My research agenda covers a wide range of topics in Finance such as Asset Pricing, Corporate Finance, Behavioral Finance, etc.
Publications
1. Borochin, P and Y. Zhao, 2019. Belief Heterogeneity in the Option Markets and The Cross-Section of Stock Returns. Journal of Banking and Finance,
2. Giaccotto, C, Giambona, E and Y. Zhao, 2020 Short-term and Long-term Discount Rates for Commercial Real Estate. Journal of Real Estate Finance and Economics.
3. Giaccotto, C, Lin, X, and Y. Zhao, 2020. Term Structure of Discount Rates for Firms in the Insurance Industry, Insurance: Mathematics and Economics. 95, Pages 147-158
4. Borochin, P., Wu, Z., and Y. Zhao, 2021 The Effect of Option-implied Skewness on Delta- and Vega-Hedged Option Returns, Journal of International Financial Markets, Institutions & Money, 74, 101408
5. Borochin, P, and Y. Zhao, 2022 Risk Neutral Skewness Predicts Price Rebounds and so can Improve Momentum Performance, Critical Financial Review.
Data available: Risk Neutral Skewness Factor-Mimicking Portfolio
Working Papers
6. Paul Borochin and Yanhui Zhao, The Economic Value of Equity Implied Volatility Forecasting with Machine Learning
OptionMetrics Annual Meeting 2019
Financial Management Association Annual Meeting 2020 (Best paper award: options & derivatives)
Under review at Review of Financial Studies
Abstract: We evaluate the importance of nonlinear interactions in volatility forecasting by comparing the predictive power of decision tree ensemble models relative to classical ones for normalized at-the-money implied volatility innovations. We measure the economic significance of these predictions in cross-sectional and time series pricing tests of delta-hedged option returns. Classification tree ensembles outperform a multinomial logit classifier by 0.35% to 0.46% monthly abnormal returns in delta-hedged option portfolio sorts on volatility innovation forecast data, while regression tree ensembles outperform OLS and LASSO models by 0.03% to 0.14%. Since the predictive variables are the same across all models, these performance differences likely capture the value of nonlinear interactions in implied volatility forecasts. Our results are robust to look-ahead bias and model over-fitting.