PERBANDINGAN VOLATILITAS EWMA, GARCH DAN MONTE CARLO TERHADAP NILAI TUKAR MATA UANG ASING BANK Bjb

Safrin Marulitua, AK., MBA., CPA., CA.

Abstract


This research addresses the comparison of accuracy volatility models especially Exponentiall Weighted Moving Average (EWMA), Generalized Autoregressive Conditional Heteroscedastic (GARCH) with its classification and Monte Carlo Simulation (MCS) for measuring market risk in order to calculate VaR of portofolio to exchange rate of AUD/IDR, EUR/IDR and USD/IDR of Bank Bjb.

Testing in the validity of the model using Kupiec mixed backtest shows that GARCH volatility model and its classification with confidence level of 95% proved that three foreign currency exchange rate AUD, EUR and USD has valid and accurate model, while EWMA valid for AUD and EUR currencies and MCS valid for USD currency.

Results of the portfolio market risk VaR estimation using filtered historical simulation method as amount of Rp797.083.763, gives information reserve capital or minimum capital charge to be provided by Bank BJB, in addition must also take into account the credit risk and operational risk.

Keywords


Ekonomi; Akuntansi; Keuangan; Mata uang; value-at-risk; exponentiall weighted moving average; generalized autoregressive conditional heteroscedasticity; monte carlo simulation; backtesting kupiec mixed; filtered historical simulation; Mata uang; Bank BJB

References


Alexander, Gordon. (2009). From

markowitz to modern risk

management, The European Journal

of Finance, Vol.15, Nos.5-6 July –

September 2009, 451-461.

Alexander, Carol. (2008). Market risk analysis IV, value-at-risk models, John Wiley & Sons, Inc.

Backus, Gregory. (1993). Theoritical relation between risk premiums and conditional variances, Journal of Business & Economic Statistics, Vol 11, no.2.

Balaban, E., Bayar, A., and Faff, R. (2002). Forecasting stock market volatility: evidence from fourteen countries. University of Edinburgh,

Center for Financial Market Research, working paper, 2002:04.

Bank Indonesia, (2003). PBI No. 5/8/PBI/2003: Penerapan manajemen risiko bagi bank umum.

Bank Indonesia, (2005). PBI No. 7/3/PBI/2005: Posisi devisa netto bank umum.

Bank Indonesia, (2006). Implementasi Basel II di Indonesia. Availabe at http://www.bi.go.id/id/perbankan/im plementasi- basel/dokumentasi/Documents/585a 12be8df34e94a4f53bfe2b59029dImp lementasiBaselIIdiIndonesia.pdf

Bank Indonesia, (2013). Booklet Perbankan Indonesia. Available at http://www.bi.go.id/id/publikasi/perb ankan-dan-stabilitas/booklet- bi/Documents/71d37a10007f4457b1 92adb4c5a0bb97BPI2013Indonesia. pdf

Barone-Adesi., Giannopoulos, K., and Vosper, Les. (1999). VaR without correlations for portfolios of derivative securities, London.

Basle Committee of Banking Supervision. (1996). Supervisory framework for the use of “backtesting” in conjunction with the internal models approach to market risk capital requirements. Available at www.bis.org

Basel Committee on Banking Supervision (2005). Amendment to the capital accord to incorporate market risks.

Basel Commitee. (2006). International convergence of capital measurement and capital standards. Availabe at http://www.bis.org/publ/bcbs128.pdf

Bernstein, Peter L.(1996). The new religion of risk management,

Harvard Business Review March-April, 96203:3-6.

Berkowitz, J., and O’Brien, J. (2002). How

accurate are value-at-risk models at

commercial banks? Journal of

Finance, Vol. 5, 2002.

Bollerslev, Tim.(1986). Generalized

autoregressive conditional

heteroskedasticity, Journal of

Econometrics, 31, 307-27.

Brooks, Chris. (2008). Introductory

econometrics for finance, 2nd

edition, Cambridge: Cambridge

Universtity Press, 2008.

Campbell, J.Y. (1988). Stock prices, earnings, and expected dividends,

Journal of Finance 43:661-676.

Campbell, S. (2005). A review of backtesting and backtesting procedure, Finance and Economics Discussion Series, Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board, Washington D.C.

Christoffersen, P.F. (1998, November).

Evaluating interval forecast, McGill

University, Canada, published in:

International Economic Review, Vol.

