot (too high), equity options (too low) and currency derivatives (too low). The next moves on this may appear in July 2014 when the new government unveils its next budget.

One more announcement of the budget speech concerns currency futures: it was stated that FII activity on currency futures will commence. This will also give more activity on currency futures; we now have two reasons for expecting more activity on currency futures (the taxation of commodity futures and the entry of FII order flow). However, the shifting of FII order flow will be a slow process, and a lot of time will be lost on their due diligence of the exchange, safety of the clearinghouse, and so on. While, in the long run, removing capital controls against FII order flow in India is a good thing, it is not an effect that will kick in quickly. Apart from this, most of the action will take place fairly quickly, in early April.

Future finance ministers will need to navigate the difficult landscape of gradually scaling down taxation of transactions while retaining low taxation of capital gains (which has unfortunately come to be seen as a linked issue in the Indian discourse). Along this path, the first priority should be to remove distortions. Our first priority should be to achieve a low rate, a wide base, and the minimal distortions. Reduced rates will always yield welfare gains. The Budget 2013 announcement makes progress on two things (reduction from 1.7 to 1, and reduced distortions between equities and non-agricultural commodities). There is much more waiting to be done: integrating currencies and fixed income, bringing sense to options, and getting away from the very high rates on the equity spot market.

The rise of high-end finance work in India

by Shashank Bansal.

Until recently, outsourcing by global financial firms to India conjured up an image of commoditised low end services outsourcing: call centres, peripheral systems programming, and testing and maintenance. However, in recent years, there is a new rise of more sophisticated work. This reflects supply and demand factors. Global financial firms are keen to cut costs. Capabilities of operations in India -- both captives and independant firms -- have grown for many reasons:

• The individuals involved in this field in India have gained experience ("learning-by-doing") and credibility.
• New management practices and improved telecommunications technologies have improved the extent to which teams and projects are handled in a more non-local way.
• The Indian diaspora has been rising to senior management levels in global firms, and is better able to envision what can be done in India and to obtain execution.

A European investment bank was among the first to experiment by bringing in teams in India into critical projects. This was a landmark change as a lot of inertia about confidentiality was overcome. Other banks followed suit. New management practices, higher pay, greater meritocracy came in, which helped Indian teams make the transition from low-end work where the HR and management techniques used are quite different. Demand for high skill labour has helped induce greater supply, with a lag, as individuals were more inclined to tool up with advanced degrees and high-end knowledge.

Alongside the developments in finance, parallel developments were taking place in the field of offshoring which have driven up skill levels, and helped create a high skill ecosystem in India. Top tier consulting firms launched `centres of excellence' in India, hiring grads from IITs, IIMs, IISc, statisticians, economists. While education in India has huge problems, the raw talent available in India was of good quality, particularly when we focus on individuals who were able to read on their own and reinvent themselves ("never let your school come in the way of your education"). This process has been helped by globally recognised certification exams such as the FRM and the PRM.

IT firms have have been evolving from core development and maintenance to an entire gamut of IT strategy and consulting for financial firms. Many smaller KPO firms with specialised domain knowledge in finance have emerged, who cater to smaller hedge funds, trading houses, not just outsourcing increasingly complex pieces of work, but also advising them on the entire outsourcing strategy. All this has helped create a pool of high skill labour which is moving between multiple employers in India and able to build knowledge through diverse kinds of experience.

The most impressive development of recent years has been the growth of offshore trading units of global brokerages and trading houses, where people sitting in India take independent trading decisions in international financial markets based on their own skills and judgement. In some ways, this is the highest level of transfer of decision functions to India, albeit at relatively low monetary stakes.

