Volatility Index (VIX) is a measure of the market’s expectation of volatility over the near term. India VIX is a volatility index derived from the Nifty Index option prices that imply expected market volatility over the next 30 calendar days.
We have seen a bump up in volatility surface and an elevated skew ever since the start of the COVID-19 crash. The spread between the CBOE and India VIX diverged to unforeseen levels.
Volatility Spread: CBOE VIX — INDIA VIX
This spread recorded its highest level on March 12, 2020 when the Nifty Index tumbled more than 11% in a day. The spread between VIX and India volatility index diverged more than 7.5 standard deviations as India VIX jumped 30% in a day.
But, despite the recent spike to 80%, India VIX is still cheap. What does this mean? How does one measure volatility?
Hold your horses!
It means volatility levels are relative, not absolute. The simplest measure to reflect the relative price of volatility is by comparing historical realized volatility to implied volatility.
Above chart shows,
- 20 days realized volatility is at 80.87%
- Implied volatility as measured by India VIX is at 64.40%
- The spread between India VIX and 20 days realized volatility diverged to a record -16.46 points on March 31, 2020.
- India VIX ranks at the 0.30% range, indicating that Implied Volatility is abnormally high.
So, is India VIX cheap or expensive? Implied Volatility is undoubtedly high ranking in below 1% range. However relative to historical volatility, it is insanely cheap.
Long-Term Perspective on Volatility
India VIX data is only available from 2010 onward, which provides limited history for any meaningful study.
However, we can use realized volatility as a proxy for implied volatility and extend the observation period to cover 23 years from 1997 to study the volatility behavior.
Since 1997, realized volatility was above 50% in nine years. The current reading is second only to 2008 levels.
Is It Time For Mean Reversion?
One of the most important characteristics of volatility is Clustering — which is the tendency of large changes in asset prices tend to cluster together, resulting in the persistence of the amplitudes of price changes.
Some investors tend to bet on volatility to mean-revert when trading at elevated levels. A systematic model is to short volatility when it is abnormally high compared to its recent history and exiting when mean-reversion set in. However, this has resulted in sub-optimal performance (more of this later) given the persistence of high volatility.
It seems the current high volatility regime is here to stay.
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