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Education 

University of Amsterdam and Tinbergen Institute, Amsterdam, The Netherlands.

Ph.D. candidate in Econometrics, expected March 2015.

Supervisors: H. Peter Boswijk and Roger J.A. Laeven.

M.phil. in Economics, 2011.

Peking University, Beijing, China.

B.A. in Economics, 2009.

B.Sc. in Mathematics and Applied Mathematics, 2009.

 

Research Interest

Financial Econometrics, Nonparametric Statistics, Asset Pricing and Empirical Finance.

 

Teaching Experience

Academic year  2013-1014

Mathematical and Empirical Finance, June 2014. 

Econometrics, Fall 2013.  

Academic year  2012-1013 

Mathematical and Empirical Finance, June 2013. 

Econometrics, Fall 2012.  

Academic year  2012-1013 

Mathematical and Empirical Finance, June 2013.  

Applied Econometrics, Fall 2011. 

 

Conference Presentation

Econometric Society European Meeting, Toulouse, France, August 25-29, 2014.

Econometric Society Australasian Meeting, Hobart, Australia, July 1-4, 2014.

China Meeting of the Econometric Society, Xiamen, China, June 15-27, 2014.

North American Summer Meeting of the ES, Minneapolis, MN, USA, June 19-22, 2014.

QED Annual PhD Conference, May 2-4 2014, Bielefeld, Germany.

Sixth Annual SoFiE Conference, Singapore, June 12-14 2013.

 

 

 

 

Research Papers

 

"Specification Test for Option Pricing Models with a Delta-Hedging Interpretation," Job Market Paper.

• Finance theory sets constraints on option pricing models. Yet empirical option pricing models either implicitly ignore some theoretical constraints or impose possibly misspecified parametric structure on them. Previous specification tests of such parametric structure often require the solution of certain density function which is difficult to obtain in close-form. In contrast, this paper proposes a new specification test without such requirement. I show how to derive a testing equation from the nonparametrically constructed theoretical constraints. Next, I provide a delta-hedging interpretation of this testing equation and show that when the option pricing model is correctly specified, the hedging error estimated from times series data should be consistent with the risk premia obtained from cross-sectional options data. I then investigate the statistical properties of the components of my testing equation and discuss how to adapt the testing procedure to specific parametric assumptions. A Monte Carlo example shows that the test has reasonable size and power. I also provide other empirical implications of the test statistics.

 

"Time-Varying Risk Premia in Options: Theoretical Analysis and Empirical Evidence."

• Risk premium is central to both financial theory and its empirical application, yet the standard approach for estimating different risk premia from options data restricts them to be either time-invariant or proportional to a volatility-based risk factor. In contrast, this paper extends the Fama-MacBeth regression to option pricing model and proposes a new method for estimating time-varying risk premia without such restrictions. To do so, I take two key steps. First, I define general forms of risk premia in an incomplete market with jumps risks and un-traded state factors. Second, I derive an equation that relates risk premia to option prices with clear economic interpretation. The generic risk-premia-based option pricing equation reveals that the sensitivity coefficients, or betas, of option prices with respect to risk premia can be either estimated from high frequency data or from any calibrated option pricing model. Then at each time point, the risk premia is determined by regressing all options prices against these estimated betas. Empirical study shows that the time-varying patterns of various risk premia help understand the market valuations of risks at different time periods.

 

"Testing for Self-Excitation in Jumps", joint with H. Peter Boswijk and Roger J.A. Laeven, 2014.

• This paper extends the notion of self-excitation in jumps to very broad families of continuous-time models, proposes statistical tests to detect its presence in a discretely observed path at high frequency, and derives the tests’ asymptotic properties. Our statistical setting is semiparametric: except for necessary parametric assumptions on the jump size measure, the other components of our model are essentially unrestricted. We design tests under two (complementary) null hypotheses to control both type I and type II errors. We analyze the finite sample performance of our tests in Monte Carlo simulations. When applied to high frequency asset price data, our empirical findings support the existence of self-excitation in asset price jumps.

 

"Estimation of the Continuous and Discontinuous Leverage Effects", joint with Yacine Aït-Sahalia, Jianqing Fan, Roger J.A. Laeven and Dan Christina Wang, submitted, Oct. 2014.

• This paper examines the leverage effect, or the generally negative covariation between asset returns and their changes in volatility, under a general setup that allows the log-price and volatility processes to be Itô semimartingales. We decompose the leverage effect into continuous and discontinuous parts and develop statistical methods to estimate them. We establish the asymptotic properties of these estimators. We also extend our methods and results to the situation where there is market microstructure noise in the observed returns. We show in Monte Carlo simulations that our estimators have good finite sample performance. When applying our methods to real data, our empirical results provide convincing evidence of the presence of the two leverage effects, especially the discontinuous one.

 

Research in Progress

 

"Dynamics Optimization of Stochastic Differential Utility with Jumps and Ambiguity."

"Flights to quality, flights to safety, market linkages and jump excitation dynamics", joint with Mardi Dungey, Deniz Erdemlioglu, Marius Matei.

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