Research Review | 9 February 2024 | Cross Market Analytics

A Changing Stock-Bond Correlation: Explaining Short-term Fluctuations
Garth Flannery (BlueCove) and Daniel Bergstresser (Brandeis Intl Business School)
December 2023
This paper builds on a framework that uses macroeconomic drivers to explain long-term variation in the correlation between stocks and bonds. The existing work focuses on the relative volatility of growth and inflation and the correlation between them and explains about 70 percent of the variation in rolling 10-year stock-bond correlation. We focus on forecasting short-term variation in stock-bond correlation with measures that capture the extent to which individual forecasters’ predictions about those markets have the same sign or opposing signs. Our framework enhances stock-bond correlation forecasting at tactical horizons, which we define here as the next three months.

Analyzing Stock-Bond Correlation: A Dynamic Causal System Perspective
Sizhen Du (Peking University) and Zili Zhang (Harvest Fund Management)
December 2023
The stock-bond correlation is a critical factor in asset allocation decisions. However, relying solely on extrapolating historical correlations of monthly returns is often unreliable. To address this issue, this paper introduces a novel framework from a dynamic causality system perspective for analyzing and predicting the stock-bond correlation. Firstly, considering the complex and nonlinear relationship between stocks and bonds that does not satisfy separability, we employ the convergent cross mapping method to test the causality between these two asset classes. Additionally, we utilize the S-map method to measure the dynamic causality strength and empirically examine its impact on correlation changes. Subsequently, we incorporate the causal strength into a correlation coefficient prediction model, creating a novel prediction model. Our findings can be summarized as follows. Firstly, there exists a bidirectional causal relationship between stocks and bonds. Secondly, causality plays a significant role in driving the stock-bond correlation. Lastly, the incorporation of causal strength enhances the performance of correlation coefficient prediction models, leading to improved asset allocation outcomes.

Cross-asset linkages and media sentiment
Ioanna Lachana (University of London)
November 2023
This paper investigates if time-varying comovements between daily stock, Treasury bond and commodity market returns can be explained with the help of investor sentiment obtained by social media textual data. Using a large data set of daily articles from 2007 to 2020 this study shows that the investor sentiment extracted from the data can be used to signal changing correlation direction for stocks-bonds. The study also finds that bond returns tend to be high during times of negative investor sentiment while stock returns tend to be negative. The findings suggest that stock-bond diversification benefits increase with worsening of investor sentiment.

Tail Risk and Flight-to-Safety
Xinyang Li (Ph.D. graduate, Boston University)
June 2023
Using information from equity and Treasury bond options, I propose a new Flight-to-Safety (FTS) regime-switching model with time-varying stock-bond correlation and regime-switching probabilities. I document that the inclusion of higher-order moments such as tail risks is crucial to capturing Flight-to-Safety. In particular, higher bond tail risk lowers investors’ intention to Flight-to-Safety. Time-varying tail risk correlation between stocks and bonds helps explain return correlation. Also, model estimates imply that FTS comprises 30% of the sample, which is significantly higher than the earlier work. I then apply my model with FTS probability-based asset allocation to prove that it significantly outperforms other models.

When do Treasuries Earn the Convenience Yield? — A Hedging Perspective
Viral V. Acharya and Toomas Laarits (New York University)
November 2023
We document that the convenience yield of U.S. Treasuries exhibits properties that are consistent with a hedging perspective of safe assets. The convenience yield tends to be low when the covariance of Treasury returns with the aggregate stock market returns is high. A decomposition of the aggregate stock-bond covariance into terms corresponding to the convenience yield, the frictionless risk-free rate, and default risk reveals that the covariance between stock returns and the convenience yield itself drives the effect in a substantive capacity. We show the convenience yield is reduced with heightened inflation expectations that erode the hedging properties of U.S. Treasuries and other fixed-income money-like assets, inducing a switch to alternatives such as gold; it is also reduced immediately prior to debt-ceiling standoffs and with increases in Treasury supply.

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One thought on “Research Review | 9 February 2024 | Cross Market Analytics

  1. Pingback: Quantocracy's Daily Wrap for 02/11/2024 - Quantocracy

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