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中山大学计算金融研讨班会议议程

文章来源:财务与投资系     浏览次数:11960     发布时间: 2014年7月22日



会议主办单位 Hosts




 

July 25-28, 2014

Sun Yat-Sen University, Guangzhou

Program

Address: Shansi Building M106, Sun Yat-Sen Business School

Thursday, July 24

16:00-18:00

Registration: Hall of Shansi Building (2th floor)

Friday, July 25

7:30-8:00

Registration: Hall of Shansi Building (2th floor)

8:00-8:10

Opening Remarks: Prof. Duan Li & Prof. Zhongfei Li

8:10-8:50

(M106)

Keynote Address: Big picture of computational finance

Prof. Peter Forsyth

8:50-9:00

Break & Photo

9:00-12:00

(M106)

Tutorial 1: Basics of financial optimization

Prof. Shushang Zhu

14:30-17:30

(M106)

Tutorial 2: Techniques for managing uncertainty in financial risk and modeling: hedging multiple Greeks and robust portfolio selection

Prof. Frederi G Viens

Saturday, July 26

9:00-12:00

(M106)

Tutorial 3: Dynamic programming methods

Prof. Zhongfei Li

14:30-17:30

(M106)

Tutorial 4: Lattice methods for option pricing

Prof. Min Dai

Sunday, July 27

9:00-12:00

(M106)

Tutorial 5: Simulation in finance

Prof. Nan Chen

14:30-17:45

Invited talks

(M106)

(45 minutes/talk)

Chair: Duan Li

Multi-period mean variance asset allocation: Is it bad to win the lottery?

Prof. Peter Forsyth

Dynamic portfolio with mispricing and model ambiguity

Prof. Frederi G Viens

Tea Break (15 minutes)

Chair: Shushang Zhu

Practical scenario tree generation and reduction methods for dynamic portfolio management problem

Prof. Zhiping Chen

Time inconsistency, self-control and internal harmony: A planner-doer game framework

Prof. Duan Li

Sunday, July 28

 

8:45-12:00

Invited talks

(M106)

(45 minutes/talk)

Chair: Jianjun Gao

On several types of partial differential equation models in mathematical finance

Prof. Baojun Bian

Modeling financial systemic risk---the network effect and the market liquidity effect

Prof. Nan Chen

Tea Break (15 minutes)

Chair: Zhiping Chen

Calibration of stochastic volatility models: A Tikhonov regularization approach

Prof. Min Dai

On dynamic mean-downside risk portfolio optimization in continuous-time

Prof. Jianjun Gao

  

Invited Talks

 

Multi-period mean variance asset allocation: Is it bad to win the lottery?

Peter Forsyth

University of Waterloo

We present semi-self-financing mean-variance (MV) dynamic asset allocation strategies which are superior to self-financing MV portfolio strategies. Our strategies are built upon a Hamilton-Jacobi-Bellman (HJB) equation approach for the solution of the portfolio allocation problem. We extend the idea of the semi-self-financing approach originally developed in Cui et al, Mathematical Finance 22 (2012)346-378. Under an HJB framework, our strategies have a simple and intuitive derivation, and can be readily employed in a very general setting, namely continuous or discrete re-balancing, jump-diffusions, and realistic portfolio constraints. MV strategies are often criticized for penalizing the upside as well as the downside. However, under our strategies, the MV portfolio optimization problem can be shown to be equivalent to maximizing the expectation of a well-behaved utility function of the portfolio wealth. We show that, for long term investors, the use of dynamic MV strategies can achieve the same expected value with a much smaller standard deviation compared to a constant proportions strategy.

 

Dynamic portfolio with mispricing and model ambiguity

Frederi G Viens

Purdue University

Abstract: We investigate optimal portfolio selection problems with mispricing and model ambiguity   under a financial market which contains a pair of mispriced stocks. We assume that the dynamics of the pair satisfies a “cointegrated system” advanced by Liu and Timmermann in a

2013 manuscript. The investor hopes to exploit the temporary mispricing by using a portfolio strategy under a utility function framework. Furthermore, she is ambiguity-averse and has a specific preference for model ambiguity robustness. The explicit solution for such a robust optimal strategy, and its value function, are derived. We analyze these robust strategies with mispricing in two cases: observed and unobserved mean-reverting (MR) stochastic risk premium. We show that the mispricing and model ambiguity have completely distinct impacts on the robust optimal portfolio selection, by comparing the utility losses. We also find that the ambiguity-averse investor (AAI) who ignores the mispricing or the model ambiguity, suffers a substantially larger utility loss if the risk premium is unobserved, compared to when it is observed. This is joint work with Bo YI, Baron LAW, and Zhongfei LI. It is in press in the Annals of Finance, 2014: DOI  10.1007/s10436-014-0252-y .