In this fashion, within a period of 15 years, India had graduated from doing repetitive low value tasks to Knowledge Process Outsourcing (KPO) for the global financial system. While these activities are primarily in Bombay, they are also taking place in Gurgaon and Bangalore. The number of high-end finance workers in Bombay has never been greater than it is today. It is estimated that there are now 50 individuals working in Bombay doing work for global financial firms who have Ph.D. degrees in quantitative fields. This is starting to become a big enough number for them to talk with each other and get network effects going. From an employer's point of view, it is now possible to shop in the labour market in Bombay and recruit a 10-man team all with Ph.D. degrees so as to get a new group going. This is a sea change when compared with conditions just a few years ago.

To appreciate this change a little further, it was interesting to take a look at some of the capabilities of finance focussed KPOs, divided mainly into 4 broad categories, catering to Sales and Trading, Middle office and Back office:

1. Quantitative Research and Analytics Support:
   1. Equity and FICC Analytics: Model Validation, Price Verification jointly with clients: these are pretty quant heavy functions which require in-depth understanding of products.
   2. Technical and Fundamental Analytics.
   3. Index and Portfolio Analytics: Index maintenance, design, construction, operations and after sales, Portfolio tracking, decomposition and correlation analysis, performance measurement and attribution support.
   4. Derivatives and Risk Analytics: Measurement of derivatives Greeks, Value at Risk, Tolerance checks.
2. Research:
   1. Equity and FICC Research: Company research, Credit Research, Economics research etc. to augment senior analysts in money centres.
   2. Trade idea generation and back testing: Sales pitches for clients and internal trading desks.
   3. Country, Sector, Company profiling, trends, news and projections: Pitch book generation and support.
   4. 24x7 weather patterns tracking for global energy trading outfits
   5. Overnight trade and market tracking to feed in summary reports, Market Dashboards, news letters, morning meetings and agendas
   6. Market Research: Pre-entry market research and positioning survey for bank's clients.
3. Data Analysis and Modelling:
   1. Data sourcing from multiple heterogeneous sources, refining and maintenance: Static data, Live and Historical market data maintenance. Data research and statistical studies feeding into trading strategies.
   2. Data Mining solutions.
   3. Data modelling, smoothing: Providing data solutions for Algo trading desks.
4. Operations and Control
   1. Derivatives trade processing and documentation: Trade review of structured trades and complex documentation. End to end life cycle management of trades e.g., matching, broker confirmations and fee calculations.
   2. P&L and balance sheet control: Generation and reporting of P&L for vanilla products. Some banks have started moving exotics P&L functions to India. This is quite a significant milestone as such activities require high degree of confidentiality and direct user (e.g., traders) interaction who have zero tolerance for mistakes.
   3. Risk Stress testing, VaR back testing, Risk reporting to senior management.
   4. Auditing: external auditing of valuation marks of trading desks and control processes around it.
   It should be noted here that since the funding crisis of 2008, these jobs have become quite complex as most banks have built more sophistication into their analytics. For example, most yield curves would now have multiple basis spreads (like tenor basis, xccy basis) and not just rates desks but even credit and equities desk have been using such advanced discounting curves.)

What's next

The biggest push probably has been in quantitative middle-office functions with an ever increasing emphasis on valuations and counterparty risk management. Given the way markets have adopted collateral based pricing of derivatives, and the regulatory push on managing counterparty default risk, some captives have started building quantitative teams who will develop and manage CVA, DVA, etc. processes for all trading desks.

The new regulatory climate (Dodd Frank, Basel III etc) has lead to a substantial increase in costs due to additional checks and reporting requirements e.g., centrally cleared OTC trades, real time trade reporting to regulators, exhaustive risk reporting - all of which can are leading to fresh volumes of activity in offshoring.

All high quality banks have a team of techno-quants who work closely with the sales/trading desk, risk managers etc, on their day to day needs as well as on strategic projects. It is now feasible to move such high impact roles to India. It would be possible to have "extended front office teams" where dedicated staff support traders in money centres, doing real time risk analysis and client profiling, while the trade is being dealt overseas.

