Stock-bond Correlation Research Articles

Can municipal bonds hedge US state-level climate risks?

Using daily data on municipal bonds and equity returns from the 50 US states, we find barring extreme periods of financial, macroeconomic, and health crises, the underlying conditional correlation between these two assets is negative. When we capture the effect of climate risk quantiles on the entire conditional distribution of the underlying time-varying stock-bond correlation, we generally observe a negative impact at different levels of climate risks, although this could turn positive in the event of extreme climate disasters. In summary, the role of municipal bonds as a hedge against climate risks cannot be denied, carrying important implications for investors.

The role of central bank communication in the long-term stock-bond correlations: Evidence from China

In this paper, we extend the traditional DCC-MIDAS-X model by incorporating the central bank communication sentiment indicator to examine its effect on the long-term correlations between stock and bond returns. Our results show that positive central bank communication can reduce stock-bond correlation. The effect changes over time depending on the state of the economy, which is significant during upturns but not noticeable during recessions.

Empirical Evidence on the Stock–Bond Correlation

The correlation between stock and bond returns is a cornerstone of asset allocation decisions. History reveals abrupt regime shifts in correlation after long periods of relative stability. We investigate the drivers of the correlation between stocks and bonds and find that inflation, real rates, and government creditworthiness are important explanatory variables. We examine the implications of a shift in the stock–bond correlation and find that increases are associated with higher multi-asset portfolio risk and higher bond risk premia.

Consumption Growth Persistence and the Stock–Bond Correlation

Abstract We consider a model in which the correlation between shocks to consumption and to expected future consumption growth is nonzero and varies over time. We validate this assumption empirically using the model’s implication that time variation in consumption growth persistence (CGP) drives the correlation between stock and bond returns. Our model implies that the stock–bond correlation is also related to the predictive relation between bond yields and future stock returns. Finally, we provide suggestive evidence that asset price fluctuations are the primary driver of changes in CGP.

Predicting systemic financial risk with interpretable machine learning

Predicting systemic financial risk is essential for understanding the financial system's stability and early warning of financial crises. In this research, we use the financial stress index to measure systemic financial risk. We construct the stress index for five financial submarkets and composite stress index, employ the Markov regime switching model to identify the systemic financial risk stress state. On this basis, we use interpretable machine learning models to forecast systemic financial risk, analyze and compare the results of the intrinsic interpretable machine learning models and the post-hoc explainable methods. The results indicate that systemic financial risk can be effectively predicted using both the submarket stress index and the feature variables, with the submarket stress index as the independent variable providing relatively higher accuracy. There is a linearly positive relationship between the stress index of each submarket and systemic financial risk, with financial stress in the stock and money markets having the greatest impact on systemic financial risk. For each feature variable, stock–bond correlation coefficient, stock valuation risk, the maximum cumulative loss of the SSE Composite Index (SSE CMAX), and loan-deposit ratio have strong predictive power. Our research can provide reference for government to construct prediction model and indicator monitoring platform of systemic financial crisis.

Oil price shocks and stock–bond correlation

This paper investigates the role of oil as a determinant of the US stock–bond correlation. The analysis uses monthly data over the period from February 1990 to July 2021. We examine the impact of oil shocks, using the Ready (2018) method, alongside a range of macroeconomic variables on the nature of stock–bond dynamic correlation. Our main findings demonstrate that during recessionary periods, the stock–bond correlation is adversely and statistically explained by oil supply shocks, and that correlation tends to statistically diverge from that of it is counterpart during expansionary periods. In addition, demand shocks are more pronounced in periods of pessimistic investor sentiment, whereas supply and risk shocks appear during optimistic periods. From a risk management standpoint, a backtesting exercise shows that the incorporation of supply and demand shocks generally improves the forecast of portfolio volatility under various portfolio weighting schemes and market conditions. A time-varying hedging exercise also reveals that accounting for both demand and supply shocks reduces the cost of hedging, mainly following major crisis periods. The contribution of shocks appears most with a long position in the bond market after the 2014 oil crisis period. Our main results remain the same after performing a set of the robustness checks.

