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What is the most appropriate model for generating scenarios for daily foreign exchange rates?

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  • Parker, John C.

Abstract

This paper investigates the most appropriate model for generating scenarios for daily foreign exchange rates for a long history of a large number of daily exchange rates and finds: returns are not normal; a mean reversion model is rarely appropriate; sampling from historical returns (natural log differenced data) will capture the basic features of the mean of the return data but will ignore the autocorrelation in the mean and variance of returns; using a fat-tailed distributional assumption by matching the kurtosis of the historical data will capture the excess kurtosis of the data but similarly ignore these autocorrelations; a GARCH(1,1) model is in most cases sufficient to model time dependence of the conditional variance and will generate returns with excess kurtosis. In some cases an MA(1) - GARCH(1,1) model is required to capture residual autocorrelation, and in a few case more complicated ARMA(p,q) - GARCH(1,1) models are needed.

Suggested Citation

  • Parker, John C., 2005. "What is the most appropriate model for generating scenarios for daily foreign exchange rates?," MPRA Paper 40269, University Library of Munich, Germany, revised Jun 2005.
  • Handle: RePEc:pra:mprapa:40269
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    File URL: https://mpra.ub.uni-muenchen.de/40269/1/MPRA_paper_40269.pdf
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    References listed on IDEAS

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    1. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
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    More about this item

    Keywords

    ARIMA models; Exchange Rates; GARCH models; Risk Management; Scenarios; Time series; Vector Autoregression Models; Volatility forecasting;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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