Modern financial system risk for macro trading

June 27, 2020

Financial system risk is the main constraint and disruptor of macro trading strategies. There are four key areas of modern systemic risk. [1] In the regulated banking sector vulnerability arises from high leverage and dependence on funding conditions. The regulatory reform of the 2010s has boosted capital ratios and liquidity safeguards. However, it has also induced new hazards, such as accumulation of sovereign risk, incentives for regulatory arbitrage, and risk concentration on central clearing counterparties. [2] Shadow banking summarizes financial intermediation outside the reach of standard regulation. It channels cash pools to the funding of asset holdings. Vulnerability arises from dependence on the market value of collateral and the absence of bank backstops. [3] Institutional asset management has grown rapidly in past decades and is now comparable in size to regulated banking. Asset managers play a vital role in global funding conditions but are prone to aggravating self-reinforcing market momentum. [4] Finally, emerging market financial systems have grown in size and complexity. China constitutes a global systemic risk factor due to the aggressive use of financial repression to sustain high levels of leverage and investment.

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External imbalances and FX returns

June 13, 2020

Hedge ratios of international investment positions have increased over past decades, spurred by regulation and expanding derivative markets. This has given rise to predictable movements in spot and forward exchange rates. First, on balance hedgers are long currencies with positive net international investment positions and short those with negative international investment positions. With intermediaries requiring some profit for balance sheet usage these trades command negative premia and widen cross-currency bases. Second, hedge ratios increase in times of rising FX volatility. An increase in the hedge ratio for a currency puts downward pressure on its market price in proportion to its external imbalance and bodes for higher medium-term returns. Also, the dispersion of cross-currency bases increases in times of turmoil.

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Predicting volatility with heterogeneous autoregressive models

May 30, 2020

Heterogeneous autoregressive models of realized volatility have become a popular standard in financial market research. They use high-frequency volatility measures and the assumption that traders with different time horizons perceive, react to, and cause different types of volatility components. A key hypothesis is that volatility over longer time intervals has a stronger impact on short-term volatility than vice versa. This leads to an additive volatility cascade and a simple model in autoregressive form that can be estimated with ordinary least squares regression. Natural extensions include weighted least-squares estimations, the inclusion of jump-components and the consideration of index covariances. Research papers report significant improvement of volatility forecasting performance compared to other models, across equity, fixed income, and commodity markets.

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Joint predictability of FX and bond returns

May 23, 2020

When macroeconomic conditions change rational inattention and cognitive frictions plausibly prevent markets from adjusting expectations for futures interest rates immediately and fully. This is an instance of information inefficiency. The resulting forecast errors give rise to joint predictability of currency and bond market returns. In particular, an upside shock to the rates outlook in a country heralds positive (rationally) expected returns on its currency and negative expected returns on its long-term bond. This proposition has been backed by empirical evidence for developed markets over the past 30 years.

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The predictive power score

May 16, 2020

The predictive power score is a summary metric for predictive relations between data series. Like correlation, it is suitable for quick data exploration. Unlike correlation, it can work with non-linear relations, categorical data, and asymmetric relations, where variable A informs on variable B more than variable B informs on variable A. Technically, the score is a measurement of the success of a Decision Tree model in predicting a target variable with the help of a predictor variable out-of-sample and relative to naïve approaches. For macro strategy development, predictive power score matrices can be easily created based on an existing python module and can increase the efficiency of finding hidden patterns in the data and selecting predictor variables.

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Equilibrium theory of Treasury yields

May 2, 2020

An equilibrium model for U.S. Treasury yields explains how macroeconomic trends and related expectations for future short-term interest rates shape the yield curve. Long-term yield trends arise from learning about stable components in GDP growth and inflation. They explain the steady rise of Treasury yields in the 1960s-1980s and their decline in the 1990s-2010s. Cyclical movements in yields curves result from learning about transitory deviations of GDP growth and inflation. They explain why curves have been steep out of recessions and inverted in mature economic expansions. Finally, since the 2000s pro-cyclical inflation expectations and fears for secular stagnation have accentuated the steepness of the Treasury curve; positive correlation between inflation and growth expectations means that the Fed can cut rates more drastically to support the economy.

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Public finance risk

April 25, 2020

Fiscal expansion was the logical response to the 2020 health and economic crisis. Alas, public deficit and debt ratios had already been historically high before. The IMF estimates that this year’s general government deficit in the developed world will reach 11% of GDP, while the government debt stock will exceed 120% of GDP. Fiscal sustainability relies on low or negative real interest rates. Yet, on smaller and lower-grade countries credit spreads have risen and become more volatile. In the large developed economies, central bank purchases help to absorb the glut of debt issuance but cannot prevent balance sheet deterioration. Concerns over sovereign credit risk or – more realistically – debt monetization are rational. Fiscal risk and related government strategies will likely be key drivers of financial market trends for years to come.

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Factor timing

April 18, 2020

Factors beyond aggregate market risk are sources of alternative risk premia. Factor timing addresses the question when to receive and when to pay such risk premia. A new method for predicting the performance of cross-sectional equity return factors proposes to focus only on the dominant principal components of a wide array of factors. This dimension reduction seems to be critical for robust estimation. Forecasts of the dominant principal components can serve as the basis of portfolio construction. Empirical evidence suggests that predictability is significant and that market-neutral factor timing is highly valuable for portfolio construction, over and above directional market timing. Factor timing is related to macroeconomic conditions, particularly at business cycle frequency.

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Macro trading and macroeconomic trend indicators

April 11, 2020

Macroeconomic trends are powerful asset return factors because they affect risk aversion and risk-neutral valuations of securities at the same time. The influence of macroeconomics appears to be strongest over longer horizons. A macro trend indicator can be defined as an updatable time series that represents a meaningful economic trend and that can be mapped to the performance of tradable assets or derivatives positions. It can be based on three complementary types of information: economic data, financial market data, and expert judgment. Economic data establish a direct link between investment and economic reality, market data inform on the state of financial markets and economic trends that are not (yet) incorporated in economic data, and expert judgment is critical for formulating stable theories and choosing the right data sets.

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A statistical learning workflow for macro trading strategies

April 4, 2020

Statistical learning for macro trading involves model training, model validation and learning method testing. A simple workflow [1] determines form and parameters of trading models, [2] chooses the best of these models based on past out-of-sample performance, and [3] assesses the value of the deployed learning method based on further out-of-sample results. A convenient technology is the ‘list-column workflow’ based on the tidyverse packages in R. It stores all related objects in a single data table, including models and nested data sets, and implements statistical learning through functional programming on that table. Key steps are [1] the creation of point-in-time data sets that represent information available at a particular date in the past, [2] the estimation of different model types based on initial training sets prior to each point in time, [3] the evaluation of these different model types based on subsequent validation data just before each point in time, and [4] the testing of the overall learning method based on testing data at each point in time.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation carry China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
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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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