Tag: volatility

Day vs. Overnight Volatility

Previously, we discussed how overnight volatility is not necessarily the scary boogeyman that it is made out to be. However, what maybe true for the NIFTY may not be true for other indices. For example, commodity stocks could carry larger overnight risks than, say, FMCG stocks.

If you look at the median volatilities of the two indices, commodity stocks have larger close-close volatility than FMCG stocks. However, FMCG stocks have lower volatility in general, so not sure if the differences are meaningful.

What about QQQs (US tech) and XLE (US Energy)?

Most energy related reports are released during US market hours while earnings reports are not. That could explain why XLE relative overnight volatility is lower than QQQ’s? Also, weekend risks averaging less than daily and overnight risks is surprising as well.

Code and charts on github.

Skew

Our previous post discussed how the implied volatility (IV) of OTM puts are often higher than the IV of OTM calls. We would like to add that this “smirk” is very much warranted – it is not an invitation to sell OTM puts. Returns of financial instruments often have negative skew – a fancy way to say that they often take an escalator up, and an elevator down.

Here are the daily and weekly return skews of the NIFTY 50 TR index and the SPY ETF:

The market is willing to pay up to hedge against this risk. If you sell the skew, you’ll have to hedge against it by some other means. Otherwise, it is like picking up pennies in front of a bulldozer.

The Smirk

When you use the Black-Scholes-Merton (BSM) model, you end up with theoretical prices that assumes that volatility affects all strikes uniformly. i.e., strikes have no bearing on implied volatility (IV). This was largely true in the market as well until the crash of 1987. However, after the October 1987 crash, the implied volatility computed from option prices using the BSM model started differing between puts and calls. This is called “volatility smile“, or the smirk, given its actual shape.

The reason for this is quite simple, markets take the stairs up and the elevator down. Fat tails, if you must. So, put options sellers require a little bit of an incentive to take on that risk.

How crooked is the smirk? If you take the ratio of the IVs of OTM puts to OTM calls and plot them, you’ll notice that as you get farther away from spot, the distribution flattens out.

Notice the area below 1.0? Those are the days when the calls were trading at a higher IV than the puts.

On the left of zero are the calls with descending order of strikes and on the right are puts with ascending order of strikes. The farther away from zero, the more OTM they are.

Also, unlike the stylized charts of IV you might have seen with sweet smiles, the reality is quite different.

If this tickles your curiosity, do read The Risk-Reversal Premium, Hull and Sinclair (SSRN)

Code and charts on github.

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.

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.