A spectral analysis based heteroscedastic model for the estimation of value at risk

Abstract

Purpose

This paper aims to focus on a better model to capture the trait of varying volatility in various financial time series, as well as to obtain reliable estimate of value at risk (VaR).

Design/methodology/approach

The typical methods in spectral analysis are used to obtain the sample of conditional mean, conditional variance and residual term. The generalized regression neural network is used to establish a time-varying non-linear model, and the non-parametric kernel density estimation method is applied for the estimation of VaR.

Findings

The proposed model is able to follow the heteroscedastic characteristic which is common in financial time series, and the estimated VaR is satisfactory.

Practical implications

The analysis method in this study allows the hedgers, bankers, financial analysts as well as economists to draw a better inference from financial time series. Also, relatively more precise estimate of the VaR value for a certain kind of financial asset is available. The model with its derived estimates would definitely help in developing other models.

Originality/value

Up-to-date, the study in literature which models financial time serial from the viewpoint of spectral analysis is rare to see. Thus, the proposed model, along with a comprehensive empirical study which reveals desirable result for the estimation of VaR would enrich the related researches at present.

Citation

Zhao, Y. (2018), "A spectral analysis based heteroscedastic model for the estimation of value at risk", Journal of Risk Finance, Vol. 19 No. 3, pp. 295-314. https://doi.org/10.1108/JRF-01-2017-0012