DOI: https://doi.org/10.14710/j.gauss.v11i2.35457

EXPECTED SHORTFALL DENGAN EKSPANSI CORNISH-FISHER UNTUK ANALISIS RISIKO INVESTASI SEBELUM DAN SESUDAH PANDEMI COVID-19 DILENGKAPI GUI R

*Reyuli Andespa  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Di Asih I Maruddani  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Tarno Tarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2022 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
In financial analysis, risk measurement is critical. Stocks are a sort of financial asset investment that is in high demand by investors. Expected Shortfall is one of the strategies used to assess stock investing risk (ES). ES is a risk metric that considers losses in excess of the Value at Risk (VaR). Cornish-Fisher Expansion (ECF) is used to calculate ES with data that deviates from normality and takes into account skewness and kurtosis values. This study used data from the closing price of Sri Rejeki Isman Tbk (SRIL) shares before and during the Covid-19 Pandemic (14 January 2019 to 18 May 2021), with non-normally distributed returns. According to the calculations, the risk that investors will bear using the ES ECF value for the next day before the Covid-19 Pandemic is 1.1752 and after the Covid-19 Pandemic is 3.3177% at a 95% confidence level. The risk that investors will bear for the next day before the Covid-19 Pandemic is 5.8928%, and after the Covid-19 Pandemic is 10.3703%, based on a 99% confidence level. The findings of the study reveal that the higher the amount of trust, the higher the risk.
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Keywords: Expected Shortfall; Value at Risk; Cornish-Fisher Expansion; Covid-19 Pandemic

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  10. Jorion, P. 2002. Value at Risk: The New Benchmark for Managing Financial Risk. Second Edition. New York: The McGraw-Hill Companies, Inc
  11. Maruddani, D. A. I. 2019. Value at Risk untuk Pengukuran Risiko Investasi Saham. Ponorogo: WADE Group
  12. Maruddani, D. A. I., dan Trimono.2020. Microsoft Excel Untuk Pengukuran Value at Risk Aplikasi pada Risiko Investasi Saham. Semarang: UNDIP Press
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  17. Urysev, S., 2000. Conditional Value-at-Risk: Optimization Algorithms and Aplication. Financial Engineering News. University of Florida
  18. Yamai, Y. dan Yoshiba, T. 2005. Value at Risk Versus Expected Shortfall: A Practical Perspective. Journal of Banking & Finance, Vol. 29: 997-1015

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