Practical Applications of When to Diversify Differently

In <ext-link><bold><italic>When to Diversify Differently</italic></bold></ext-link>, from the March 2022 multiasset special issue of <bold><italic>The Journal of Portfolio Management</italic></bold>, authors <bold>Brian Jacobsen</bold> and <bold>Matthias Scheiber</bold>, both of <bold>Allspring Global Investments</bold>, determine that changes in the correlation between returns on stocks and bonds tend to be caused by financial crises and changes in investors’ fears about inflation. For the past few decades, investors have allocated assets to stocks and bonds based on the theory that the two are negatively correlated. However, before 1997, stocks and bonds had a greater propensity to decline simultaneously. Jacobsen and Scheiber find that negative stock–bond correlation is associated with low real interest rates and low inflation, while positive correlation is associated with higher rates and inflation. During a market drawdown, the correlation depends on the cause of the drawdown. If the cause is fear of higher inflation, then the correlation tends to be positive. If the cause is fear of recession or geopolitical issues, and if inflation is low, then the correlation tends to be negative and bond allocations may help protect against equity losses.

The determinants of stock–bond return correlations

AbstractI study the options‐implied market risks that affect US stock–bond correlations from 2007 to 2021. I discover that US stock and bond market uncertainty, stock market tail risk, and global credit‐default risk are dominant contributors to changing stock–bond correlations during the global financial crisis (GFC) period. However, these market risks collectively contribute much less to time‐varying correlations in the post‐GFC period. Furthermore, stock–bond correlations rise in times of rising US and global bond market risks. Rising stock market uncertainty raises stock–bond correlations in the GFC period but lowers them in the post‐GFC period. My results disentangle the risks of stock and bond markets and show that equity tail risk, bond market risk, and stock market uncertainty are dominant factors in changing stock–bond diversification benefits in periods of market turmoil.

Geopolitical Risk and Stock-Bond Interplay: A Comparative Study of Islamic and Conventional Assets in the GCC

ABSTRACT Undoubtedly, financial markets are not only affected by common economic, financial and behavioral factors but also by other global risk shocks. In this paper, we investigate the relevance of global geopolitical risk in stock-bond relationships. Specifically, we assess the possibility and manner in which time-varying correlations of Islamic stocks and bonds (sukuk) returns are affected by geopolitical risk and whether they behave differently from their conventional counterparts. We do so by implementing a multivariate GARCH model under dynamic conditional correlations (DCCs) to daily data of the Gulf Cooperation Council (GCC) markets (2013-2019). Our outcomes unveil that geopolitical risks positively affect the dependence between Islamic stocks and sukuk, as well as their conventional counterparts. Regional rather than global geopolitical risk factors positively affect the volatility of conventional stock-bond correlation, while the volatility of Islamic stock-sukuk correlation decreases during times of high geopolitical risks. Our findings offer several prominent implications in terms of financial stability, asset allocation of portfolios and policy design.

Strategic Asset Allocation and Inflation Resilience

Inflation risk poses a significant challenge to strategic asset allocators and is forcing many to reevaluate the suite of quantitative tools they use. In particular, standard simulation methods are inadequate for modeling inflation dynamics and do not generate uncertainty in long-term average inflation. Furthermore, despite low-frequency regime switching between negative and positive stock–bond correlation regimes, linked to inflation dynamics, standard methods do not incorporate this either. Finally, when markets are undergoing long-term structural changes, modeling choices should be able to integrate the forward-looking expectations of subject matter experts on joint economic and market dynamics. This article describes a simple way of retrofitting these features to an existing simulation engine.

