High‐frequency data and stock–bond investing

Understanding the comovements between stock and bond returns is crucial in asset allocation. This paper employs a new class of multivariate covariance models with realized covariance measures for modeling the joint distribution of returns. Estimation results indicate that high‐frequency data not only enhance explanatory power but also sort out the features of heteroskedasticity with a short response time and short‐run momentum effects in describing the covariance dynamics. To confirm the efficiency of the models in covariance predictions, multiperiod volatility‐timing strategies are implemented to evaluate the benefits of incorporating the distinguishing features into the modeling. Out‐of‐sample forecasting results indicate that the models outperform conventional models in finding optimal portfolio shares and thus considerably reduce the conditional volatility in a portfolio. Consequently, investors with high risk aversions are willing to pay pronounced performance fees to obtain the economic value of volatility timing.