Interest rate swap returns: empirical lessons

March 17, 2018

Interest rate swaps trade duration risk across developed and emerging markets. Since 2000 fixed rate receivers have posted positive returns in 26 of 27 markets. Returns have been positively correlated across virtually all countries, even though low yield swaps correlated negatively with global equities and high-yield swaps positively. IRS returns have posted fat tails in all markets, i.e. a greater proclivity to outliers than would be expected from a normal distribution. Active volatility management failed to contain extreme returns. Relative IRS positions across countries can be calibrated based on estimated relative standard deviations and allow setting up more country-specific trades. However, such relative IRS positions have even fatter tails and carry more directional risk. Regression-based hedging goes a long way in reducing directionality, even if risk correlations are circumstantial rather than structural.

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Directional predictability of daily equity returns

March 3, 2018

A new empirical paper provides evidence that the direction of daily equity returns in the Dow Jones has been predictable over the past 15 years, based on conventional short-term factors and out-of-sample selection and forecasting methods. Hit ratios have been 51-52%. The predictability has been statistically significant and consistent over time. Trading returns based on forecasting have been economically meaningful. Simple forecasting methods have outperformed more complex machine learning.

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Information inefficiency in market experiments

February 3, 2018

Experimental research illustrates the mechanics of market inefficiency. If information is costly traders will only procure it to the extent that markets are seen as inefficient. In particular, when observing others’ investment in information, traders will cut their own information spending. Full information efficiency can never be reached. Moreover, business models that invest heavily in information may have higher trading profits, but still earn lower overall profits due to the costs of improving their signals. What seems crucial is high cognitive reflection so as to invest in relevant information where or when others do not.

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Clues for estimating market beta

January 6, 2018

A new empirical paper compares methods for estimating “beta”, i.e. the sensitivity of individual asset prices to changes in a broad market benchmark. It analyzes a large range of stocks and more than 50 years of history. The findings point to a useful set of initial default rules for beta estimation: [i] use a lookback window of about one year, [ii] apply an exponential moving average to the observations in the lookback window, and [iii] adjust the statistical estimates by reasonable theoretical priors, such as the similarity of betas for assets with similar characteristics.

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Gold: risk premium and expected return

December 23, 2017

An empirical paper suggests that the risk premium and excess return on gold have been time-varying and predictable, also out-of-sample. The key predictors have been the variance risk premium and the jump risk premium of gold. Gold has historically also served as a hedge and “safe haven” for equity and bond investments, but this could not have been expected based on forecasting models. Common sense suggests that the hedge value of gold depends on the dominant market shock. For example, gold hedges against inflationary policies but not against rising real interest rates.

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The predictability of relative asset returns

November 4, 2017

Empirical research suggests that it is easier to predict relative returns within an asset class than to predict absolute returns. Also, out-of-sample value generation with standard factors has been more robust for relative positions than for outright directional positions. This has been shown for bond, equity and currency markets. Importantly, directional and relative predictability have been complementary sources of investment returns, suggesting that using both will produce best performance.

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Financial econometrics and machine learning

September 16, 2017

Supervised machine learning enhances the econometric toolbox by methods that find functional forms of prediction models in a manner that optimizes out-of-sample forecasting. It mainly serves prediction, whereas classical econometrics mainly estimates specific structural parameters of the economy. Machine learning emphasizes past patterns in data rather than top-down theoretical priors. The prediction function is typically found in two stages: [1] picking the “best” form conditional on a given level of complexity and [2] picking the “best” complexity based on past out-of-sample forecast performance. This method is attractive for financial forecasting, where returns depend on many complex relations most of which are not well understood even by professionals, and where backtesting of strategies should be free of theoretical bias that arises from historical experience.

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Predicting asset price correlation for dynamic hedging

July 29, 2017

Dynamic hedging requires prediction of correlations and “betas” across asset classes and contracts. A new paper on dynamic currency hedging proposes two enhancements of traditional regression for this purpose. The first is the use of option-implied volatilities, which are plausibly related to future actual volatility and correlation across assets. The second enhancement is the use of parameter shrinkage in regression estimation (LASSO method), which mitigates the risk of overfitting.

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What traders can learn from market price volatility

July 1, 2017

Equity and bond market volatility can be decomposed into persistent and transitory components by means of statistical methods. The distinction is relevant for macro trading because plausibility and empirical research suggest that the persistent component is associated with macroeconomic fundamentals. This means that persistent volatility is an important signal itself and that its sustainability depends on macroeconomic trends and events. Meanwhile, the transitory component, if correctly identified, is more closely associated with market sentiment and can indicate mean-reverting price dynamics.

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Simple international macroeconomics for trading

June 10, 2017

Simple New Keynesian macroeconomic models work well for analyzing the impact of various types of shocks on small open economies and emerging markets. The models are a bit more complex than those for large economies, because one must consider the exchange rate, terms-of-trade and financial pressure. Yet understanding some basic connections between market factors and the overall economy already supports intuition for macro trading strategies. Moreover, the analysis of the effect of various shocks is possible in simple diagrams.

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