Estimating market risk with neural networks
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Jürgen Franke
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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Articles in the same Issue
- Statistical inference on graphs
- Estimating market risk with neural networks
- On Markovian short rates in term structure models driven by jump-diffusion processes
- Robust multivariate location estimation, admissibility, and shrinkage phenomenon
- On local bootstrap bandwidth choice in kernel density estimation
- Correction note: On the optimal risk allocation problem
Articles in the same Issue
- Statistical inference on graphs
- Estimating market risk with neural networks
- On Markovian short rates in term structure models driven by jump-diffusion processes
- Robust multivariate location estimation, admissibility, and shrinkage phenomenon
- On local bootstrap bandwidth choice in kernel density estimation
- Correction note: On the optimal risk allocation problem
