Invest Without Emotions
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.
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.
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.
Also, buying the worst performers did no better either.
Realized Semi-variance is a measure of intraday volatility. It is nothing more than the sum of squared high-frequency positive and negative returns.
It is typically used for forecasting volatility. However, can it be used for market timing? After all, volatility is said to be sticky and avoiding downside volatility is supposed to be desirable.
What if, you exit the market when the current volatility is more than the historical average (based on some lookback)?
Turns out, doing something like that would’ve worked on the pre-pandemic NIFTY 50. Maybe not higher returns but better Sharpe than buy & hold.
However, post-pandemic returns have been disappointing.
The same thing can be observed on the MIDCAP 50 index as well.
We’ll add this to the growing pile of disappointing results of using volatility for directional bets.
Volatility is calculated over a time period – the lookback. While developing a strategy, it is typical to try a range of lookbacks and pick one that looks reasonable for the strategy being built. However, is there an “ideal” lookback period?
This is where a volatility signature plot comes into the picture. It is typically used in high frequency trading but there is no reason not to use it on a lower frequency time series.
If you plot the distribution of volatility over different lookbacks, this is how it looks:
Ideally, you want the box to be small, the median in the middle and the wicks to be short. After all, if you are using volatility to drive a strategy, if the distribution of volatility itself is too wonky, then how do you trust the output?