A Changing Stock–Bond Correlation: Drivers and Implications

The relationship between stock and bond returns is a fundamental determinant of risk in traditional portfolios. For the first two decades of the 21st century, the stock–bond correlation was consistently negative and investors were largely able to rely on their bond investments for protection when equities sold off. But this was not the case in the previous century, and macroeconomic changes—such as higher inflation uncertainty—could lead to a reappearance of the positive stock–bond correlation of the 1970s, 80s, and 90s. This would have broad implications for investors, either increasing portfolio risk or forcing allocation changes likely to reduce expected returns. This article analyzes the implications for investors of a change in this “golden parameter” and presents a simple macroeconomic model to help understand its drivers, supported by international empirical evidence. Finally, it explores the role of alternatives in making up the potential diversification deficit in a positive stock–bond correlation world.

Empirical evidence on the stock-bond correlation

Empirical evidence on the stock-bond correlation

A Changing Stock-Bond Correlation: Explaining Short-term Fluctuations

A Changing Stock-Bond Correlation: Explaining Short-term Fluctuations

Sustainable Withdrawal Rates by Return Environment: A Time-Varying Bayesian Analysis

As investors depend more on their investment portfolios to generate income in retirement, financial advisors have developed rules of thumb to estimate a portfolio’s sustainable withdrawal rate (SWR). The most famous heuristic is Bengen’s “4% rule.” The strength of these rules is their ease of implementation. Among their limitations is their insensitivity to long-term variation in return environments. We borrow a statistical technique from the macroeconomics literature—a Bayesian vector autoregression with time-varying parameters—to model the impact of changing return dynamics on SWRs. We first illustrate how changes in stock-bond correlation, return and inflation volatilities, and returns have affected historical SWRs. Building on the consensus forecast, we then use these insights to identify 2.8% to 3.3% to be a relevant SWR for those retiring in today’s evironment characterized by low prospective returns and inflationary uncertainties.

You sneeze, and the markets are paranoid: the fear, uncertainty and distress sentiments impact of the COVID-19 pandemic on the stock–bond correlation

PurposeThis paper investigates the influence of three different sentiment indicators on the time-varying stock–bond correlation of 15 countries during the global crisis period of the coronavirus disease 2019 (COVID-19) pandemic.Design/methodology/approachThe author uses the time-varying correlation estimated using the autoregressive moving average -dynamic conditional correlation - generalised autoregressive conditional heteroskedasticity (ARMA-DCC-GARCH) model to achieve this aim. The impact of investor sentiment on the stock–bond correlation was analysed using the Markov regime-switching regression.FindingsThe study results show that the sentiment indicators of fear, uncertainty and distress have a pronounced negative impact on the stock–bond correlation. They further provide evidence of a strong regime effect on the stock–bond correlation with sentiment indicators.Practical implicationsThe paper has a relevant impact on policymakers and fund managers. First, the policymakers now have more insightful evidence of how the stock and bond markets react during crises. Second, the fund managers need to focus on behavioural variables as they may be driving factors in crisis periods that may impair portfolio management.Originality/valueTo the best of my knowledge, the paper is the first to throw light on the behaviour of the stock–bond correlation for 15 countries during the COVID-19 period.

Regime shifts in a long-run risks model of stock and treasury bond markets

PurposeThis paper aims to study the asset pricing implications for stock and bond markets in a long-run risks (LRR) model with regime shifts. This general equilibrium framework can not only generate sign-switching stock-bond correlations and bond risk premium, but also quantitatively reproduce various other salient empirical features in stock and bond markets, including time-varying equity and bond return premia, regime shifts in real and nominal yield curves, the violation of the expectations hypothesis of bond returns.Design/methodology/approachThe researchers study the joint determinants of stock and bond returns in a LRR model framework with regime shifts in consumption and inflation dynamics. In particular, the means, volatilities, and the correlation structure between consumption growth and inflation are regime-dependent.FindingsThe model shows that the term structure of interest rates and stock-bond correlation are intimately related to business cycles, while LRR play a more important role in accounting for high equity premium than do business cycle risks.Originality/valueThis paper studies the joint determinants of stock and bond returns in a Bansal and Yaron (2004) type of LRR framework. This rational expectations general equilibrium framework can (1) jointly match the dynamics of consumption, inflation and cash flow; (2) generate time-varying and sign-switching stock and bond correlations, as well as generating sign-switching bond risk premium; and (3) coherently explain another long list of salient empirical features in stock and bond markets, including time-varying equity and bond return premia, regime shifts in real and nominal yield curves, the violation of the expectations hypothesis of bond returns.

