On November 9th, 2016, Indian financial markets were asked to digest two major events. At 8:30 PM on 8th evening, the announcement had come out about de-monetisation of the 500/1000 rupee notes. And, by late night on the 8th, there was news of Donald Trump's early gains in the U.S. presidential elections. At 9:28 a.m. IST on the 9th, Donald Trump won Florida. At this point in time, for Hillary Clinton to win, she had to win the states of Wisconsin and Michigan, where Trump was already leading. At 1:00 p.m. IST, Clinton conceded.
The two events contain an interesting contrast. With the US presidential election, the betting markets were reporting a 20% chance of a Trump win. But de-monetisation was on nobody's radar. It was not part of our distribution.
In this article, we go back to those events and look at how the Indian financial markets responded to the major events.
|Figure 1: Prices|
All the graphs in this article show physical time from the morning of 7 November to the end of 11 November. The top panel shows the equity market, with Nifty spot in blue and Nifty futures in grey. On 8th evening, Nifty rose slightly, which suggests that there was no insider trading based on the de-monetisation, and nobody had a sense that Trump would win. On 9th morning, Nifty opened sharply down, reflecting both elements of news. By the time Nifty trading stopped, Nifty had remarkably come back to 8,432. With the benefit of hindsight, we know that the market was too quick in reconciling itself to the news. By the end of the week, Nifty had fallen further. On 2 December, Nifty closed at 8086, showing that the de-monetisation news had not been fully understood even by 11 November.
We may speculate that in the Indian equity market, there is a lot of focus on information production about individual stocks, but low capabilities in macroeconomics. In many previous events also, we have seen the market being relatively slow in understanding far-reaching macroeconomic developments.
The middle panel is the USD/INR. The blue line is USD/INR futures (which trade 09:00 a.m. to 05:00 p.m.) and the grey line is the spot market, which can trade for 24 hours a day. Let's think about the evening of 8th. At 8:30 PM, there was the de-monetisation announcement. Remarkably, the spot market showed an appreciation of 40 paisa. This shows that the currency traders of 8th evening did not understand the de-monetisation. In the late night, news of Trump's success started trickling in. In the morning of 9th, INR depreciated reflecting both elements of news. In the Indian afternoon, the USD moved sharply when Clinton conceded, which gave an appreciation.
For the rest of the week, INR depreciated as the bad news sank in. One element that was at work was the huge demand in India to convert 500/1000 rupee notes into US dollars. Anecdotal reports suggest that by 10th, the entire inventory of US dollars in the Indian black market had been exhausted. This would have triggered off demand for dollars, and fueled the depreciation. To the credit of RBI, they let the market do its job; they did not interfere with a large INR depreciation.
The bottom panel is gold. The blue line is MCX gold futures and the grey line is the CME gold futures. MCX gold trading enjoys long hours: from 10:00 a.m. to 11:30 p.m. In the late hours of 8th November, gold prices became very volatile when the de-monetisation announcement came in. Trump's lead in the U.S elections inflated gold prices when the market opened on 9th.
There were two distinct things going on. Worldwide, buying gold is a vote of no-confidence in civilisation and paper money. The Trump win would have encouraged many people worldwide to shift their holdings in favour of more gold. In India, the de-monetisation announcement had an abstract implication (mistrust of the Indian rupee, mistrust of the Indian State) which would have encouraged a higher weightage of gold in the portfolio, but there was also an immediate and practical dimension. Thousands of people flocked to Sarafa bazaars to exchange their high denomination notes for gold in the morning of 9th November.
These extremes were somewhat unwound in the rest of the week. Perhaps there was a premium on physical gold available in India on the 9th. Within a few hours, gold bars could be flown in from Dubai and Singapore, through which the Indian spot price would come back to the world price. But on the day of the 9th, there was extreme demand for gold and the Indian price deviated from the world price.
|Figure 2: Turnover|
Turnover on equity derivatives -- Nifty futures and Nifty ATM options -- is a critical element of Indian price discovery. On the 9th, markets opened with very high trading intensity in both Nifty futures and options. The turnover in near month at-the-money options surged again when Trump's victory was confirmed. There is an interesting pattern thereafter: On 10th and 11th, the futures activity was larger. This runs against the normal pattern in India, where options trading is favoured owing to the high securities transaction tax (STT) on futures trading. This is worth exploring further. Perhaps this odd behaviour is being induced by errors in the rules for initial margin calculation.
