Tag: volatility

Volatility as Beta

The volatility risk premium (VRP) is the difference between implied volatility and realized (or actual historical) volatility. Implied volatility is, on average, overpriced compared to realized volatility.

The VRP exists because investors are essentially “selling insurance” when they sell implied volatility.

Volatility is negatively correlated with equity returns; typically, volatility increases when equity markets decline. Therefore, a short volatility position is implicitly “long equity risk”. Since equities are generally expected to earn an equity risk premium (ERP) over the risk-free rate, strategies that are implicitly long equity risk should also be abnormally profitable. This is why short volatility strategies tend to be profitable on average.

Just like how you can get long ERP by getting long an equity index, you should be able to get long VRP by programmatically shorting options and delta-hedging them. Volatility becomes a beta that you allocate towards.

Building Blocks

An option’s value changes relative to the price of the underlying – the rate of this change is called delta.

Gamma is the rate of change of delta given a change in the price of the underlying. As the underlying price moves, an option’s delta does not remain constant; gamma quantifies how much that delta will change.

Since we are only interested in volatility and not price, we can hedge out this delta. Delta-hedging a basket of options mitigates the exposure to the directional movement of the underlying. Profitability becomes solely determined by the volatility (not direction) of the underlying.

Vega is the rate of change of an option’s value relative to a change in implied volatility (IV). If IV rises or declines by one percentage point, the value of the option is expected to rise or decline by the amount of the option’s vega, respectively.

When you short options, you have negative gamma (you don’t want large price movements) and negative vega (you don’t want IV to rise). You hope for low realized volatility and falling IV. However, you have positive theta — time works in your favor.

Theoretically, a delta-hedged short option position’s P&L = vega(IV – RV).1

Construction

Historically, NIFTY ATM option Implied Volatility across days-to-expiry, looks like this:

So, theoretically, if you shorted 30dte ATM calls and exited them at 7dte, your P&L distribution will look like this:

And the same thing with puts:

If you are willing to treat volatility as just-an-other beta, then by creating programmatic delta-hedged short ATM straddle/strangle portfolio, you can get long this beta.

Just as it is with ERP, one could build models to time VRP. Having a beta portfolio as a benchmark should help.

  1. Volatility Trading, Euan Sinclair ↩︎

Bitcoin Volatility Seasonality

Is Bitcoin Volatility seasonal? Yes.

There are calm months and there are frantic ones…

When you decompose the series, you can see the ebb and flow of monthly seasonality.

The pattern largely holds post-COVID as well — even after Bitcoin began its journey as an institutionally accepted asset.

Zooming in on the seasonal component alone, you can see how it troughs around October-November.

And this has tracked post-COVID as well.

The seasonal component has been negative during the months of July through December indicating that the volatility experienced during that time was idiosyncratic.

Code and charts on github.

Related: INDIA VIX Seasonality

Prophet for VIX

Open sourced by Meta back in 2017, Prophet is a procedure for forecasting time series data. How does it compare to GARCH(1,1) and locf (last one carried forward) for forecasting VIX 20 days out?

We fit 500-days of rolling VIX data using Prophet and GARCH(1,1) and forecast forward 20-days. We then calculate the RMSE (Root Mean Squared Error) of the forecast vs. actual of both the models and locf. Plot RMSE of all three.

Ideally, you want the error to be low and the tail of errors to be as short as possible. GARCH(1,1) looks worse than Prophet. However, locf beat both?

When in doubt, take the average.

Code on github.

Also: VIX Seasonality

Volatility Targeting

There are a number of ways to construct low-volatility portfolios. You could either use a bottom-up approach of selecting individual stocks that have low-volatility or you could you could run them through a portfolio optimizer (Low Volatility: Stock vs. Portfolio) to get target weights. However, if you are trading a single index, then you could use its own volatility to scale your exposure up and down. The advantage here is that if you trade index futures, you can set the volatility and leverage dials to the risk that you are most comfortable with.

Let’s take our own NIFTY 50, for example. Calculate the std-dev of daily returns over a sensible window. The index exposure is simply the ratio of the median std-dev vs. the current std-dev. Use a scaling factor (tvf) to further fine-tune the risk. To reduce transaction costs, rebalance once a week.

A tvf of 0.25 has roughly half the returns of buy & hold but with superior risk metrics that makes it receptive to leverage.

The problem with this approach is that the weights are continuous. What if you want them discrete so that it directly maps to how many lots of NIFTY you need to trade?

Here, we bucket the std. dev. into quintiles and use that to set our exposures in discrete steps.

At 2x leverage, you will outperform buy & hold by 5% with only half its drawdown.

The same for NIFTY MIDCAP SELECT looks like this:

The stats for this index looks worse than buy & hold. However, volatility sizing has resulted in lower drawdowns.

Stats and charts for different indices and code are on github.

Also read: Large Moves Happen Together

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.