Extreme Inflation and Time-Varying Expected Consumption Growth

In a parsimonious regime switching model, we find strong evidence that expected consumption growth varies over time. Adding inflation as a second variable, we uncover two states in which expected consumption growth is low, one with high and one with negative expected inflation. Embedded in a general equilibrium asset pricing model with learning, these dynamics replicate the observed time variation in stock return volatilities and stock-bond return correlations. They also provide an alternative derivation for a measure of time-varying disaster risk suggested by Watcher [ Wachter J (2013 ) Can time-varying risk of rare disasters explain aggregate stock market volatility? J. Finance 68(3):987–1035]. implying that both the disaster and the long-run risk paradigm can be extended toward explaining movements in the stock-bond correlation. This paper was accepted by Kay Giesecke, finance. Funding: We gratefully acknowledge research and financial support from the Leibniz Center for Financial Research SAFE (formerly Research Center SAFE, funded by the State of Hessen initiative for research LOEWE). Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4451 .

Analyzing the EU sovereign debt crisis by a new asymmetric copula with reversible correlations

ABSTRACT It is well known that a correlation between stock and bond returns had changed in the financial crisis. This paper analyzes it using a novel asymmetric copula. The proposed test is constructed from bivariate split normal distribution and can change correlation signs of upper and lower tails of distribution independently. It is shown that the stock–bond correlation of the EU periphery countries in the capital outflow period is asymmetric, in that it is higher in the lower tail than in the upper tail. The lower tail correlation is higher when capital outflowed than in the calm period. The lower tail correlation sign in the inflow period was at odds; Ireland, Italy and Spain had negative lower tail correlation. The correlation reversion of these three countries can be explained by the change of capital movement from inflow to outflow. In contrast, Germany had negative stock–bond correlation before and after the crisis.

Time-varying risk of nominal bonds: How important are macroeconomic shocks?

I study the sufficiency of macroeconomic information to explain the time-variation in second moments of stock and bond returns, with a particular attention to stock-bond correlations. I propose an external habit model supplemented with realistic non-Gaussian fundamentals estimated solely from macroeconomic data. Intertemporal smoothing and precautionary savings effects – driven by consumption shocks – combine with a time-varying covariance between consumption and inflation to generate large positive and negative stock-bond return correlations. Macroeconomic shocks are most important in explaining second moments of stock and bond returns from the late 1970’s to mid-1990’s and during the Great Recession.

Forecasting US Equity and Bond Correlation—A Machine Learning Approach

The stock–bond correlation is a cornerstone of every asset allocation decision, but estimating it reliably can prove to be challenging given the potential for co-movements to fluctuate significantly based on economic conditions. Using supervised machine learning techniques, this article presents a new approach for identifying key determinants of the correlation between US equity and bond returns, ultimately finding that inflation, alongside real yields, equity volatility, economic growth, and inflation uncertainty, predict changes in correlation dynamics overtime. Relative to the existing literature, the authors’ approach allows for the systematic detection of the main drivers of stock–bond correlation and uncovers the time variation in importance of each determinant across economic regimes. Upon conducting an out-of-sample portfolio evaluation, the authors show that the five factors with gradient boosting regression approach outperforms all other existing factor-based models in estimating both the trend and level of correlation, thereby offering an alternative robust solution for forecasting time-varying equity and bond co-movements that can be further applied to asset allocation decisions and risk management.