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.

Citation Format:
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.
Fulltext View|Download
Keywords: Expected Shortfall; Value at Risk; Cornish-Fisher Expansion; Covid-19 Pandemic

Article Metrics:

Article Info
Section: Articles
Recent articles
PEMODELAN KURS DOLLAR AMERIKA SERIKAT TERHADAP RUPIAH MENGGUNAKAN REGRESI PENALIZED SPLINE DILENGKAPI GUI R GUI R UNTUK ANALISIS KERANJANG BELANJA DENGAN ALGORITMA APRIORI PADA SUATU PERUSAHAAN E-COMMERCE PEMODELAN TOPIK PADA KELUHAN PELANGGAN MENGGUNAKAN ALGORITMA LATENT DIRICHLET ALLOCATION DALAM MEDIA SOSIAL TWITTER More recent articles
  1. Aktas, O., & Sjostrand, M. 2011. Cornish-Fisher Expansion and Value at Risk Method in Application to Risk Management of Large Portfolios. Thesis, Scholl of Information Science, Computer and Electrical Engineering. Halmstad University
  2. Anisyah, N. 2021. Pengaruh Covid-19 Terhadap Pasar Modal di Indonesia. Jurnal Perbankan dan Keuangan Unsil, Vol. 2, No. 2
  3. Artzner, P., Eber, F. Delbaen, Eber, J. M. dan Heath, D. 1999. Coherent Measures of Risk. Mathematical Finance 9 : 203-228
  4. Cox, N. J. 2010. The Limits of Sample Skewness and Kurtosis. The Stata Journal, Vol. 10, No. 3: 482-495
  5. Gio, P., U., & Effendie, A., R. 2017. Belajar Bahasa Pemrograman R (Dilengkapi Cara Membuat Aplikasi Olah Data Sederhana dengan R Shiny). Medan: USU Press
  6. Internet: Bursa Efek Indonesia pada alamat http://www.idx.co.id. Diakses pada tanggal 20 Juli 2021
  7. Internet: Yahoo! Finance pada alamat http://finance.yahoo.com. Diakses pada tanggal 21 Juli 2021
  8. Internet: Sri Rejeki Isman Tbk pada alamat https://www.sritex.co.id diakses pada tanggal 21 Juli 2021
  9. Jarque, Carlos M. & Bera, Anil K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters 6 : 255–259
  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
  13. Messaoud, S.B., Aloui, C. 2015. Measuring Risk of Portofolio:GARCH-Copula Model. Journal of Economic Integration. Vol 30(1) : 172-205
  14. Sudirman, J. 2015. Pasar Modal dan Manajemen Portofolio. Gorontalo: Sultan Amai Press
  15. Tanasya, L. 2020. Expected Shortfall dengan Pendekatan Glosten-Jagannathan-Runkle GARCH dan Generalized Pareto Distribution. Jurnal Gaussian, Vol.9, No. 4:505-514
  16. Tirta, I., M. 2014. Pengembangan E-Modul Statistika Terintegrasi dan Dinamik dengan R-Shiny dan mathJax. Prosiding Seminar Nasional Matematika Universitas Jember, 223–232
  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

Last update:

No citation recorded.

Last update:

No citation recorded.