Category: Investing Insight

Investing insight to make you a better investor.

Understanding Futures Rollover Cost

What is the difference between buying gold through an ETF over buying the front-month futures contract and constantly rolling it over?

When you buy physical gold, there is a cost of carry involved (funding rate + storage), plus an ETF will charge an asset management fee.

Futures also have a similar cost of carry plus a rollover cost. At expiry, the price at which you sell the expiring contract and buy the next month contract is not the same. The differential is the rollover cost.

Typically, the farther you go out on the futures termstructure, the higher the premium to spot – the commodity needs to be financed and stored for longer. This is called contango.

Currently, gold futures (GC) traded on COMEX has the following termstructure:

However, sometimes, the demand for near delivery is much higher than future delivery. This typically happens during a supply shock. When the near expiry futures trade at a premium to later expiries, the termstructure is said to be in backwardation.

Currently, oil futures (CL) traded on NYMEX has the following termstructure:

During contango, rolling over long futures incurs a positive rollover cost, negative otherwise.

For Gold Minis (GOLDM) traded on the MCX, the historical rollover cost at expiry has fluctuated within a wide band:

What this means for our analysis is that if we merely lined up the closing prices of the front-month contract and calculated returns, we will be off by ~2.5% (not considering brokerage, fees and CTT):

So, to answer the question we posed at the beginning of this post, GOLDBEES or GOLDM?

GOLDBEES, definitely.

Previously: Investing in Gold

Charts and code on github.

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Sector Momentum

Previously, we had looked at using the momentum of S&P 500 Sector SPDRs for potential rotation strategies. How would the Indian story unfold?

We take 16 sector indices, use a 6-month look-back window and go long the sector with the highest returns, holding it for a month.

You end up with higher returns but lower Sharpe – makes sense given the super-concentrated nature of the portfolio.

The 4 points of out-performance (after costs, pre-tax) over the NIFTY 100 index is not much to write home about. Besides, this strategy trailed the benchmark pre-2020. If this were pitched back then, nobody would’ve deployed it and nobody would’ve been around for the post-2020 out-performance. On a positive note, the availability of index funds and ETFs should make this strategy fairly easy to implement.

The main caveat is that the index construction rules themselves are subject to change. Mid last year, SEBI capped the maximum concentration of a single stock for a sector index at 35% and required them to have at least 10 stocks.

Code and charts are on github.

Here are some other things we tried, so that you don’t have to:

Equal-weight all Sector Indices

Inverse-volatility weight all Sector Indices

Equal-weight Sectors in an Up Trend

Inverse-volatility weight Sectors in an Up Trend

The excess returns of these alternatives do not justify the costs.

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Budget-day Options

A lot of ink is spilled on the budget. Some are prescriptive (and completely useless.) Some are predictive (and mostly wrong.) Most investors will do well to just ignore the noise and continue with their SIP/DCA. However, if you do want to trade it, what should you do?

Of the last 26 budgets, 16 ended the day red. You could just short the NIFTY and play the odds.

Budget days tend to have huge intraday ranges that lead to dislocations that you could monetize. However, this is largely a high-frequency trading affair and may not be feasible for most.

Another thing worth pursuing are delta-hedged short-strangles. The table above gives you the P&L of shorting NIFTY ATM delta-hedged strangles overnight and closing them on budget-day (or the immediate business-day.) There’s a fair amount of execution risk here given the intraday volatility on the day. However, it seems like a decent profit pool to fish in.

Code and charts on github.

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Macro: Timing the NIFTY 50

Prior research has shown that there is no correlation between GDP growth and stock market returns (see: The Enigma of Economic Growth and Stock Market Returns).

GDP is a trailing measure. However, does the relationship change if we use leading economic indicators?

To answer this question, we look to the OECD Composite Leading Indicator database. It is a monthly time series of CLIs of different regions. Here’s India’s and the G7’s charted from 1980:

If we scatter India’s CLI with next month’s NIFTY 50 returns, we get:

No correlation whatsoever.

However, we know that the market likes growth. So, what happens if we scatter the diff of the CLI over returns?

Noisy, but not hopeless!

Turns out, if you go long NIFTY 50 only when the CLI is improving, you get a 2% boost over the long run return.

The kicker here is that the drawdowns are a lot less severe.

Code and charts are on github.

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Roll’s Serial Covariance Spread Estimator

The book Trading and Exchanges (Amazon,) has a section on Roll’s Serial Covariance Spread Estimator which tackles the problem of estimating the bid/ask spread with only the price series.

The Roll’s serial covariance spread estimator is an econometric model designed to estimate the average bid/ask spread (or effective spread) of a security using only transaction prices, without needing quotation data. It is one of the best-known estimators based on price change serial covariances.

The idea is from the 90’s and we’ve come a long way since then. Now, we have streaming quotes from which the spread can be directly computed. What makes this approach interesting is the decomposition of volatility that was used to estimate the spread can be used to estimate fundamental volatility instead.

Total Volatility = Fundamental Volatility + Transitory Volatility

Fundamental volatility consists of seemingly random price changes that do not revert. These changes often have the properties of a random walk.

Transitory volatility consists of price changes that ultimately revert. This price reversal creates negative serial correlation in the series of price changes.

Using Roll’s model, Fundamental Volatility = Total Volatility – (Effective Spread)2/4

Here’s NIFTY through Roll’s model:

Code on Github.

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