 

Practical scenario tree generation and reduction methods for dynamic portfolio management problem

Zhiping Chen

Xi’an Jiaotong University

Dynamic portfolio management problems can be described as some kinds of dynamic stochastic optimization problems, while a scenario tree is an efficient way to transform a stochastic programming problem into a tractable nonlinear programming problem. To enhance their computational efficiency and make them practical for multistage decision problems under uncertainty, we discuss in this talk new scenario tree generation and reduction techniques, as well as their application in dynamic portfolio selection problems. A general modified K-means clustering method is first presented to generate the scenario tree with a general structure; the moment matching of multi-stage scenario trees is described as a linear programming problem; by simultaneously utilizing simulation, clustering, non-linear time series and moment matching skills, a sequential generation method and another new hybrid approach which can generate the whole multi-stage tree right off are proposed; to find a superior investment policy, we propose an extremum scenario tree generation approach, which is further improved by incorporating the moment matching technique; a refined version of the existing single node reduction method is  developed to overcome its deficiencies, the single node reduction method is extended to a multi-node reduction method, which could reduce multiple nodes at each iteration; in addition, the no-arbitrage opportunity condition is considered in our scenario tree reduction methods, satisfying the important requirement in financial optimization problems. All the above scenario tree generation and reduction methods are illustrated by using them to solve multi-period portfolio selection problems, a series of empirical experiments show the practicality and efficiency of our new methods.

 

 

Time inconsistency, self-control and internal harmony:

A planner-doer game framework

Duan Li

The Chinese University of Hong Kong / Sun Yat-Sen University

For time inconsistent multi-period mean-variance portfolio decision, we develop a two-tier planner-doer game model with self-control, in which planner and doers represent different interests of the same investor at different time instants and planner (the willpower to resist short term temptations) can impact preferences of doers through commitment by punishment, while the applied total penalty in turn affects the planner's preference. Dealing with time inconsistency is to achieve a degree of internal harmony (measured quantitatively by expected cost of self-control) through aligning interests of planner and doers. We further extend this game framework to general time inconsistent stochastic decision problems.

 

金融数学中的几类偏微分方程模型

边保军

同济大学

在报告中,我们将介绍和讨论几类有金融数学背景的偏微分方程,包括大家熟知的Black-Scholes 线性抛物方程(欧式期权定价模型),抛物方程变分不等式(美式期权定价模型),Hamilton-Jacobi-Bellman-Issacs方程(最优投资和微分博弈数学模型),以及非局部积分-微分方程(跳扩散问题),也讨论相关的数值计算。

 

Modeling financial systemic risk---the network effect and the

market liquidity effect

Nan Chen

The Chinese University of Hong Kong

Financial institutions are interconnected directly by holding debt claims against each other (the network channel), and they are also bound by the market liquidity in selling assets to meet debt liabilities when facing distress (the liquidity channel). The goal of our study is to investigate how these two channels of risk interact to propagate individual defaults to a system-wide catastrophe. We formulate the model as an optimization problem with equilibrium constraints and derive a partition algorithm to solve for the market-clearing equilibrium. The solutions so obtained enables us to identify two factors, the network multiplier and the liquidity amplifier, to characterize the contributions of these two channels to financial systemic risk, whereby we can acquire better understanding of the effectiveness of several policy interventions. The analysis behind the algorithm yields estimates for the contagion probability on the basis of the market value of the institutions' net worths, underscoring the importance of equity capital as a cushion against systemic shocks in the presence of the liquidity channel. The optimization formulation also provides more structural insights to allow us to extend the study of systemic risk to a system with debts of different seniorities, and meanwhile presents a close connection to the literature of stochastic networks. Finally, we illustrate the impacts of the network and the liquidity channels--- in particular, the significance of the latter---in the formation of systemic risk with data on the European banking system.

 

Calibration of stochastic volatility models: A Tikhonov regularization approach

Min Dai

National University of Singapore

We aim to calibrate stochastic volatility models from option prices. We develop a Tikhonov regularization approach with an efficient numerical algorithm to recover the risk neutral drift term of the volatility (or variance) process. In contrast to most existing literature, we do not assume that the drift term has any special structure. As such, our algorithm applies to calibration of general stochastic volatility models. An extensive numerical analysis is presented to demonstrate the efficiency of our approach.

 

On dynamic mean-downside risk portfolio optimization in continuous-time

Jianjun Gao

Shanghai Jiaotong University

Instead of controlling “symmetric” risks measured by central moments of investment return or terminal wealth, more and more portfolio models have shifted their focus to manage ``asymmetric'' downside risks that the investment return is below certain threshold. Among the existing downside risk measures, the lower-partial moments (LPM) and conditional value-at-risk (CVaR) are probably most promising. In this paper we investigate the dynamic mean-LPM and mean-CVaR portfolio optimization problems in continuous-time, while the current literature has only witnessed their static versions. Our contributions are two-fold, in both building up tractable formulations and deriving corresponding analytical solutions. By imposing a limit funding level on the terminal wealth, we conquer the ill-posedness exhibited in the class of mean-downside risk portfolio models. The limit funding level not only enables us to solve both dynamic mean-LPM and mean-CVaR portfolio optimization problems, but also offers a flexibility to tame the aggressiveness of the portfolio policies generated from such mean - downside risk models. More specifically, for a general market setting, we prove the existence and uniqueness of the Lagrangian multiplies, which is a key step in applying the martingale approach, and establish a theoretical foundation for developing efficient numerical solution approaches. Moreover, for situations where the opportunity set of the market setting is deterministic, we derive analytical portfolio policies for both dynamic mean-LPM and mean-CVaR formulations.

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