Portfolio Management and Market Risk Quantification Using Neural Networks

Franke, Jürgen

doi:10.1142/9781848160156_0018

Cited by 3 publications

(2 citation statements)

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“…Another and frequently even more important problem is selecting the number and kind of exogenous variables serving as part of the inputs of the network. If one is willing to spend considerable time on model building and if the necessary data are available like in Franke [8,9] a further improvement is, therefore, to be expected compared to the examples above.…”

Section: Neural Network Based Risk Managementmentioning

confidence: 99%

“…Some case studies in Franke [8,9] show that using additional exogenous information represented by a high-dimensional X t helps in forecasting financial time series and in developping portfolio management strategies. Therefore, we are interested in estimating conditional market risk given observations of a large range of past financial data.…”

Section: Introductionmentioning

confidence: 99%

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Estimating market risk with neural networks

Franke¹,

Diagne²

2006

Statistics &Amp; Decisions

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We consider stochastic volatility models for discrete financial time series of the nonlinear autoregressive-ARCH type with exogenous components. We discuss how the trend and volatility functions determining the process may be estimated nonparametrically by least-squares fitting of neural networks or, more generally, of functions from other parametric classes having a universal approximation property. We prove consistency of the estimates under conditions on the rate of increase of function complexity. The procedure is applied to the problem of quantifying market risk, i.e. of calculating volatility or value-at-risk from the data taking not only the time series of interest but additional market information into account. As an application, we study some stock prices series and compare our approach with the common method based on fitting a GARCH(1,1)-model to the data.

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Section: Neural Network Based Risk Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%