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.