Stock-Bond Correlation, an In-Depth Look

Quantpedia

Contributor:
Quantpedia
Visit: Quantpedia

The recent surge in global inflation sent shock waves across financial markets and affected the complicated relationship between stocks and bonds. Today, we would like to present you with a review of two interesting papers, which provide both a deep and easy-to-understand examination of the correlation structure of those two main asset classes. The first paper reviews specifics in various parts of the world, and the second one summarizes known information about the macroeconomic drivers of the US stock-bond correlation.

Stock-Bond Correlation: A Global Perspective

The first paper reiterates the crucial statement that stock-bond correlation is considered one of the most, if not the most important input for multi-asset portfolio construction. Negative stock-bond correlation, which is present in Developed Markets (DMs), provides an implicit hedge of one asset to the other, dampening overall portfolio risk. Noah Weisberger and Xiang Xu (June 2021) investigate the specifics of these effects in other parts of the world, especially Emerging Markets (EMs). In the case of a local stock-bond correlation regime change from negative to positive, there is a need to alter the expected risk-reward characteristics of a portfolio of local assets and change the parameters of hedging. They advise CIOs and asset allocators to look through the findings thoughtfully, which can provide significant inputs and possible ideas for models that can help to assess possible global risk.

Looking across six DMs (Australia, Canada, Germany, Japan, the UK, and the US), the authors observed that changes in local currency stock-bond correlations are synchronized (Figure 1). Indeed, the correlation of DM local currency stock-bond correlations with US stock-bond correlation is different and ranges from a high of 0.92 for Canada to 0.58 for Japan (Figure 2). They found the most interesting and useful results from using Model 3, which combines both sources of variation in local stock-bond correlation and apportions explanatory power to US variables (as the global proxy) and to local effects (net of US influence). Its R² is in Figure 9. Beyond DM sovereign fixed income, when the US stock-bond correlation is positive, Oil, Gold, and Energy total returns are negatively correlated with US stock returns. Figure 13 summarizes this finding: negative correlations are, however, relatively small, and hedging comes at the cost of higher volatility.

Unlike DMs, where stock-bond correlation has been persistently negative for the last 20 years, EM (local currency) stock-bond correlations have been mostly positive since 2000 but not uniformly so, as can be seen from Figure 14. In Figure 17, we can see EM (hedged USD) bond returns have generally been positively correlated with US stock returns, even when the US stock-bond correlation was negative.

US Stock-Bond Correlation: What are the Macroeconomic Drivers?

Building on previous themes, Junying Shen and Noah Weisberger (June 2021) 2nd paper provided interesting perspectives into various important macroeconomic conditions which drive both negative and positive correlations, such as monetary and fiscal policy. US stock-bond correlation, which plays an important role in institutional portfolio construction, has been persistently negative for the last 20 years. This negative correlation allows stocks and bonds to serve as a hedge for each other, enabling CIOs to increase stock allocations while still satisfying a portfolio risk budget. However, the stock-bond correlation is not immutable. In fact, it was consistently positive for more than 30 years prior to 2000. A return to positively correlated stock and bond returns may require hedge fund managers to rethink their asset allocation.

Authors estimated stock (S&P 500) – bond (10y Treasury) correlation using monthly total returns over a centered, rolling 5y window from 1950 to 2020 and compiled nice Figure 3. Portfolio returns vary little as correlation change, as shown in Figure 4, but it discusses other important aspects of CIOs’ portfolio volatility and Sharpe ratios which needs to be managed as well. The fact that the correlation of overlapping long-term returns will absorb and amplify (i.e., by increasing the number of observations where the two asset returns are of opposite sign) the one-off disconnect and will remain negative for a protracted period can be seen from Figure A4. Figures A7 and A8 confirm that short-term return and long-term return regimes are consistent, and negative correlation regimes are not just due to “risk-off” dynamics.

Conclusion

Institutional investors whose work is to form well-suited portfolios need to choose the right asset allocation to achieve firm returns for their clients. The analyzed articles provide help asset managers and CIOs evaluate the potential for a change in both US and global stock-bond correlation, identify the underlying macroeconomic components of the correlation, and show how changes in these components have been linked to changes in monetary and fiscal policy over the last 70 years. It is important to realize that neither theory nor history points to a single factor that determines the correlation regime, and there are many, sometimes even contradictory, indicators that signal regime change. Persistently continuing falling and low-interest rates we have been used to in recent years alone may not be enough to support a negative correlation. Today’s challenging environment with the unprecedented rise of inflation competes with different aspects and characteristics of widening ranges in different parts of both DM and EM, and there is a need to be aware of possible implications.

Visit Quantpedia for additional insight on this topic: https://quantpedia.com/stock-bond-correlation-an-in-depth-look/.

Disclosure: Interactive Brokers

Information posted on IBKR Traders’ Insight that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Traders’ Insight are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.

This material is from Quantpedia and is being posted with permission from Quantpedia. The views expressed in this material are solely those of the author and/or Quantpedia and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

In accordance with EU regulation: The statements in this document shall not be considered as an objective or independent explanation of the matters. Please note that this document (a) has not been prepared in accordance with legal requirements designed to promote the independence of investment research, and (b) is not subject to any prohibition on dealing ahead of the dissemination or publication of investment research.

Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.