Year | Month |
2024 | Jan Feb Mar May |
2023 | Jan Feb Mar Apr May Jun Jul Aug Oct Nov Dec |
2022 | Jan Feb Jun Jul Aug Oct Nov Dec |
2021 | Jul Aug Sep Oct Nov |
Title: | Nonstandard inference for mortality models and momentum trade |
Speaker: | Prof Liang Peng, Georgia State University |
Time/Place: | 16:00:00 - 17:00:00 FSC1217 |
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: | Parallel-in-Time Iterative Methods for Pricing American Options |
Speaker: | Prof. Jun Liu, Department of Mathematics and Statistics Southern Illinois University Edwardsville |
Time/Place: | 09:30:00 - 10:30:00 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: | A fast algorithm for the quadratic optimization problem with inequality quadratic constraints |
Speaker: | Dr. LI Siqing, College of Mathematics Taiyuan University of Technology |
Time/Place: | 15:00:00 - 16:00:00 Subspace (FSC1110) |
Abstract: | The least-squares quadratic optimization problems with quadratic inequality constraints (LSQI) are applicable in various research fields, including the inverse problem and the wave equations with energy preservation. In this talk, we introduce a fast algorithm for solving this kind of LSQI problem. The proposed algorithm begins by simplifying the original LSQI problem through the the Generalized Singular Value Decomposition (GSVD) of matrices in the objective function and inequality constraint function. This step helps to transform the problem into an equivalent LSQI problem which is easier to solve. Next, the Lagrange multiplier and Lagrange function are introduced to formulate the optimization problem which incorporate the inequality constraints into the objective function. The proposed algorithm computes the Lagrange parameter by solving a scalar secular equation using Newton iteration with a Hebden model. We will show the performance of the proposed fast algorithm by applying it to two specific problems: the inverse Cauchy problem and Hamiltonian wave equations with energy preservation. These examples demonstrate the effectiveness and applicability of the proposed algorithm in real-world scenarios. |
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Learn MoreDr S. Hon recevied the Early Career Award (21/22) from the Research Grants Council.
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