Welcome! I am an Assistant Professor at Nankai University. I obtained my PhD in Financial Econometrics from the University of Amsterdam in Novemebr 2021.
My research interests are in Financial Economeitrics, High-Dimensional Time Series Analysis, Term Structure Modeling, the Macro-Finance Interaction, Empirical Asset Pricing, and Machine Learning in Economics and Finance.
Modeling and forecasting serially dependent yield curves (Job Market Paper)
Econometric Society Winter Meeting 2020, ASSA 2021 meeting AEA poster session, 2020 European Conferences of the Econom[etr]ics Community poster session, 2021 Asian Meeting of the Econometric Society, 2021 China Meeting of the Econometric Society
Considering that yield curves usually are serially dependent, this paper proposes a new method to estimate and forecast yield curves based on factors driving serial dependence of yield curves. Gathering information at different lags of yield curves, the dimensionality and the lag order of yield curves are jointly determined. Applying this method to monthly U.S. government bond yields from January 1985 through December 2020, I find that the dynamic structure of yield curves reduces to a vector process lying in a 3-dimensional space, with 1-month lag information. Yield curve residuals from this new model over time exhibit zero mean and less autocorrelation. Moreover, this new model provides favorable in-sample fit and out-of-sample forecasting performance.
JEL Classification: C1, C51, E4, G1
Keywords: Yield curve; Dimension reduction; Functional principal component analysis; Factor model; Term structure
Predicting Intraday Return Patterns based on Overnight Returns for the US Stock Market, with Cees Diks and Valentyn Panchenko
CFECMStatistics 2019, CEF 2018
This paper investigates predicting intraday return patterns conditional on observed overnight returns. Based on Trade and Quote data, we find evidence for dependence between overnight returns and subsequent intraday first and last half-hour return patterns for the S&P 500 Exchange Traded Fund for the time period from 2003 to 2013 with both statistical and economic significance. Our methodology allows studying the return patterns documented in the existing theoretical and empirical literature in more detail. Moreover, we find that both the first and the last half hours offer trading opportunities for day traders. Specifically, 20-minute after the market opens and the last 30-minute before the market closes seem to be the best holding periods for investors in terms of annualized returns, Sharpe ratios, and Certainty Equivalent Returns .
JEL Classification:C14, C22, C53
Keywords: Overnight returns; Intraday returns; High-frequency trading; Nonlinear dependence; Day trading
Market Intraday Trend Reversal, with Stijn Groot
We provide a new factor that captures intraday trend based on intraday half hour prices. Using high frequency S&P 500 exchange-traded fund (ETF) data from 1998 to 2020, we find that this intraday trend factor significantly predicts the next day’s last half hour return, with a highly significant negative coefficient. We name this predictability as intraday trend reversal. This intraday trend reversal also shows in 15 other most actively-traded ETFs for domestic and international markets and is stronger on more volatile days. Portfolio constructed to exploit this intraday trend reversal substantially outperforms the benchmark portfolios based on the market intraday momentum and the historical mean forecasts during the recent COVID-19 crisis period. This novel empirical evidence on timeseries stock market reversals is consistent with Johnson’s (2016) model of peer effects and heterogeneous beliefs of investors.
JEL Classification:G2, G12, G14, G17
Keywords: Return predictability; Predictive regression; High-frequency trading; Trend reversal