For a back-of-envelope calculation, if we think of internal billing rates of $100,000 per person per year, and if there are 10,000 persons at this average price, then this is services export of $1 billion a year, which is a sizeable amount. It appears that the early beach-head is in place, and this area will grow dramatically now.

This blog post reflects my experience, which is in investment banking and money management. A similar escalation of complexity of work in India is taking place in retail banking, insurance, etc., reflecting similar compulsions and opportunities.

Constraints

There is a certain tension between the push towards offshoring to India, and the activities that regulators consider `key in-house activities' that cannot be outsourced.

There are serious constraints with education in India. The top institutions are producing some quantitative skills (e.g. fluency with matrix algebra, fluency in numerical computation). On one hand, there are weaknesses of broad intellectualisation that shapes cognition, creativity and malleability. On the other hand, there is essentially nothing in place by way of a finance education in India. A small amount of high-end finance research is taking place (example) but for the rest, there isn't much capacity in the existing academic campuses. New approaches to learning and training need to be devised through which high quality individuals, with strong quantitative skills, can be converted into full fledged participation in high-end global finance work. A mix of public and private initiatives are required in order to jump to the next level.

There are strong synergies between the sophistication of the Indian financial system and the work that is done for global financial firms. There is a two-way feedback loop here: Better domestic capabilities will help do sophisticated offshore work, and the brainpower built for offshore work will strengthen domestic capabilities. The best example of this is found in the equity derivatives market, where India has a world-class market. The individuals with a domestic background here are ready for offshore jobs in fields like algorithmic trading, and individuals with capabilities built in offshore work are useful in the domestic setting. This is where India can set itself apart from Malaysia and the Philippines. To the extent that Indian financial reform makes progress, this will fuel the rise of high-end outsourcing to India.

Acknowledgements

I am grateful to Anand Pai, Paul Alapat and Gangadhar Darbha for useful discussions.

Next big development in the global market for the rupee

The next interesting development after ICE trading of rupee futures: CME will launch rupee futures soon also. See CME follows ICE into rupee futures by Tom Osborn on Financial News.

ICE and CME are the world's top exchanges and they are serious rivals for the global rupee market. These recent developments add up to a substantial change in the outlook for the rupee as an internationally traded currency. The rupee will become more prominent as a globally traded and liquid market. And, ICE and CME are likely to do well, thus accelerating the decline of the onshore market.

Rupee and Real futures at ICE

Intercontinental Exchange has announced cash-settled futures on the Indian Rupee and the Brazilian Real [press release] [Saabira Chaudhuri in the Wall Street Journal]. With this, ICE is the first serious global exchange to start trading in the rupee.

Vimal Balasubramaniam and I have pointed out that the global market for the Indian rupee is adding up to some fairly big numbers. I recently noticed that in 2010, even though China is a much bigger economy than India, rupee trading was 0.9 per cent of global currency trading while RMB trading was at 0.7 per cent. Similarly, it appears that the INR NDF is bigger than the RMB NDF, even though China is a much bigger economy. Something is going right in the growth of the rupee as a big currency by world standards. Rupee trading at ICE would strengthen that process.

The ICE announcement also connects to the issues of global competition for Indian underlyings. The two biggest financial markets in India are Nifty and the rupee. So far, NSE faced serious competition with Nifty futures trading at SGX and CME, but there was no significant rival with the rupee. With the arrival of ICE, the competitive dynamics for the rupee changes, which is a welcome development. NSE now faces genuinely difficult competition from three first-tier rivals: CME, ICE, SGX. At the same time, the outlook for rupee trading in India is hobbled by an array of constraints:

• ICE can pitch for business from non-residents, while NSE cannot, since foreign participation in currency futures is banned. We seem to think that OTC trading of currency forwards requires encouragement from industrial policy operated by RBI.
• ICE is able to start contracts any time it likes on (say) the Brazilian Real while NSE is forbidden from starting any new contracts.
• India has mistakes on tax treatment, lacking residence based taxation, while the world has all this well sorted out.
• India has an array of other policy and regulatory mistakes that hobble local players. The ICE transaction charge is zero. I wonder if litigation will now start at CCI to try to block this.

