Scenario Simulation: Theory and methodology

Jamshidian, Farshid; Zhu, Yu

doi:10.1007/s007800050016

Cited by 120 publications

(69 citation statements)

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“…Recently Jamshidian and Zhu (1997) have suggested an interesting alternative way of speeding up Monte Carlo simulation. This is known as scenario simulation.…”

Section: Approaches To Calculating Value At Riskmentioning

confidence: 99%

Value at Risk When Daily Changes in Market Variables are not Normally Distributed

Hull

White

1998

JOD

215

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This paper proposes a new model for calculating VaR where the user is free to choose any probability distributions for daily changes in the market variables and parameters of the probability distributions are subject to updating schemes such as GARCH. Transformations of the probability distributions are assumed to be multivariate normal. The model is appealing in that the calculation of VaR is relatively straightforward and can make use of the RiskMetrics or a similar database. We test a version of the model using nine years of daily data on 12 different exchange rates. When the first half of the data is used to estimate the model's parameters we find that it provides a good prediction of the distribution of daily changes in the second half of the data.

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“…Recently Jamshidian and Zhu (1997) have suggested an interesting alternative way of speeding up Monte Carlo simulation. This is known as scenario simulation.…”

Section: Approaches To Calculating Value At Riskmentioning

confidence: 99%

Value at Risk When Daily Changes in Market Variables are not Normally Distributed

Hull

White

1998

JOD

215

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“…Procedures for estimating the joint distribution of future bond and stock returns have been discussed by Chen et al (1986), Keim and Stambaugh (1986), Ferson and Harvey (1993), Karolyi and Stultz (1996), and Bossaerts and Hillion (1999). Procedures for estimating discrete scenarios from joint multivariate bond and stock forecasting models have been discussed by Mulvey (1996), Jamshidian and Zhu (1997), Cariño et al (1994, , Zenios (1999), Høyland and Wallace (2001), Pflug (2001), and Roemisch and Heitsch (2003).…”

Section: Scenario Generation and Statistical Inputsmentioning

confidence: 99%

The Innovest Austrian Pension Fund Financial Planning Model InnoALM

Geyer

Ziemba

2008

Operations Research

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This paper describes the financial planning model InnoALM we developed at Innovest for the Austrian pension fund of the electronics firm Siemens. The model uses a multiperiod stochastic linear programming framework with a flexible number of time periods of varying length. Uncertainty is modeled using multiperiod discrete probability scenarios for random return and other model parameters. The correlations across asset classes, of bonds, stocks, cash, and other financial instruments, are state dependent using multiple correlation matrices that correspond to differing market conditions. This feature allows InnoALM to anticipate and react to severe as well as normal market conditions. Austrian pension law and policy considerations can be modeled as constraints in the optimization. The concave risk-averse preference function is to maximize the expected present value of terminal wealth at the specified horizon net of expected discounted convex (piecewise-linear) penalty costs for wealth and benchmark targets in each decision period. InnoALM has a user interface that provides visualization of key model outputs, the effect of input changes, growing pension benefits from increased deterministic wealth target violations, stochastic benchmark targets, security reserves, policy changes, etc. The solution process using the IBM OSL stochastic programming code is fast enough to generate virtually online decisions and results and allows for easy interaction of the user with the model to improve pension fund performance. The model has been used since 2000 for Siemens Austria, Siemens worldwide, and to evaluate possible pension fund regulation changes in Austria.

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“…When using stochastic programming models, scenario generations play important roles, which determine the validity of the models. A few methods have been presented to model economic factors and asset returns, such as statistical modelling with the Value-at-Risk approach (Jamshidian and Zhu (1997) [11], Consiglio and Zenios(2001) [12]), vector autoregressive models (Boender (1997) [13]), etc.. Future research may consider to evaluate different methods in generating scenarios of the uncertainties in the multi-period models.…”

Section: Discussionmentioning

confidence: 99%

A Dynamic Stochastic Programming Model for Bond Portfolio Management

Wang

et al. 2004

Computational Science - ICCS 2004

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Abstract. In this paper we develop a dynamic stochastic programming model for bond portfolio management. A new risk measurement-shortfall cost is put forward. It allows more tangible expression of the risks that the decision makers face than does the traditional risk measure-variance of terminal wealth. We also adopt the interest rate model of Black et al. to generate scenarios of riskless short rates at future periods. An example of bond portfolio management is presented to illustrate that our model dominates the usual fixed-mix model.

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Scenario Simulation: Theory and methodology

Cited by 120 publications

References 0 publications

Value at Risk When Daily Changes in Market Variables are not Normally Distributed

Value at Risk When Daily Changes in Market Variables are not Normally Distributed

The Innovest Austrian Pension Fund Financial Planning Model InnoALM

A Dynamic Stochastic Programming Model for Bond Portfolio Management

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