No.4.

Christoffersen, P. F., and Diebold. F. X. (2000). How relevant is volatility forecasting for financial risk management? The Review of Economics and Statistics, 82:1,12-22.

Chistodoulakis, George, Stephen Satchell (2008). The analytics of risk model validation, Elsevier.

Clewlow, L., and Strickland, C. (2000). Energy derivatives: pricing and risk management, Lacima Group, Texas.

Danielsson, Jon. (2011). Financial risk forecasting, John Wiley & Sons.

Djati, B.S Lelono. (2007). Simulasi teori dan aplikasinya, Andi Penerbit.

Dowd, Kevin. (2005). Measuring market risk (2nd edition). Wiley

Dowd, Kevin. (2006a). Beyond value at risk: the new science of risk management, John Wiley & Sons Ltd.

Dowd, Kevin. (2006b). Retrospective assessment of value-at-risk. risk management: a modern perspective, pp. 183-202, San Diego, Elsevier.

Ding, Z., Engle, R. F., and Granger, C.W.J. (1993). Long memory properties of stock market returns and a new model. Journal of empirical finance, 1, 83 – 106.

Ding, Jie., and Meade, Nigel. (2010). Forecasting accuracy of stochastic volatility, GARCH and EWMA models under different volatility scenarios, Applied Financial Economics, 2010, 20, 771–783.

Eviews 6 user’s guide. (2007).

Quantitative micro software, LLC, USA, Management.

Engle, Robert. F. (1982). Autoregressive conditional heteroscesdascity with estimates of United Kingdom inflation, Econometrica; 50/4, 987-1007.

Engle, Robert. F., and Bollerslev, T. (1986). Modelling the persistence of conditional variances, Econometrics Reviews, vol.5, 1-50, 81-87.

Engle, Robert. F., and Gizyki. (1999).

Conservatism, accuracy and

efficiency: comparing value at risk model, Reserve Bank of Australia.

Engle, Robert.F. (2001). The use of ARCH/ GARCH models in applied econometrics. Journal of Economic Perspective. Volume 15, Number 4, pp 157-168.

Enders, Walter. (2004). Applied

econometric time series 2nd ed., John

Wiley & Sons, Inc.

Engle, Robert. F. (1995). “ARCH selected readings, Oxford University Press, New York.

Engle, R.F., Lilien, D., and Robins, P. (1987). Estimating time varying risk premia in the term structure: The ARCH-M model, Journal Econometrics, Vol 55, p.3.

Figlewski, Stephen. (2004). Forecasting volatility, working paper, ( http://citeseerx.ist.psu.edu/viewdoc/ download?doi=10.1.1.125.6925&rep =rep1&type=pdf)

Finger, C. (2005). Back to backtesting, Research Monthly, May 2005, RiskMetrics Group.

Galdi., and Pereira. (2007). Value at Risk (VaR) using volatility forecasting models: EWMA, GARCH and stochastic volatility, Brazilian Business Review, Vol. 4, No. 1 Jan/ Apr 2007 pp. 74-94. Vitória-ES, Brazil.

Haas, M. (2001). New methods in backtesting, Financial Engineering, Research

Center Caesar, Bonn.

Hartono, Jogiyanto. (2010). Teori

portofolio dan analisis investasi,

BPFE Yogyakarta.

Hendricks, Darryll. (1995). Evaluation of

value-at-risk models using historical

data, Federal Reserve Bank of New

York Economic Policy Review.

Holton, Glyn, A. (2004). Defining risk, Financial analyst journal, 60:19-25

Jorion, Philippe. (2001). Value at risk, Second Edition, MacGraw-Hill.

Jorion, Philippe. (2007a). Value at risk: The new benchmark for managing financial risk. (3rd ed). New York: McGraw-Hill/Irwin.

Jorion, Philippe. (2007b). Financial Risk Manager Handbook. (4th ed), John Wiley & Sons, Inc, New Jersey.

Morgan, J.P. (1996). Riskmetrics-

technical manual. New York: JP

Morgan & Co.

Nachrowi, D. N., and Usman, H. (2006). Pendekatan Populer dan Praktis Ekonometrika untuk Analisis Ekonomi dan Keuangan, LP FEUI.

Juanda., and Junaidi. (2012). Ekonometrika deret waktu, teori dan aplikasi, IPB Press.

