Jump Driven Risk Model Performance in Cryptocurrency Market

Ramzi Nekhili,Jahangir Sultan

doi:10.3390/ijfs8020019

Ramzi Nekhili, Jahangir Sultan

Open Access

https://doi.org/10.3390/ijfs8020019

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Abstract

This paper aims at identifying a validated risk model for the cryptocurrency market. We propose a stochastic volatility model with co-jumps in return and volatility (SVCJ) to highlight the role of jumps in returns and volatility in affecting Value-at-Risk (VaR) and Expected Shortfall (ES) in cryptocurrency market. Validation results based on backtesting show that SVCJ model is superior in terms of statistical accuracy of VaR and ES estimates, compared to alternative models such as TGARCH (Threshold GARCH) volatility and RiskMetrics models. The results imply that for the cryptocurrency market, the best performing model is a stochastic process that accounts for both jumps in returns and volatility.

Highlights

Forecasting volatility is pivotal for developing accurate and realistic risk management models that perform well in good times and in bad
This paper aims at exclusively identifying a risk model that is valid for the cryptocurrency markets
The Christoffersen (1998) conditional coverage test confirms that the two models Stochastic Volatility with Co-Jumps (SVCJ) and threshold GARCH volatility (TGARCH) accurately forecast the VaR as the p-values are greater than 5%

Summary

Introduction

Forecasting volatility is pivotal for developing accurate and realistic risk management models that perform well in good times and in bad. An accurate volatility forecast depends on the assumptions made by the analyst and selection of proper statistical models that can provide a parsimonious representation of the stylized features of the data. According to Bernanke (2008), “Those institutions faring better during the recent turmoil generally placed relatively more emphasis on validation, independent review, and other controls for models and similar quantitative techniques. They continually refined their models and applied a healthy dose of skepticism to model output”. A crucial task facing a risk manager is to make sure the models are tested, back-tested, and validated to minimize expected losses

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