Value at risk estimation of exchange rate in banking industry

Siti Saadah, Marsiana Luciana Sitanggang

Abstract


More integrated financial sector has made market risk arising from volatility of exchange rate critical for banking industry. In attempt to mitigate such risk, this study aims to measure risk from IDR/USD exchange rate movement using value-at-risk method by comparing results of estimates using standard and asymmetric generalized autoregressive conditional heteroscedasticity models. Using data on the daily exchange rate of IDR to USD between July 31, 2018 and July 31, 2019, this study found that the asymmetric exponential GARCH using generalized error distribution is the best approach to estimate exchange rate risk. Results of the estimate suggest that standard GARCH model generated a more conservative measure of risk than value-at-risk estimated using exponential GARCH model. Value at risk can be one of the risk indicators for risk managers in banks. The choice of a model is likely to depend on the attitude to risk itself. Risk averse character who does not like risk will choose the most conservative method in calculating the VaR.

JEL Classification: E44, G4, G11

DOI: https://doi.org/10.26905/jkdp.v24i4.4808


Keywords


Asymmetric GARCH; Generalized error distribution; Standard GARCH; Value at risk

Full Text:

PDF

References


Altun, E., Alizadeh, M., Kadilar, G. O., & Tatlıdil, H. (2017). Forecasting Value-at-Risk with two-step method: GARCH exponentiated odd log-logistic normal model. Romanian Journal of Economics Forecasting, 20(4).

Azzalini, A. (1985). A class of distributions which includes the normal ones. Scandinavian Journal of Statistics, 12(2), 171-178.

Basel Committee on Banking Supervision. (1996a). Supervisory framework for the use of “backtesting” in conjunction with the internal models approach to market risk capital requirement. BIS.

Basel Committee on Banking Supervision. (1996b). Amendment to the capital accord to incorporate market risk. BIS.

Brooks, C. (2002). Introductory Econometrics for Finance. 3rd Edition. Cambridge: Cambridge University Press.

Bohdalova, M., & Michal, G. (2015). Estimating Value-at-Risk based on non-normal distributions. CBU International Conference Proceedings, 3, 188-195. https://doi.org/10.12955/cbup.v3.601

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1

Christoffersen, P. F., & Diebold, F. X. (2000). How relevant is volatility forecasting for financial risk management? Review of Economics and Statistics, 82(1), 12-22. https://doi.org/10.1162/003465300558597

Christianti, A. (2010). Risiko Pasar: perbandingan model EWMA dan GARCH pada nilai tukar rupiah terhadap US Dollar. Jurnal Riset Manajemen dan Bisnis, 5(2), 153-172.

Crouhy, M., Galai, D., & Mark, R. (2000). A comparative analysis of current credit risk models. Journal of Banking & Finance, 24(1-2), 59-117.

https://doi.org/10.1016/S0378-4266(99)00053-9

Fauziah, M. (2014). Analisis risiko pada portfolio saham Syariah menggunakan Value at Risk dengan pendekatan Generalized Pareto Distribution (GPD). Jurnal Konvergensi, 4(2), 85-104.

Febriana, D., Tarno, T., & Sugito, S. (2014). Perhitungan Value at Risk menggunakan model integrated generalized autoregressive conditional heteroscedasticity (Studi kasus pada return kurs rupiah terhadap dollar Australia). Jurnal Gaussian, 3(4), 635-643. https://doi.org/10.14710/j.gauss.v3i4.8074

Fernandez, C., & Steel, M. On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93(441), 359-371. https://doi.org/10.1080/01621459.1998.10474117

Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779-1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x

Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics, 20(7), 873–889. https://doi.org/10.1002/jae.800

Jorion, P. (2001). Value at Risk: The New Benchmark for Managing Financial Risk. 3nd Edition. Boston: Mc-Graw Hill.

Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. The Journal of Derivatives Winter, 3(2), 73-84. https://doi.org/10.3905/jod.1995.407942

Nelson, D. B. (1990). Stationarity and persistence in the GARCH(1,1) Model. Econometric Theory, 6(3), 318–334. https://doi.org/10.1017/s0266466600005296

Owen, D. B. (1956). Tables for computing bivariate normal probabilities. Annals of Mathematical Statistics, 27(4), 1075-1090. https://doi.org/10.1214/aoms/1177728074

Penza, P., & Bansal, V. K. (2001). Measuring Market Risk with Value at Risk. New Jersey: John Wiley & Sons.

Student. (1908). The probable error of a mean. Biometrika, 6(1), 1-25. https://doi.org/10.2307/2331554

Theodossiou, P. (1998). Financial data and the skewed generalized t distribution. Management Science, 44(12 part 1), 1650-1661. http://dx.doi.org/10.1287/mnsc.44.12.1650




DOI: https://doi.org/10.26905/jkdp.v24i4.4808

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Jurnal Keuangan dan Perbankan (Journal of Finance and Banking)

Diploma Program of Banking and Finance, Faculty of Economics and Business, University of Merdeka Malang

Published by University of Merdeka Malang

Mailing Address:
2nd floor Finance and Banking Building, Jl. Terusan Raya Dieng No. 57 Malang, East Java, Indonesia
Phone: +62 813-3180-1534
Email: jkp@unmer.ac.id

This work is licensed under a Creative
Commons Attribution-ShareAlike 4.0