In the currency market, futures trading dominates as there is no STT-related distortion. Here, we see we got a big surge of trading on 9th morning, a smaller surge in the afternoon when Clinton conceded, and then a fading away of excess turnover through the rest of the week.
MCX was open for business when the de-monetisation announcement came out, and reaped a bonanza with a massive increase in turnover on 8th evening. For most financial traders in India, on 8th evening, MCX was the only game in town as NSE and BSE were closed. From 9th onwards, the patterns in turnover are similar to those seen with the other two markets: a surge on 9th morning, a surge when Clinton conceded, and a gradual phaseout of extraordinary turnover through the week.
|Figure 3: Violations of no-arbitrage on the futures market|
On 9th morning, when NSE opened for trading, there was a huge mispricing between the Nifty spot and the Nifty futures. Once that was corrected, for the rest of the week, pricing errors were comparable to those seen before the news, but the basis risk was higher.
The pricing errors on USD/INR futures are surprisingly small when compared with those seen on the equity market. This suggests there is ample capital in currency futures arbitrage, relative to the small size of the market.
With gold, what we are reporting is the pricing error between the MCX gold futures and the CME gold futures. As emphasised earlier, there was a large dislocation on the Indian gold spot market on 9th, as many people were buying gold. The Indian gold spot price fell out of sync with the world gold price. This is showing up as large pricing errors in the bottom panel, and normalcy is attained as enough planes land in India bearing physical gold.
|Figure 4: Violations of put-call parity on the options market|
Put-call parity held up pretty well through these events, for both Nifty and USD/INR. A brief large error was found on the morning of the 9th, on the Nifty options market. Apart from that, the deviations on both markets are small. The readings of deviation from put-call parity on the USD/INR options market are relatively sketchy as this market is often illiquid.
|Figure 5: Realised volatility|
By 8th evening, realised vol on the Nifty futures market was showing some large values. Extreme realised volatility was found on the morning of the 9th: rvol was 6 times larger the pre-event mean value, as the market digested the two events. The price discovery was largely completed by 9th evening, and then realised vol was only slightly higher than the values seen in peacetime.
On the USD/INR futures, realised vol on the morning of the 9th was roughly 11 times larger than the normal values. The morning of 10th also shows a significant spike in realised vol. While large price fluctuations were not evident on 10th, we observe a significant rise in variations on 11th.
Ruminating on methodologyOrdinarily, economists obsess on the question of identification. How do you know that event $x$ caused the outcome $y$? Could it be that there were other things going on which were impacting upon the observed change? We normally struggle to find plausible control units which can be juxtaposed against treatment units where both kinds of units are alike. The game is about rising beyond simple comparisons of means (or regressions), and look for plausible quasi-experimental designs. There are two tricks through which we are allowed to read the world and learn about how it works, without requiring the discipline of a matched sample of treatments and controls.
The first trick is when there are very big events. Ordinarily, we'd be worrying about whether the observed change in the treatment unit was caused by the event; what about other things that might be going on? But when big events happen, they dominate everything else. In the week under examination, we don't need to think about macroeconomic or firm news. The market was absorbed in doing price discovery in figuring out these two events; they drowned out all the other news flow.
The second trick is high frequency data. When we zoom into high frequency data, we have an opportunity to see the impact of the event alone, as it is unlikely that other confounding events have unfolded in that very short time.
- Prices: These are traded prices of a security reported at minute-level frequency.
- Traded volumes: These are number of units of underlying security traded at five-minute frequency.
- No arbitrage violations: These are measured in terms of difference between the price of underlying security and price of near-month futures contract based on it. We do this for Nifty and for USD/INR but for gold, we just focus on the gap between MCX Gold futures and CME Gold futures.
- Violations in put-call parity: According to put-call parity, S + P = C + X(1+r)-T i.e. a spot investment that is risk-managed using an at-the-money put option is tantamount to a combination of a bond and an at-the-money call option. Thus, violations in put-call parity are measured as the difference between the two investments.
- Realised volatility: It is constructed by computing intra-day returns at 5-second frequency. Thus, for every 5 minute-interval, realised volatility is computed as standard deviation across 60 readings of returns in the interval. We have very high frequency data and there are ample transactions within 5s, thus permitting differencing at such a high frequency.
Anurag Dutt, Sargam Jain and Susan Thomas are researchers at the IGIDR Finance Research Group. Ajay Shah is a researcher at the National Institute for Public Finance and Policy.