Lopez, J. (1998). Methods for evaluating value-at-risk estimates, Economic Policy Review, October 1998, 119-64.

Lopez, J. (1999). Regulatory evaluation of value-at-risk models, Journal of Risk 1, 37-64

Thomas, J. Kaikay. (2004). Pengantar

sistem simulasi, Andi penerbit.

Kupiec, P. (1995). Techniques for verifying the accuracy risk measurement model, The Journal of Derivatives, Vol. 2 (Desember), Hlm. 74-84.

McCullogh, B,D., and Vinod, H.D. (2003). Econometrics and software, Journal of Economic Perspective, 17(1): 223-224.

Minkah, Richard. (2007). Forecasting volatility, Uppsala University. Available at http://www2.math.uu.se/research/pu b/Minkah1.pdf

Nachrowi, D. N., and Usman, H. (2006). Pendekatan populer dan praktis ekonometrika untuk analisis ekonomi dan keuangan, LP FEUI, Jakarta.

Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: a new approach, Econometrica, Vol. 59, pp. 347-70.

Nieppola, Olli. (2009). Backtesting value-at-risk models. Department of Economics- Helsinki School of Economics.

Pindyck, Robert, S., and Rubinfeld, L.

Daniel. (1998). Econometric models

and economic forecast, The

McGraw-Hill Companies.

Poon, Ser-Huang., and Granger, W.J, Cliff. (2003). Forecasting volatility in financial market: A review,

Journal of Economic Literature, 41:478-539.

Poon, Ser-Huang., and Granger, W.J,

Cliff. (2005). Practical issues in

forecasting volatility. Financial

Analyst Journal, 61:1, 45-56.

Rabemananjara, R., and Zakoian. J. M. (1993). Threshold ARCH models and asymmetries in volatility."

Journal of Applied Econometrics,

Vol. 8, 31-49.

Render, Barry, Jr., and Stair, Ralph. M.

(1997). Quantitative analysis for

management, Prentice-Hall, Inc,

New Jersey.

Rubinstein. R.Y. (1981). Simulation and

the monte carlo method, John Wiley

& Sons.

Rosadi, Dedi. (2012). Ekonometrika & analisis runtun waktu terapan dengan eviews, Andi Publisher.

Rosenblatt. M. (1952, Sept). Remarks on a multivariate transformation, The Annals of Mathematical Statistics

Vol.23 No. 3, pp.470-472.

Saita, F. (2007). Value at risk and bank capital management: risk adjusted performance, capital management, and capital allocation decision making. Oxford: Elsevier.

Sauders, Anthony. (2000). Financial

institutions management – A modern

perspective. Ed-3, McGraw-Hill,

New York.

SAS Institute. (1999). GARCH, IGARCH,

EGARCH, and GARCH-M Models.

SAS Institute Inc. North Carolina.

Sentana, E. (1995). Quadratic ARCH models, Review of Economic Studies

, 639–661.

Spiegel, M. R., Schiller, J. J., & Srinivasan, R. A. (2000). Schaum’s Outline ofTheory and Problems of Probability and Statistics. (2nd ed.). New York:

McGraw-Hill/Irwin.

Starica, C., Herzel, S., and Nord, T. (2006, February). The impact of the IGARCH effect on longer-horizon

volatility forecasting.

Handelsbanken Research

Foundation, working paper.

Tsay., Wen-Jen., and Ching-Fan Chung. (2000). The spurious regression of fractionally integrated processes,

Journal of Econometrics 96, 155-182.

Virdi, Navneet Kaur. (2011). A Review of

Backtesting Methods for Evaluating

Value-at-Risk, International Review

of Business Research Papers Vol. 7.

No. 4. July 2011 Pp. 14-24.

Wang, Kent. (2010). Forecasting volatilities in equity, bond and money markets: A market-based approach, Australian Journal of Management; Aug 2010; 35, 2.

Watsham., Terry J., and Parramore, K.

(1997). Quantitative methods in

finance, First Edition, Thompson

Learning.

Zakoian, J. M. (1994). Threshold heteroskedastic models, Journal of Economic Dynamics and Control,18 (1994): 931-955.




DOI: http://dx.doi.org/10.35137/jabk.v3i2.80

Copyright (c) 2016 Safrin Marulitua, AK., MBA., CPA., CA.



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