Author: shyam

Volatility, Volatility of Volatility, and Momentum

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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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Weighted Strike-Spot Ratio

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Can options trading predict the underlying’s returns? Center of Volume Mass: Does Options Trading Predict Stock Returns? Bernile, Gao, Hu (SSRN) tries to answer the age-old question.

They construct an options volume weighted strike-spot ratio and use that the predict the underlying’s next-day returns.

We rely on the volume-weighted strike-spot ratio to characterize the central location of the distribution of trading activity along the moneyness of available option contracts on the same stock. The ratio of the contract’s strike price (K) and the underlying stock price (S) measures the option moneyness, whereby call (put) options are out-of-the-money when K/S is above (below) one. After normalizing K/S by subtracting one, we calculate the weighted average of the normalized K/S ratio across available contracts using as weights the number of lots traded on each contract during the same period (V WKS, hereafter). V WKS
reflects the center of mass in the options volume distribution along strike prices of available contracts and takes on higher (lower) values when the trading volume is tilted more toward OTM (ITM) calls and ITM (OTM) puts.

While their results look promising, we setup a very simple backtest to see if it can be used to trade the NIFTY. Sadly, both net Open Interest and Value traded fail to show any effect on next-day returns.

I guess this is one more for the #fail pile.

Code and charts are on github.

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Mahalanobis Distance with Trend

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Previously, we constructed a portfolio that switches between equities and bonds based on the Mahalanobis distance between them. Here, keeping everything else the same, we add a trend filter to the same set of indices.

The composite regime-switching model ends up with superior Sharpe Ratios. However, if you don’t switch to bonds (and stay in cash, earning zero), then you maybe better off with a simple trend model.

The alpha seems to be in earning the risk-free rate when things are “bad” and getting long equities only when things are “favorable.”

Code and charts are on github.

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Mahalanobis Distance

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We are big fans on using distance measures while prospecting for investment strategies. Previously:

Recently, we came across an interesting paper, Skulls, Financial Turbulence, and Risk Management, Mark Kritzman, CFA, and Yuanzhen Li, that uses the Mahalanobis distance to construct a turbulence index. The basic idea is that the more asset returns break from the past, the more “significant” a market event.

We took the basic intuition behind this and constructed a portfolio that switches between equities and bonds based on the Mahalanobis distance between them.

The out-of-sample results, factoring in transaction costs, look promising but doesn’t really stand out compared to other, more dumber, strategies that avoid steep drawdowns. However, two points over the Midcap buy & hold cannot be dismissed outright.

The code, charts and paper are on github.

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Large Moves Happen Together

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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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