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Two-sided exponential–geometric distribution: inference and volatility modeling

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  • Emrah Altun

    (Bartin University)

Abstract

In this paper, two-sided exponential–geometric (TSEG) distribution is proposed and its statistical properties are studied comprehensively. The proposed distribution is applied to the GJR-GARCH model to introduce a new conditional model in forecasting Value-at-Risk (VaR). Nikkei-225 and BIST-100 indexes are analyzed to demonstrate the VaR forecasting performance of GJR-GARCH-TSEG model against the GJR-GARCH models defined under normal, Student-t, skew-T and generalized error innovation distributions. The backtesting methodology is used to evaluate the out-of-sample performance of VaR models. Empirical findings show that GJR-GARCH-TSEG model produces more accurate VaR forecasts than other competitive models.

Suggested Citation

  • Emrah Altun, 2019. "Two-sided exponential–geometric distribution: inference and volatility modeling," Computational Statistics, Springer, vol. 34(3), pages 1215-1245, September.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:3:d:10.1007_s00180-019-00873-3
    DOI: 10.1007/s00180-019-00873-3
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    References listed on IDEAS

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