This paper proposes a novel time-varying present-value model to analyze the joint dynamics of stock returns and cash flows periodically. We use a nonparametric time-varying vector autoregressive model to examine the economic implications of the time-varying present-value model. By conducting...

In organized energy markets that use locational pricing, power generators and energy suppliers procure Financial Transmission Rights (FTRs) to hedge against grid congestion charges, while third party speculators attempt to capture a return with these extremely volatile contracts. The paper...

We provide a theoretical basis for understanding the properties of compound returns. At long horizons, multiplicative compounding induces extreme positive skewness into individual stock returns, an effect primarily driven by single-period volatility. As a consequence, most individual stocks...

Following recent advances in the non-parametric realized volatility approach, we separately measure the discontinuous jump part of the quadratic variation process for individual stocks and incorporate it into heterogeneous autoregressive volatility models. We analyze the distributional...

This paper examines inflation and the role of inflation-hedging assets in an institutional portfolio. Even in the current environment of low realized inflation, inflation risk remains, in the form of inflation surprises, which have historically been damaging for equity and bond portfolios....

This paper finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a...

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based upon observable characteristics, using machine learning principles with linear models. The best performing methods are flexible two-stage...

Stock-bond correlation is considered an important input for multi-asset portfolio construction. While there has been much research on US stockbond correlation, less work has focused on stock-bond correlations in other countries, their relationship to each other, and their common macroeconomic...

With approximately 900 million observations we conduct, to our knowledge, the largest study ever of intraday stock return predictability using machine learning techniques finding consistent out-of-sample predictability across market, sector, and individual stock returns at various time horizons....

In this paper we document that realized variation measures constructed from highfrequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even...