A process is afoot, at present, through which the Indian financial system is being hollowed out. If this process runs unchecked, RBI and SEBI will be left lording over nothing. There is a need to reverse this  policy framework of reverse protectionism.

Preventing shocks or becoming resilient to them?

My previous blog post, on not cancelling trades after a fat finger trade, elicited some interesting email conversations. In a nutshell, there are two views of the world. One camp argues that it is important to prevent fat finger trades and other such weird episodes. This requires building an array of preventive measures. The other side argues that the costs of prevention are high, and what's really important is to make a resilient market that is able to absorb shocks.

Prevention is difficult for two reasons:

1. NSE and BSE are some of the biggest exchanges of the world. We should be pleased that India has two of the great factories of the world doing order matching. But as a side effect, NSE and BSE are at the limits of what today's CPUs can do. Many, many orders are placed, compared with the number of trades. Pre-trade checks are expensive because the number of orders is high. Fairly trivial notions of pre-trade checks can triple the hardware requirements or worse. We have to ask ourselves: Is it worth driving up the cost of transacting by 3x or 5x or 10x in order to do those checks? In addition, pre-trade checks introduce delays ("latency") which are not good for the trading process. When an order is placed, the person wants an instant confirmation that it was placed into the order book and ideally matched. More work in screening orders before the trade increases the latency suffered by traders. This, in turn, increases the risk faced by various trading strategies, which has adverse implications for market liquidity and market efficiency.
2. What validation rules would you write, pre-trade? There is a danger of fighting the last war. New kinds of problems will inevitably surface in the future. Will we keep on increasing the burden of pre-trade computation, over the years, as the list of potential difficulties goes up through time?

There is a shades-of-gray dimension here. It appears obvious to us that if a computer program is buggy, and puts in a wrong order, this should be blocked. But what when a man-machine hybrid (the typical human trader that operates a computer) makes a mistake? What about a pure human trader that makes a mistake (e.g. saying on the phone "buy me 25 million shares of Infosys" when he meant "buy me 25 million rupees of Infosys")? Where do you draw the line?

It is better, instead, to see that mistakes are an inevitable part of financial markets. I would argue that pre-trade computation should be kept to the bare minimum, and that it is instead important to focus on deeper initiatives that will make the market more resilient. We need more eyeballs, more capital, more limit orders, more arbitrageurs, more algorithmic trading, more short selling. This is what will make the market resilient. A resilient market is one that is ready to accept a diverse array of unpredictable shocks in the future. Until a few weeks ago, we never imagined an order for 17 lakh nifties could be placed. The market did well in absorbing this completely unanticipated shock. The market should be a flexible, intelligent, resilient construct that is ready for all sorts of unexpected events of the future.

Some people say: "We should put in infinite expenses in order to screen orders". This reflects a lack of  economic thinking. The strategies of prevention and cure need to be evaluated from a cost/benefit perspective. Each features tradeoffs. Driving up the charges of an exchange by 3x to 10x, and increasing the latency suffered by every market participant, is a big cost. This should be weighed against the benefits.

I am reminded of a great story told by the Chilean economist Raimundo Soto at a NIPFP/DEA Conference in 2009. He started by describing a cautious 80-year old person, who is very careful about what he eats, who avoids stepping out of the house, and so on. He stays alive, but is perennially afraid that a small sickness will bring him down. And, indeed, when one small common cold comes along, it can have catastrophic consequences for him. Compare this with a 15-year old prancing around the world, tumbling in the dirt, taking risks, and living a great life. He is exposed to many illnesses, but rapidly bounces back from each of them.

