**7. Conclusions**

This paper examines the impact of financial market risk and policy uncertainties on the correlation between stock and bond returns. Analyzing the financial data of US markets for the period January 1990–June 2019, I derive several important empirical conclusions. First, empirical estimations based on the asymmetric dynamic correlation model (ADCC) sugges<sup>t</sup> that stock–bond correlations are time-varying and display a negative relation overtime, especially for the period before 2002.

Second, evidence confirms that the stock–bond relationship is negatively correlated with the implied volatility in stock market (VIX), suggesting a higher market risk would cause a "flight-to-safety." This phenomenon appears in stock markets of TTMK and VALUE. However, for indices such as the DJIA (with two-year and five-year bonds), the NASDAQ and RUSSELL, the sign turns out to be positive. The mixed results reflect differing attitudes toward risks of investors who hold different stock portfolios. This finding suggests that the use of a single stock index to measure stock returns and one specific

of using governmen<sup>t</sup> bonds, one might use junk bonds to trace the dynamic correlations (Glassman 2018), which will be the subject of future research.

form of bond maturity (10-year bond) as was done in the previous research (Connolly et al. 2005) could produce a biased estimator and hence a misleading statistical inference.

Third, with respect to the performance of implied bond volatility (*MOVEt*−1), this study arrives at more consistent results. In this case, the coe fficients tend to have negative signs with a couple of exceptions. For this reason, we can reach a more concrete conclusion that a rise in *MOVEt*−<sup>1</sup> leads to a decoupling of stock and bond returns. Thus, *MOVEt*−<sup>1</sup> to some extent reflects di fferent market information and complementarily contributes to explaining movements in stock and bond return correlations.

Fourth, this study find evidence that estimated coe fficient of the *EPUt*−<sup>1</sup> has a positive and significant e ffect on the stock–bond return correlation. This result is consistent with a dominant income effect resulting from a rise in economic policy uncertainty that impedes economic activities and leads to a decline in income. This decline brings about a decrease in liquidity and in turn weakens demand for both stocks and bonds. Therefore, both stock and bond prices move in the same direction.

Fifth, testing results conclude that the estimated coe fficients for both *FPUt*−<sup>1</sup> and *MPUt*−<sup>1</sup> are negative and highly significant. The negative sign of this policy uncertainty is mainly due to the dominance of the substitution e ffect, which prompts investors to replace higher uncertainty assets with lower uncertainty assets due to an upward shift in policy uncertainty. This occurs because of a policy stance that causes a sudden rise in *MPUt*−<sup>1</sup> (or *FPUt*−<sup>1</sup> in bond financing) and increases uncertainty in interest rates, prompting a sello ff in stocks and a flight-to-quality phenomenon. Note that this market action essentially stems from a heightened fear from policy uncertainty rather than something of inherent in the asset's return. It is possible that a rise in *MPUt*−<sup>1</sup> (or *FPUt*−1) could threaten the future cash flow and reduce the demand for both stocks and bonds. However, the evidence of a negative coe fficient indicates the dominant force of the substitution e ffect. An implication of a negative coe fficient suggests that a portfolio can benefit from a combination of stocks and bonds as a way of diversification and hedging against monetary policy or fiscal policy uncertainty.

Sixth, by testing the total policy uncertainty on the dynamic correlations between stock–bond returns, the evidence turns out to display mixed signs. For DJIA and VALUE stocks, the correlation coe fficients present positive signs, indicating the dominance of an income e ffect attributable to general economic policy uncertainty; however, correlations of the TTMK, NASDAQ, and RUSSELL stocks with bond returns display negative signs, suggesting the dominance of a substitution e ffect, resulting from the reallocation of assets from ones with higher uncertainty to those with lower uncertainty in response to a rise in total policy uncertainty.

In sum, this paper provides significant empirical evidence to support the impact of *MPUt*−<sup>1</sup> and *FPUt*−<sup>1</sup> on stock–bond correlations. In addition to the *VIXt*−1, *MOVEt*−<sup>1</sup> and *EPUt*−1, our Chi-squared statistics consistently sugges<sup>t</sup> the rejection of the null, *MPUt*−<sup>1</sup> = *FPUt*−<sup>1</sup> = 0, and support the incremental significance of *MPUt*−<sup>1</sup> and *FPUt*−<sup>1</sup> in the test equation. This evidence has not previously been shown in the literature to explain the stock–bond return correlation.

Further, this study has practical implications for investment firms by tracing the time-varying correlations and is distinct from more commonly taken approaches by calculating the individual, constant correlations (Forbes and Rigobon 2002) within a given period of time. This study identifies categorical policy uncertainties as factors to explain the change in stock–bond correlations over time. Given the model parameters, firms can access information via newspapers to make projections related to stock–bond dynamics.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The author declares no conflict of interest.
