Author
Hoi Ying Wong
Other affiliations: Hong Kong University of Science and Technology
Bio: Hoi Ying Wong is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Stochastic volatility & Valuation of options. The author has an hindex of 26, co-authored 133 publications receiving 1801 citations. Previous affiliations of Hoi Ying Wong include Hong Kong University of Science and Technology.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors consider the continuous-time mean-variance portfolio selection problem in a financial market in which asset prices are cointegrated and propose an index to simultaneously measure the departure level of a pair from equilibrium and the mean-reversion speed.
86 citations
TL;DR: This paper investigates the valuation of options when the underlying asset follows a mean-reverting lognormal process with stochastic volatility and proposes a bivariate trinomial lattice approach to value path-dependent options.
73 citations
TL;DR: In this paper, the problem of portfolio selection with uncertain correlation is formulated as the utility maximization problem over the worst-case scenario with respect to the possible choice of correlation, and solved under the Black-Scholes model under the theory of $G$-Brownian motions.
Abstract: In a continuous-time economy, we investigate the asset allocation problem among a risk-free asset and two risky assets with an ambiguous correlation between the two risky assets. The portfolio selection that is robust to the uncertain correlation is formulated as the utility maximization problem over the worst-case scenario with respect to the possible choice of correlation. Thus, it becomes a maximin problem. We solve the problem under the Black--Scholes model for risky assets with an ambiguous correlation using the theory of $G$-Brownian motions. We then extend the problem to stochastic volatility models for risky assets with an ambiguous correlation between risky asset returns. An asymptotic closed-form solution is derived for a general class of utility functions, including constant relative risk aversion and constant absolute risk aversion utilities, when stochastic volatilities are fast mean reverting. We propose a practical trading strategy that combines information from the option implied volatilit...
64 citations
TL;DR: This paper considers the continuous-time mean–variance (MV) asset–liability management (ALM) problem for an insurer investing in an incomplete financial market with cointegrated assets and generalizes the technique developed by Lim (2005) to tackle this problem.
56 citations
TL;DR: In this paper, the robust optimal investment and reinsurance problem for a general class of utility functions under a general stochastic volatility model is formulated and an investment-reinsurance strategy that well approximates the optimal strategy of the robust optimization problem under a multiscale SV model is derived.
Abstract: This paper investigates the investment and reinsurance problem in the presence of stochastic volatility for an ambiguity-averse insurer (AAI) with a general concave utility function. The AAI concerns about model uncertainty and seeks for an optimal robust decision. We consider a Brownian motion with drift for the surplus of the AAI who invests in a risky asset following a multiscale stochastic volatility (SV) model. We formulate the robust optimal investment and reinsurance problem for a general class of utility functions under a general SV model. Applying perturbation techniques to the Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation associated with our problem, we derive an investment–reinsurance strategy that well approximates the optimal strategy of the robust optimization problem under a multiscale SV model. We also provide a practical strategy that requires no tracking of volatility factors. Numerical study is conducted to demonstrate the practical use of theoretical results and to draw economic interpretations from the robust decision rules.
55 citations
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Book•
10 Jul 2012
TL;DR: In this paper, a convergence series for Divergent Taylor Series is proposed to solve nonlinear initial value problems and nonlinear Eigenvalue problems with free or moving boundary in heat transfer.
Abstract: Basic Ideas.- Systematic Descriptions.- Advanced Approaches.- Convergent Series For Divergent Taylor Series.- Nonlinear Initial Value Problems.- Nonlinear Eigenvalue Problems.- Nonlinear Problems In Heat Transfer.- Nonlinear Problems With Free Or Moving Boundary.- Steady-State Similarity Boundary-Layer Flows.- Unsteady Similarity Boundary-Layer Flows.- Non-Similarity Boundary-Layer Flows.- Applications In Numerical Methods.
852 citations
TL;DR: In this paper, an optimal homotopy analysis approach is described by means of the nonlinear Blasius equation as an example, which can be used to get fast convergent series solutions of different types of equations with strong nonlinearity.
822 citations
01 Jan 2009
TL;DR: This volume provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backward stochastically differential equations, and martingale duality methods.
Abstract: Stochastic optimization problems arise in decision-making problems under uncertainty, and find various applications in economics and finance. On the other hand, problems in finance have recently led to new developments in the theory of stochastic control. This volume provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backward stochastic differential equations, and martingale duality methods. The theory is discussed in the context of recent developments in this field, with complete and detailed proofs, and is illustrated by means of concrete examples from the world of finance: portfolio allocation, option hedging, real options, optimal investment, etc. This book is directed towards graduate students and researchers in mathematical finance, and will also benefit applied mathematicians interested in financial applications and practitioners wishing to know more about the use of stochastic optimization methods in finance.
759 citations
671 citations