Dr. Wing Hong Chan
Associate Professor (Economics)
Contact InformationEmail: firstname.lastname@example.org
Phone: 519.884.0710 ext.3650
Office Location: P3042
Office Hours: Fall 2014: Tuesday/Thursday, 11:30am-1:00pm
Academic BackgroundBA (Hons) (University of Manitoba), MA (University of Alberta), PhD (University of Alberta)
Wing Chan obtained his MA and PhD in Economics from the University of Alberta (1996, 2002). Prof. Chan's main research interest is in financial econometrics. This includes research on ARCH/GARCH, stochastic volatility, and jumps for applications in asset pricing, hedging, and option trading.
Econometrics, Derivatives, Risk Management, Asset Pricing Models
GRANTS AND AWARDS
- Research Prize for Top Ph.D. Paper Award at the Midwest Finance Association Meeting (2009) “Extreme News Events, Long-Memory Volatility and Time Varying Risk Premia", with LiLing Feng.
- Research Prize for Best Paper on Derivatives at the Northern Finance Association Meeting (2004) “The Economic Value of Trading with Realized Volatility in the S&P 500 Index Options Market,” with Ranjini Jha and Madhu Kalimipalli.
- RGC Competitive Earmarked Research Grant (CERG), Hong Kong University Grant Committee (UGC) "Does a financial crisis change relationship? The case of a housing market" (2007-2009) HKD$449,000 (with Charles Leung)
- Standard Research Grant, Social Sciences and Humanities Research Council of Canada SSHRC, (2004-2007), “Extreme Events in Financial Markets,” CAD$33,826.
· Chong, T, C. Lu, and W. H. Chan (2012) “Long-Range Dependence in the International Diamond Market,” Economics Letters, 116 (3), 401-403. .
· Chan, W. H., and L. Feng (2012) “Time Varying Jump Risk Premia in Stock Index Futures Returns,” Journal of Futures Markets, 32(7), 639-659.
· Chan, W. H., X. Cheng, and J. Fung (2010), “Forecasting Volatility: Role of High Frequency Data and Forecasting Horizon”, Journal of Futures Markets, 30(12), 1167-1191.
· Chan, W. H., G. Wang, and L. Yang (2010), “Weather, Inventory, and Common Jump Dynamics in Natural Gas Spot and Futures Markets”, Review of Futures Markets, 18 (4), 363-384.
· Chan, W. H., (2010), “Optimal Hedge Ratios in the Presence of Jumps”, Journal of Futures Markets, 30 (8), 801-80.
· Chan, W. H. and D. Young, (2009), “Conditional Jump Dynamics for Copper Prices”, Review of Futures Markets, 18 (1) ,75-85.
· Chan, W. H., R. Jha and M. Kalimipalli (2009), “The Economic Value of Using Realized Volatility in Forecasting Future Implied Volatility," Journal of Financial Research, 32 (3) 231-259.
· Chan, W. H. (2008) “Dynamic Hedging with Currency Futures in the Presence of Jumps”, Studies in Nonlinear Dynamics & Econometrics, 12 (2) Article 4.
· Fung, J., R. Webb, and W. H. Chan (2008), "Do Derivative Markets Contain Useful Information for Signaling "Hot Money" Flows?" Hong Kong Institute for Monetary Research, Research Report.
· Chan, W. H. and D. Young, (2006), “Jumping Hedges: An Examination of Movements in Copper Spot and Futures Markets”, Journal of Futures Markets, 26 (2) pp.169-188.
· Chan, W. H. and D. Rich, (2006), “Occupational Labor Demand and the Sources of Nonneutral Technical Change”, Oxford Bulletin of Economics& Statistics,68 (1) pp.23-43.
· McMillan, M. and W. H. Chan, (2006), “Comparing University Efficiency Using Stochastic and Non-Stochastic Methods: The Case of Canadian Universities”, Education Economics, 14 (1) pp. 1-30.
· Boxall, P., W. H. Chan, and M. McMillan, (2005), “The Impact of Oil and Natural Gas Facilities on Rural Residential Property Values”, Resources & Energy Economics, 27, pp.248-269.
· Chan, W. H., (2004), “Conditional Correlated Jump Dynamics in Foreign Exchange”, Economics Letters, 83, pp.23-28.
· Chan, W. H., (2003), “A Correlated Bivariate Poisson Jump Model for Foreign Exchange”, Empirical Economics, 28, pp.669-689. (Matlab Program)
· Chan, W. H. and J. M. Maheu, (2002), “Conditional Jump Dynamics in Stock Market Returns”, Journal of Business & Economic Statistics, 20 (3), pp.377-389. (Program provided by RATS)
· Buse, A. and W. H. Chan, (2000), “Invariance, Price Indices, and Estimation in Demand Analysis”, Empirical Economics, 25, pp.519-539.