Critical transitions in financial markets

September 30, 2017

Critical transitions in financial markets are shifts in prices and operational structure to a new equilibrium after reaching a tipping point. “Complexity theory” helps analysing and predicting such transitions in large systems. Quantitative indicators of a market regime change can be a slowdown in corrections to small perturbations, increased autocorrelation of prices, increased variance and skewness of prices, and a “flickering” of markets between different states. A new research paper applies complexity theory to changes in euro area fixed income markets that arose from non-conventional policy. It finds that quantitative indicators heralded critical structural shifts in unsecured money markets and high-grade bond markets.

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Hedging FX trades against unwanted risk

September 23, 2017

When FX forward positions express views on country-specific developments one can shape the trade to its rationale by hedging against significant unrelated global influences. Almost all major exchange rates are sensitive to directional global market moves and USD-based exchange rates are typically also exposed to EURUSD changes. A simple empirical analysis for 29 currencies for 1999-2017 suggests that the largest part of these influences has been predictable out-of-sample and hence “hedgeable”. Even volatility-adjusted relative positions across EM or FX carry currencies may sometimes be hedged against market directional influences.

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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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FX forward returns: basic empirical lessons

September 9, 2017

FX forward returns for 29 floating and convertible currencies since 1999 provide important empirical lessons. First, the long-term performance of FX returns has been dependent on economic structure and clearly correlated with forward-implied carry. The carry-return link has weakened considerably in the 2010s. Second, monthly returns for all currencies showed large and frequent outliers beyond the borders of a normal random distribution. Simple volatility targeting would not have mitigated this. Third, despite large fundamental differences, all carry and EM currencies have been positively correlated among themselves and with global risk benchmarks. Fourth, relative standard deviations across currencies have been predictable and partly structural. Hence, they have been important for scaling FX trades across small currencies.

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Treasury yield curve and macro trends

September 2, 2017

There is a strong logical and empirical link between the U.S. Treasury yield curve and long-term economic trends, particularly expected inflation and the equilibrium short-term real interest rate. Accounting for variations in these two trends allows isolating cyclical factors in a non-arbitrage term structure model. Put simply, interest rates mean-revert to a ‘shifting endpoint’ that is driven by macroeconomics. According to new research, term structure models that include long-term macro trends substantially improve yield forecasts for the medium term as well as predictions of bond excess returns.

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China’s internal debt overload: a refresher

August 26, 2017

According to the latest IMF China report credit to non-financial institutions has soared to over 230% of GDP, an increase of 60%-points and a doubling in nominal terms from 2011 to 2016. Credit efficiency, i.e. the benefit of new lending in terms of economic output, has deteriorated markedly. Corporate lending has soared with an outsized allocation to state-owned enterprises, particularly to “zombie” and overcapacity firms. The credit boom has been supported by an abnormally high national savings rate of over 45% of GDP, which is likely to decline going forward. Historically, almost all credit booms that were similar to China’s in size and speed ended in a major downturn or credit crisis.

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Fear of drawdown

August 19, 2017

Experimental research suggests that probability of outright loss rather than volatility is the key driver of investor risk perceptions. Moreover, fear of drawdown causes significant differences of prices for assets with roughly equal expected returns and standard deviations. Investors forfeit significant expected returns for the sake of not showing an outright loss at the end of the investment period. This suggests that trading strategies with a high probability of outright losses produce superior volatility-adjusted returns. Rational acceptance of regular periodic drawdowns or “bad years” should raise long-term Sharpe ratios.

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Covered interest parity: breakdowns and opportunities

August 12, 2017

Since the great financial crisis conventional measures of the covered interest parity across currencies have regularly broken down. Two developments seem to explain this. First, money markets have become more segmented, with top tier banks having access to cheaper and easier funding, particularly in times distress. Second, FX swap markets have experienced recurrent imbalances and market makers have been unable or unwilling to buffer one-sided order flows. Profit opportunities arise for some global banks in form of arbitrage and for other investors in form of trading signals for funding liquidity risk premia.

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The demographic compression of interest rates

August 5, 2017

Declining population growth and rising dependency ratios in the developed world have been one key factor behind the decline in nominal and real interest rates since the 1980s. Personal savings for retirement are growing, while investment spending is not rising commensurately, and long-term economic growth is dampened by slowing or even shrinking work forces. A new ECB paper suggests that for the euro area these trends will likely continue to compress interest rates for another 10 years, a challenge for monetary policy and financial stability.

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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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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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