Modeling exchange rate volatility: application of GARCH models with a Normal Tempered Stable distribution

Sahar Charfi; Farouk Mselmi; Sahar Charfi; Farouk Mselmi

doi:10.3934/QFE.2022009

Quantitative Finance and Economics

2022, Volume 6, Issue 2: 206-222. doi: 10.3934/QFE.2022009

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Research article

Modeling exchange rate volatility: application of GARCH models with a Normal Tempered Stable distribution

Sahar Charfi ^{1
,
,},
Farouk Mselmi ²

1.
Departement of Finance, Faculty of economics and management Sfax, University of Sfax, Sfax, Tunisia
2.
Euromed Research Center, Euromed University of Fes (UEMF), Fes, Morocco

Received: 14 March 2022 Revised: 04 May 2022 Accepted: 10 May 2022 Published: 12 May 2022
JEL Codes: C52, C580, E44, E47

The aim of this paper is to examine exchange rate volatility using GARCH models with a new innovation distribution, the Normal Tempered Stable. We estimated daily exchange rate volatility using different distributions (Normal, Student, NIG) in order to specify the performed model. In addition, a forecasting analysis is performed to check which distribution reveals the best out-of-sample results. We found that the estimated parameters of GARCH-NTS model outperform the GARCH-N and GARCH-t ones for all currencies. Besides, we asserted that GARCH-NTS and EGARCH-NTS are the preferred models in terms of out-of sample forecasting accuracy. Our results indicating the performance of GARCH models with NTS distribution contribute to increase the accuracy of risk measures which is very important for international traders and investors.
- GARCH,
- Normal Tempered Stable distribution,
- exchange rate volatility
Citation: Sahar Charfi, Farouk Mselmi. Modeling exchange rate volatility: application of GARCH models with a Normal Tempered Stable distribution[J]. Quantitative Finance and Economics, 2022, 6(2): 206-222. doi: 10.3934/QFE.2022009

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Abstract

The aim of this paper is to examine exchange rate volatility using GARCH models with a new innovation distribution, the Normal Tempered Stable. We estimated daily exchange rate volatility using different distributions (Normal, Student, NIG) in order to specify the performed model. In addition, a forecasting analysis is performed to check which distribution reveals the best out-of-sample results. We found that the estimated parameters of GARCH-NTS model outperform the GARCH-N and GARCH-t ones for all currencies. Besides, we asserted that GARCH-NTS and EGARCH-NTS are the preferred models in terms of out-of sample forecasting accuracy. Our results indicating the performance of GARCH models with NTS distribution contribute to increase the accuracy of risk measures which is very important for international traders and investors.

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