R tidyverse for macro trading research

September 19, 2020

The tidyverse is a collection of packages that facilitate data science with R. It is particularly powerful for macro trading research because [a] it supports efficient and standardized work with R’s vast universe of econometric models, [b] is well adapted for analyzing data vintages (i.e. data series that change over time), and [c] supports code in form of visually clean chains of statistical operations. The tidyverse’s core and peripheral packages share common design principles that harmonize workflow for crucial tasks: [1] organizing data structures, [2] transforming the content of data structures, [3] functional programming with complex nested data sets, [4] extraction of statistical information across models in a standardized form, [5] coding and mathematics with date-time objects, [6] coding with strings and regular expressions, [7] a flexible machine learning workflow, [8] highly versatile and consistent graphics creation, and [9] connectors to financial analysis packages.

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Nowcasting with MIDAS regressions

September 5, 2020

Nowcasting macro-financial indicators requires combining low-frequency and high-frequency time series. Mixed data sampling (MIDAS) regressions explain a low-frequency variable based on high-frequency variables and their lags. For instance, the dependent variable could be quarterly GDP and the explanatory variables could be monthly activity or daily market data. The most common MIDAS predictions rely on distributed lags of higher frequency regressors to avoid parameter proliferation. Analogously, reverse MIDAS models predict a high-frequency dependent variable based on low-frequency explanatory variables. Compared to state-space models (view post here), MIDAS simplifies specification and theory-based restrictions for nowcasting. The R package ‘midasr’ estimates models for multiple frequencies and weighting schemes. In practice, MIDAS has been used for nowcasting financial market volatility, GDP growth, inflation trends and fiscal trends.

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Market-implied macro shocks

August 22, 2020

Combinations of equity returns and yield-curve changes can be used to classify market-implied underlying macro news. The methodology is structural vector autoregression. Theoretical ‘restrictions’ on unexpected changes to this multivariate linear model allow identifying economically interpretable shocks. In particular, one can distinguish news on growth, monetary policy, common risk premia and hedge premia. Monetary and growth news capture shocks to investors’ expectations of discount rates and cash flows, respectively. The common risk premium is a price for exposure to risks that drive stock and bond returns in the same direction. The hedge premium is a price for exposure to risks that drive stock and bond returns in opposite directions. Identifying shocks helps to uncover trading opportunities, including market trends and reversion of relative market returns that were inconsistent with actual macro developments.

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Measures of market risk and uncertainty

August 8, 2020

In financial markets, risk refers to the probability distribution of future returns. Uncertainty is a broader concept that encompasses ambiguity about the parameters of this probability distribution. There are various types of measures seeking to estimate risk and uncertainty: [1] realized and derivatives-implied distributions of returns across assets, [2] news-based measures of policy and political uncertainty, [3] survey-based indicators, [4] econometric measures, and [5] ambiguity indices. The benefits for macro trading are threefold. First, uncertainty measures provide a basis for comparing the market’s assessment of risk with private information and research. Second, changes in uncertainty indicators often predict near-term flows in and out of risky asset classes. Third, the level of public and market uncertainty is indicative of risk premia offered across asset classes.

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Nowcasting for financial markets

July 25, 2020

Nowcasting is a modern approach to monitoring economic conditions in real-time. It makes financial market trading more efficient because economic dynamics drive corporate profits, financial flows and policy decisions, and account for a large part of asset price fluctuations. The main technology behind nowcasting is the dynamic factor model, which condenses the information of numerous correlated ‘hard’ and ‘soft’ data series into a small number of ‘latent’ factors. A growth nowcast can be interpreted as the factor that is most correlated with a diverse representative set of growth-related data series. The state-space representation of the dynamic factor model formalizes how markets read economic data in real-time. The related estimation technique (‘Kalman filter’) generates projections for all data series and estimates for each data release a model-based surprise, called ‘news’. In recent years machine learning models, such as support vector machines, LASSO, elastic net and feed-forward artificial neural networks, have been deployed to improve the predictive power of nowcasts.

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How banks’ dollar holdings drive exchange rate dynamics

July 11, 2020

Non-U.S. financial institutions hold precautionary positions in U.S. dollar assets as protection against financial shocks. This gives rise to a safety premium on the dollar. The premium varies over time and, hence, not only accounts for contemporaneous exchange rate dynamics but also helps to predict exchange rate trends. An IMF paper measures non-U.S. banks’ dollar demand for 26 economies as the ratio of assets denominated in dollar to total assets by nationality. Demand for U.S. dollars tends to surge following negative financial market shocks and causes dollar strength. Non-U.S. holdings of dollar assets have also been a highly significant predictor of dollar trends in subsequent years. Thus, large holdings have heralded dollar depreciation in the past.

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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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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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