Title: | Parallel-in-Time Iterative Methods for Pricing American Options |
Speaker: | Prof. Jun Liu, Department of Mathematics and Statistics, Southern Illinois University Edwardsville |
Time/Place: | 09:30 - 10:30 Zoom, Meeting ID: 977 5587 0594 |
Abstract: | In finance, American options allow holders to exercise the option rights at any time before and including the day of expiration. For pricing such American options by PDE models, a sequence of linear complementarity problems (LCPs) need to be solved at each time step sequentially. We can reformulate LCPs as HJB equations, which can be then solved by the popular policy iteration. We propose to solve an “all-at-once” form of HJB equations simultaneously by the policy iteration, which can be accelerated by our designed parallel-in-time (PinT) preconditioners. Numerical examples are presented to confirm the effectiveness of our proposed methods. |
Title: | Nonstandard inference for mortality models and momentum trade |
Speaker: | Prof Liang Peng, Georgia State University |
Time/Place: | 16:00 - 17:00 FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University |
Abstract: | Mortality models have been critical in pricing life insurance products, and trading momentum is popular in finance. The employed mortality models in the literature of actuarial science involve unobserved mortality indexes and use estimated mortality indexes to fit a time series model for forecasting mortality risk and hedging longevity risk. The estimated mortality index approximates the random mortality index with measurement error. A recent study of trading momentum involves measurement errors for a time series model too. Standard statistical methods without taking the measurement errors into account often lead to biased inferences. This talk will discuss some nonstandard inferences in these two situations. |
Title: | Probabilistic Forecasting for Daily Electricity Loads |
Speaker: | Professor Qiwei Yao, London School of Economics and Political Science |
Time/Place: | 15:00 - 16:00 RRS905, Sir Run Run Shaw Building, HSH Campus, Hong Kong Baptist University |
Abstract: | Probabilistic forecasting of electricity daily loads is of fundamental importance for effective scheduling and decision making in the increasingly volatile and competitive energy markets. A critical challenge is to deal with the nonstationarity at daily, weekly and monthly levels. Working with EDF (Electricity of France), we recast the problem as a curve-to-curve regression. The newly proposed probabilistic predictors for curves outperform several state-of-the-art predictive methods in terms of forecasting accuracy, coverage rate and average length of the predictive bands. The predictive quantile curves provide some risky scenarios which are important for the electricity supply risk management. |
We organize conferences and workshops every year. Hope we can see you in future.
Learn MoreProf. M. Cheng, Dr. Y. S. Hon, Dr. K. F. Lam, Prof. L. Ling, Dr. T. Tong and Prof. L. Zhu have been awarded research grants by Hong Kong Research Grant Council (RGC) — congratulations!
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