How systemic financial risk is measured

November 10, 2018

Public institutions have developed a wide range of methods to track systemic financial risk. What most of them have in common is reliance on financial market data. This implies that systemic risk indicators typically only show what the market has already priced, in form of correlation, volatility or value. They cannot anticipate market crises. Their main use is to predict when and how market turmoil begins to sap the functioning of the financial system. Some methods may be useful for macro trading. For example, Conditional Value-at-Risk can identify sources of systemic risk, such as specific institutions or market segments. Principal Components Analysis can indicate changing concentration of risk across securities and markets.

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Equity index futures returns: lessons of 2000-2018

August 18, 2018

The average annualized return of local-currency index futures for 25 international markets has been 6% with a standard deviation of just under 20%. All markets recorded much fatter tails of returns than should be expected for normal distributions. Autocorrelation has predominantly been positive in the 2000s but decayed in the 2010s consistent with declining returns on trend following. Correlation of international equity returns across countries has been high, suggesting that global factors dominate performance, diversification is limited and country-specific views should best be implemented in form of relative positions. For smaller countries equity returns have mostly been positively correlated with FX returns, underscoring the power of international financial flows. Volatility targeting has been successful in reducing the fat tails of returns and in enhancing absolute performance. Relative volatility scaling is essential for setting up relative cross-market trades. The performance of relative positions has displayed multi-year trends in the past.

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The dangerous disregard for fat tails in quantitative finance

July 28, 2018

The statistical term ‘fat tails’ refers to probability distributions with relatively high probability of extreme outcomes. Fat tails also imply strong influence of extreme observations on expected future risk. Alas, they are a plausible and common feature of financial markets. A summary article by Nassim Taleb reminds practitioners that fat tails typically invalidate methods and conventions applied in quantitative finance. Standard in-sample estimates of means, variance and typical outliers of financial returns are erroneous, as are estimates of relations based on linear regression. The inconsistency between the evidence of fat tails and the ongoing dominant usage of conventional statistics in markets is plausibly a major source of inefficiency and trading opportunities.

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Understanding collateral runs

April 14, 2018

In normal financial runs lenders want their money back. In collateral runs borrowers want their collateral back. In today’s highly collateralized financial system the institutions at risk are broker-dealers that lend and borrow cash in secured transactions and that use part of that liquidity to fund their own asset holdings. In collateral runs cash borrowers, such as hedge funds, have an incentive to rush to repay secured loans as soon as the liquidity of a broker-dealer is being questioned. That is because haircuts keep collateral value above loan notional. The demise of Bear Sterns in 2008 illustrates that the peril of collateral runs is real. Still, this source of liquidity risk has not been well explored.

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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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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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Equity alpha through volatility targeting

February 25, 2017

Volatility targeting has historically enhanced the statistical alpha of standard equity strategies. That is because volatility is more predictable in the short-term than returns. Thus, Sharpe ratios tend to decline, when volatility rises. Expected returns increase after turmoil but only overtime, when volatility might already be subsiding. On its own volatility is not a pure measure of risk premia and does not indicate if actual risk is overstated or underappreciated. A flipside of mechanical volatility targeting is that it contributes to herding and escalatory price dynamics.

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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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