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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The danger of volatility feedback loops

February 17, 2018

There is evidence that the financial system has adapted to low fixed income yields through an expansion of explicit and implicit short volatility strategies. These strategies earn steady premia but bear large volatility, “gamma” and correlation risks and include popular devices such as leveraged risk parity and share buybacks. The total size of explicit and implicit short-volatility strategies may have reached USD2000 billion and probably created two dangerous feedback loops. The first is a positive reinforcement between interest rates and volatility that will overshadow central banks’ attempts to normalize policy rates. The second is a positive reinforcement between measured volatility and the effective scale of short-volatility positions that has increased the risk of escalatory market volatility spirals.

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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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Basic theory of momentum strategies

January 27, 2018

Systematic momentum trading is a major alternative risk premium strategy across asset classes. Time series momentum motivates trend following; cross section momentum gives rise to ‘winners-minus-losers strategies’. Trend following is a market directional strategy that promises ‘convex beta’ and ‘good diversification’ for outright long and carry portfolios as it normally performs well in protracted good and bad times alike. It works best if the underlying assets earn high absolute (positive or negative) Sharpe ratios and display low correlation. By contrast, cross section momentum strategies benefit from high absolute correlation of underlying contracts and are more suitable for trading assets of a homogeneous class. The main pitfalls of both momentum strategies are jump events and high costs of ‘gamma trading’ conjoined with high leverage.

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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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The point of volatility targeting

December 30, 2017

Volatility targeting adjusts the leverage of a portfolio inversely to predicted volatility. Since market volatility is predictable in the short run and returns are not this adjustment typically improves conventional risk-adjusted return measures, such as the Sharpe ratio. An empirical analysis for the U.S. equity market over the past 90 years confirms this point but suggests that the real key benefit of volatility targeting is the reduction of outsized drawdowns in extreme market situations. That is because large cumulative losses mostly occur when market volatility remains high for long. On these occasions volatility targeting has benefits somewhat similar to a momentum strategy, selling risk early into market turmoil, thereby positioning for escalation.

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The correlation of equity and bond returns

December 16, 2017

History shows that the correlation of equity and bond returns has been either positive or negative for prolonged periods of time. Monetary policy has played a key role for the direction of equity-bond correlation. In periods of restrictive monetary policy the correlation has been positive. In periods of low inflation and accommodative monetary policy the equity-bond correlation has been negative. The latter regime has predominated since the late 1990s and is critical for performance and sustainability of risk-parity trading strategies.

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The downside variance risk premium

December 9, 2017

The variance risk premium of an asset is the difference between options-implied and actual expected return variation. It can be viewed as a price for hedging against variation in volatility. However, attitudes towards volatility are asymmetric: large upside moves are fine while large downside moves are scary. A measure of aversion to negative volatility is the downside variance risk premium, the difference between options-implied and actual expected downside variation of returns. It is this downside volatility risk that investors want to protect against and whose hedging price is a valid and apparently robust indicator of future returns. Similarly, the skewness risk premium, the difference between upside and downside variance risk premia, is also a powerful predictor of markets.

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The 1×1 of risk perception measures

December 1, 2017

There are two reasons why macro traders watch risk perceptions. First, sudden spikes often trigger subsequent flows and macroeconomic change. Second, implausibly high or low values indicate risk premium opportunities or setback risks. Key types of risk and uncertainty measures include [1] keyword-based newspaper article counts that measure policy and geopolitical uncertainty, [2] survey-based economic forecast discrepancies, [3] asset price-based measures of fear and uncertainty and [4] derivatives-implied cost of hedging against directional risk and price volatility.

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Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.

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