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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Simple macroeconomics for trading

June 7, 2017

Most modern dynamic economic models are too complex and ambiguous to support macro trading. A practical alternative is a simplified static model of the “New Keynesian” tradition that combines basic insights from dynamic equilibrium theory with an intuitive and memorable representation. Macro traders can analyse real life events in this framework my shifting curves in a simple diagram. In this way they can analyse the effect of fiscal policy shocks, monetary policy shocks, inflation expectation shocks, economic supply shocks and so forth. Part 1 of this post focuses on a model for a large closed economy (or the world as a whole).

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FX returns and external balances

March 29, 2017

A new paper supports the view that currency excess returns can to some extent be viewed as compensation for risk to net capital flows in imperfect markets. An increase in current account uncertainty can be approximated by economists’ forecast dispersion. Historically, a rise in current account uncertainty has reduced returns on carry currencies and investment currencies, i.e. those of countries with net capital inflows. There is also evidence that markets have been sluggish in adapting to higher uncertainty.

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Lessons from long-term global equity performance

February 11, 2017

A truly global and long-term (116 years) data set for both successful and failed financial markets shows that equity has delivered positive long-term performance in each and every country that did not expropriate capital owners, even those that were ravaged by wars. Also, equity significantly outperformed government bonds in every country, with a world average annual return of 5% versus 1.8%. The long-term Sharpe ratio on world equity has been 0.24 versus 0.09 for bonds. Valuation-based strategies for market timing have historically struggled to improve equity portfolio performance. Active management strategies that rely on both valuation and momentum would have been more useful.

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Statistical remedies against macro information overload

February 4, 2017

“Dimension reduction” condenses the information content of a multitude of data series into small manageable set of factors or functions. This reduction is important for forecasting with macro variables because many data series have only limited and highly correlated information content. There are three types of statistical methods.The first type selects a subset of “best” explanatory variables (view post here). The second type selects a small set of latent background factors of all explanatory variables and then uses these background factors for prediction (Dynamic Factor Models). The third type generates a small set of functions of the original explanatory variables that historically would have retained their explanatory power and then deploys these for forecasting (Sufficient Dimension Reduction).

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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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