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

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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.

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Authors and Affiliations

  1. Department of Statistics, Bartin University, 74100, Bartin, Turkey

    Emrah Altun

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  1. Emrah Altun
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Correspondence to Emrah Altun.

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Altun, E. Two-sided exponential–geometric distribution: inference and volatility modeling. Comput Stat 34, 1215–1245 (2019). https://doi.org/10.1007/s00180-019-00873-3

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  • DOI: https://doi.org/10.1007/s00180-019-00873-3

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