Assessing Asset Tail Risk with Artificial Intelligence: The Application of Artificial Neural Network

Abstract

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

Citation

Becker, Y.L., Guo, L. and Nurmamatov, O. (2020), "Assessing Asset Tail Risk with Artificial Intelligence: The Application of Artificial Neural Network", Lee, C.F. and Yu, M.-T. (Ed.) Advances in Pacific Basin Business, Economics and Finance (Advances in Pacific Basin Business, Economics and Finance, Vol. 8), Emerald Publishing Limited, Leeds, pp. 23-52. https://doi.org/10.1108/S2514-465020200000008002