Author: shyam

Historical vs. Implied Volatility

India VIX is a volatility index computed by NSE based on the order book of NIFTY Options. For this, the best bid-ask quotes of near and next-month NIFTY options contracts. India VIX indicates the investor’s perception of the market’s volatility in the near term i.e. it depicts the expected market volatility over the next 30 calendar days. Higher the India VIX values, higher the expected volatility and vice versa. (NSE)

Does the actual volatility come close what the VIX was implying 30 calendar days before? Not always and probably never.

What if it’s pricing something more immediate? Here’s the regression with a 10-day lag:

Regression with no lag:

The relationship between implied and historical is one of those things that are directionally true… sometimes.

Code and charts on github.

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Trend-following Bonds

Does trend following work on bonds? According to alphaarchitect, it should. However, they use data going back to 1928 and we wanted to look at something more recent. Also, we wanted to check if it worked for Indian bonds?

For Indian bonds, you are better off buying and holding. Once you consider transaction costs and taxes, there is no benefit.

For US, we ran the same SMA scenarios on the TLT (20+), IEF (7-10), SHY (1-3) and AGG etfs. There is some benefit to applying a 100-day SMA filter on the first three. However, the after-cost benefits are questionable.

Code and charts on github.

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VIX Seasonality

Is India VIX seasonal? Yes.

There is a huge amount of dispersion in the daily data when grouped by months. Taking averages of these may not make much sense.

However, when you decompose the series, you get some interesting monthly seasonality.

Zooming into the “season_year” chart:

If you transform the seasonality component and plot it by month, you’ll notice why everybody gets nervous in May.

Code and charts on github.

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Overnight Volatility

Currently, Indian markets are open for 6.5 hours. During that time, global commodity markets are largely closed and overnight US futures markets are barely coming to life. This exposes positions carried forward to the next day to event risks. How is this risk priced?

Surprisingly, Close-Open (next-day) (CO) volatility is less than Open-Close (same-day) (OC) volatility. This doesn’t quite jive with the intuition about large overnight risks. This holds even if you include pre-pandemic data.

If you believe that overnight risks are larger than what the market perceives, then buying strangles at the close surprisingly doesn’t cost you much. A naïve strategy should breakeven after costs and occasionally, you might get lucky.

The unknown-unknown is scarier than the known-unknown. However, it is the known-unknown that you should be worried about more.

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Daily Momentum

Daily Momentum and New Investors in an Emerging Stock Market (SSRN) describes the trading behavior of Chinese retail investors.

Our study finds that daily returns, instead of monthly returns, display price momentum and attributes it to the trading behaviors of new investors using account-level transaction data.

Apparently, most new entrants to the market in China take a very short-term punt on whatever worked on the day. They go on to study a bunch of DM and EM markets and its worth a read.

The interesting bit is that Indian investors don’t chase daily momentum. In fact, for an equal-weighted “buy the best performing quintile and hold till tomorrow’s close” strategy, after transaction costs and taxes, there’s nothing left, on average.

The median average next-day return of the 5th quintile (highest return) is 0.30%, before slippage.

Also, buying the worst performers did no better either.

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