Raimundo Soto said that the analysis of capital account convertibility should be rooted in the desire to become this 15 year old rather than this 80 year old. We should be asking: How can the system be made more resilient to shocks? We should not aspire for a Chinese Wall of capital controls that cuts India off from the global financial system; instead we should be doing the things that make India resilient to international shocks - such as develop a sophisticated Bond-Currency-Derivatives Nexus.

In similar fashion, too much of the conversation in India, after the Emkay fat finger trade, is about asking How can such shocks be prevented? I think we should aspire to be like the 15 year old and not like the 80 year old. The really important question is: How can the system be made more resilient to such shocks?

Cancelling trades on an exchange: When is it a good idea?

When inexplicable things happen on an exchange, many people argue that those trades should be cancelled. I think it is useful to be clear about the test to apply for this.

The key question should be: Did something foul up in the order matching software? If order matching went wrong, or if there was a systematic breakdown of connectivity to the exchange, then there is a case for cancelling trades. We'd say that persons placed certain orders, but the exchange mis-handled the orders, hence the observed series of matched trades and prices is unfair.

If the exchange and its rules worked as advertised, this reason peels away. In fact, I would argue that particularly when there is a fat finger trade or something like the US `flash crash', it is important to not cancel trades, to cement faith in the trading process.

The recent events surrounding the fat finger trade by Emkay are a good example of this line of thought. Owing to a human error,  a basket trade to sell Rs.17 lakh of Nifty was instead placed as an order  to sell 17 lakh nifties (where one `nifty' is a basket of 50 shares adding up to the present level of the Nifty index expressed in rupees). If Nifty is at 5000, then an order for "100 nifties" is an order for Rs.500,000.

Through this human error, a very large sell order appeared on the market. At that instant, everyone looking at the market would have been taken aback. What was going on? Has a huge event unfolded which some informed speculator knows about, but I do not know about? It takes nerve in that moment to be on the other side of the order. We must reward the people who did not lose their head when everyone around them was losing theirs.

When the big Emkay order came in, many of the orders which were matched were limit orders which had been patiently waiting there. This does not, in any way, change the analysis. Waiting with `deep out of the money' limit orders is a hazardous business. As an example, consider the persons waiting with deep out of the money limit orders, standing ready to buy at very cheap prices (e.g. 10% below the current market price) when the Satyam scandal unfolded. They lost money big time because the informed speculators, who understood the Satyam announcement and placed massive market sell orders, knew more than them. Waiting patiently with limit buy orders, 10% away from the touch, is not free money. ("The touch" is finance parlance for the bid and the offer price). It is a risky trading strategy.

Two trading strategies matter most in stabilising a market when crazy things have happened. Traders  have to be there ahead of time, with limit buy orders far away from the touch. The limit order book should be thick with orders; i.e. the impact cost associated with a giant market order should be low. And there have to be traders who see that the market has crashed, are able to work the phone and gain confidence that this is an idiosyncratic shock, and come into the market and buy. The more the capital and intelligence behind such trading strategies, the more stable the market will be.

If trades are now cancelled, these two trading strategies will have suffered the risk and got nothing in return. In the future, they will be more circumspect about stabilising the market. Similar considerations apply on the other side. When there are strange and large upward moves of the market, we want rational speculators who short sell and bring the price back to fundamentals. The market must be designed in a way that supports and enables this. At present, it is not [link, link].

Fat finger trades will happen. There will occasionally be strange rumours and other odd things that will make markets fluctuate away from fair price. In those situations, what we want most is for clear-headed rational speculators to put large scale capital into making money by stabilising the market. The rules of the market should reward the people who perform these roles. Trades from their orders should not be cancelled.

The Emkay story has gone well for the Indian securities markets. The market design worked as it should have. A human error was made, there was a brief market-wide suspension on the equity spot market (but the futures market continued to work). A call auction took place to discover the price, and within minutes everything came back to normal. Emkay took full responsibility for their trades and came through with the money. We shouldn't stumble in the policy analysis that follows this story.