Tag: volatility

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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Intraday Volatility

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.

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Volatility Lookbacks

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?

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Volatility, Volatility of Volatility, and Momentum

Momentum has proved to be the premier anomaly in different markets. And so has low-volatility. What happens if you combine both of them? Also, what if you also add low volatility of volatility into the mix?

There are a couple of ways to skin this cat. You can start with low-volatility and add momentum. Or, you could go the other way – start with momentum and then add a volatility sort.

tl;dr: go with low-volatility first, momentum second (VOLxMOM).

While a simple momentum sort gives the highest return, adding a low-volatility filter to it gets you a better risk-adjusted return.

The order of the sort – first volatility and then momentum or first momentum and then volatility – doesn’t seem to matter much for the Sharpe rankings but the former ended up with slightly better returns.

Code and charts on github.

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Large Moves Happen Together

We are often told that missing the 10-biggest days in the market leads to sub-par returns. While it is certainly true, what is often not said is that those really big days occur around really bad days. Welcome to tail-risk.

The average daily return of the NIFTY 50 is 0.06%. The worst daily return is ~ -13% and the best is ~18%. Welcome to tail-risk.

In any given year, there are a lot of days when returns fall out of 1, 2 or even 3 standard deviations (σs), Up and Down.

And these σ moves tend to happen close to each other. i.e., volatility clusters.

What the histogram above is showing is that most of the 3σ moves have happened within 5 days of each other! Let’s zoom in on a 10-year period of the index and mark the outliers on it:

Now, lets pick a very simple actively managed strategy that tries to side-step the –σ moves. The details of the strategy itself is unimportant. Suffice to say that it creates excess returns compared to buy & hold.

The average daily return of this strategy is 0.07%. The worst daily return is ~ -7% and the best is ~18%. At least some of the left-tail has been clipped but at what cost?

Notice how both the number of large Up and Down days are lower here compared to buy & hold?

Outliers still cluster but there a lot less of them.

This is the nature of market volatility. Investors have to either commit to buy & hold and catch all the moves or commit to an actively managed strategy knowing that while trying to side-step –σ moves, some +σ moves will also be sacrificed. It is the FOMO that keeps investors switching between the two, resulting in sub-par returns.

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