Yan Xu
Prof. Yan XU
Associate Professor

2859 7037

KK 929

Academic & Professional Qualification
  • Ph.D., University of South Carolina

Dr. Yan XU received his Ph.D. from the University of South Carolina in 2007.  Yan joined The University of Hong Kong (HKU) as an Associate Professor of Finance in 2013.  Prior to joining HKU, Yan had worked as a quantitative research analyst in State Street Global Advisors and had been an Assistant Professor of Finance in the University of Rhode Island from 2008 to 2013.

Yan’s main research interests are in international financial markets, empirical asset pricing, applied time-series analysis, financial development and economic growth.  His research was published in academic journals such as Journal of Financial Economics, Review of Financial Studies, and Journal of Financial and Quantitative Analysis

Currently Yan teaches Derivatives and International Financial Management at the undergraduate level.  For more details of his research and teaching, please visit webpage: http://www.hkubs.hku.hk/~xuyan/

Research Interest
  • International Financial Markets
  • Empirical Asset Pricing
  • Applied Time-Series Analysis
  • Financial Development and Economic Growth
Selected Publications
  • “Mispricing and Risk Premia in Currency Markets,”
    (with Söhnke M. Bartram, Leslie Djuranovik, and Anthony Garratt), Journal of Financial and Quantitative Analysis, forthcoming.
  • “Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data,”
    (with Xin Chen, Dan Yang, Yin Xia, Dong Wang, and Haipeng Shen), Journal of Econometrics, 232(2), February 2023, pp. 544-564.
  • “Corporate R&D and Stock Returns: International Evidence,”
    (with Kewei Hou, Po-Hsuan Hsu, Shiheng Wang, and Akiko Watanabe), Journal of Financial and Quantitative Analysis, 57, June 2022, pp. 1377-1408.
  • “What Affects Innovation More: Policy or Policy Uncertainty?”
    (with Utpal Bhattacharya, Po-Hsuan Hsu, and Xuan Tian), Journal of Financial and Quantitative Analysis, 52, Oct 2017, pp. 1869-1901.
  • “Improving Mean Variance Optimization through Sparse Hedging Restrictions,”
    (with Shingo Goto), Journal of Financial and Quantitative Analysis, 50, Dec 2015, pp. 1415-1441.
  • “Financial Development and Innovation: Cross-Country Evidence,”
    (with Po-Hsuan Hsu and Xuan Tian), Journal of Financial Economics, 112, April 2014, pp. 116-135.
  • “The Asset Growth Effect: Insights from International Equity Markets,”
    (with Akiko Watanabe, Tong Yao, and Tong Yu), Journal of Financial Economics, 108, May 2013, pp. 529-563.
  • “Strategic Disclosure and Stock Returns: Theory and Evidence from US Cross-Listing,”
    (with Shingo Goto and Masahiro Watanabe), Review of Financial Studies, 22, April 2009, pp. 1585-1620.
Recent Publications
Testing and Support Recovery of Correlation Structures for Matrix-valued Observations With an Application to Stock Market Data

Estimation of the covariance matrix of asset returns is crucial to portfolio construction. As suggested by economic theories, the correlation structure among assets differs between emerging markets and developed countries. It is therefore imperative to make rigorous statistical inference on correlation matrix equality between the two groups of countries. However, if the traditional vector-valued approach is undertaken, such inference is either infeasible due to limited number of countries comparing to the relatively abundant assets, or invalid due to the violations of temporal independence assumption. This highlights the necessity of treating the observations as matrix-valued rather than vector-valued. With matrix-valued observations, our problem of interest can be formulated as statistical inference on covariance structures under sub-Gaussian distributions, i.e., testing non-correlation and correlation equality, as well as the corresponding support estimations. We develop procedures that are asymptotically optimal under some regularity conditions. Simulation results demonstrate the computational and statistical advantages of our procedures over certain existing state-of-the-art methods for both normal and non-normal distributions. Application of our procedures to stock market data reveals interesting patterns and validates several economic propositions via rigorous statistical testing.

Corporate R&D and Stock Returns: International Evidence

Firms with higher R&D intensity subsequently experience higher stock returns in international stock markets, highlighting the role of intangible investments in international asset pricing. The R&D effect is stronger in countries where growth option risk is more likely priced, but is unrelated to country characteristics representing market sentiments and limits-of-arbitrage. Moreover, we find that R&D intensity is associated with higher future operating performance, return volatility, and default likelihood. Our evidence suggests that the cross sectional relation between R&D intensity and stock returns is more likely attributable to risk premium than to mispricing.