Augustin P., F. Saleh, and H. Xu (2020), CDS Returns, Journal of Economic Dynamics and Control, 118
We show that existing metrics of CDS returns poorly approximate cash flow-based CDS returns. Given the complexities involved in computing CDS returns correctly, we provide a simple closed-form approximation that bears a correlation of no less than 99% with the true return series. Our work emphasizes the importance of distinguishing between changes in credit spreads and CDS returns. In addition, it highlights the need to rely on true CDS return metrics to evaluate investment strategies and predictive return regressions that involve the selling or buying of CDS contracts.
“The Integration Between Option and CDS Markets” – Job Market Paper
An increasing number of studies extract credit spreads using equity options data. This inference relies on the assumption that option and credit markets are integrated. I empirically test this assumption using firm level option implied credit spreads (IS) and CDS spreads. While the IS and CDS spreads are cointegrated, suggesting that they converge to an equilibrium relation in the long run, I find significant short-lived price discrepancies. These price discrepancies are closely associated with variables related to limits to arbitrage, such as illiquidity, idiosyncratic risk, institutional ownership, and analyst coverage, as well as the health of financial intermediaries. I provide a stylized asset pricing framework with an intermediary constraint, which can rationalize the salient features of the empirical evidence.
“Why does the CDS Term Structure Predict Equity Returns” with Patrick Augustin and Jan Ericsson
(draft available upon request)
Work in Progress
“Multi-currency Corporate Credit Default Swap Spread Differences”
Pre-Ph.D. Publications (Chinese)
Haohua Xu1, Haifeng Gu2, Stock Index Futures Short-term Prediction Modelling Based on Difference BP Neural Network, Finance Teaching and Research, 2014, 03:27-32.
This paper divides a short-term price prediction based on 2 steps. The first step predicts the magnitude of the price variation via machine learning. The second step predicts the direction of the price movement by examining the dominant curve topological structures of the time series. This paper offers an empirical analysis of Chinese stock index futures market and proves the accuracy of this method.
Haohua Xu1, Haifeng Gu2, Study on Stock Index Futures Long-term Price from High to Low Point Prediction Model Based on Grey Correlation – Empirical Analysis from HS300 Stock Index Futures, Finance Teaching and Research, 2014, 05:53-58+76.
This paper provides an innovative indicator for long term investment in the Chinese stock index futures market using Grey Method. This indicator reflects the peaks and troughs of the futures price time series. An empirical analysis of Chinese HS300 SPIF market demonstrates the usefulness of the indicator.