RiskMetrics method for estimating Value at Risk to compare the riskiness of BitCoin and Rand
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Author(s)Delson ChikobvuLink to ORCID Index: https://orcid.org/0000-0003-3979-658X
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Thabani NdlovuLink to ORCID Index: https://orcid.org/0000-0002-4386-8147
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DOIhttp://dx.doi.org/10.21511/imfi.20(1).2023.18
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Article InfoVolume 20 2023, Issue #1, pp. 207-217
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Creative Commons Attribution 4.0 International License
In this study, the RiskMetrics method is used to estimate Value at Risk for two exchange rates: BitCoin/dollar and the South African Rand/dollar. Value at Risk is used to compare the riskiness of the two currencies. This is to help South Africans and investors understand the risk they are taking by converting their savings/investments to BitCoin instead of the South African currency, the Rand. The Maximum Likelihood Estimation method is used to estimate the parameters of the models. Seven statistical error distributions, namely Normal Distribution, skewed Normal Distribution, Student’s T-Distribution, skewed Student’s T-Distribution, Generalized Error Distribution, skewed Generalized Error Distribution, and the Generalized Hyperbolic Distributions, were considered when modelling and estimating model parameters. Value at Risk estimates suggest that the BitCoin/dollar return averaging 0.035 and 0.055 per dollar invested at 95% and 99%, respectively, is riskier than the Rand/dollar return averaging 0.012 and 0.019 per dollar invested at 95% and 99%, respectively. Using the Kupiec test, RiskMetrics with Generalized Error Distribution (p > 0.07) and skewed Generalized Error Distribution (p > 0.62) gave the best fitting model in the estimation of Value at Risk for BitCoin/dollar and Rand/dollar, respectively. The RiskMetrics approach seems to perform better at higher than lower confidence levels, as evidenced by higher p-values from backtesting using the Kupiec test at 99% than at 95% levels of significance. These findings are also helpful for risk managers in estimating adequate risk-based capital requirements for the two currencies.
- Keywords
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JEL Classification (Paper profile tab)C13, C22, C52, C58
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References44
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Tables6
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Figures2
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- Figure 1. Plot of BTC/USD prices (left) and one-day log returns (right)
- Figure 2. Plot of ZAR/USD prices (left) and one-day log returns (right)
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- Table 1. Error distribution functions
- Table 2. Descriptive statistics of exchange rate price returns
- Table 3. Optimal RiskMetrics estimate parameters for BTC/USD
- Table 4. Optimal RiskMetrics parameter estimates for ZAR/USD
- Table 5. VaR estimates
- Table 6. Kupiec’s test p-values
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- Bachelier, L. (1900). Theorie de la speculation. Annales scientifiques de l’écolenormale supérieure, 17, 21-86.
- Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 74(1), 3-30.
- BCBS. (2012). Fundamental Review of the Trading Book. Basel Committee on Banking Supervision. Consultative document. Basel: Basel Committee on Banking Supervision.
- BCBS. (2016). Minimum Capital Requirements for Market Risk. Basel Committee on Banking Supervision. Standards.
- Blau, B. M. (2017). Price dynamics and speculative trading in BitCoin. Research in International Business and Finance, 41, 493-499.
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
- Bollerslev, T. (1987). A Conditionally Heteroscedasticity Time Series Model for Speculative Prices and Rates of Return. The Review of Economics and Statistics, 69, 542-47.
- Bollerslev, T., & Mikkelsen, H. O. (1996). Modelling and pricing long-memory in stock market volatility. Journal of Econometrics, 73(1), 151-184.
- Bouri, E., Moln´ar, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of BitCoin: Is it really more than a diversifier? Finance Research Letters, 20, 192-198.
- Brailsford, T. J., & Faff, R. W. (1996). An evaluation of volatility forecasting techniques. Journal of Banking and Finance, 20, 419-438.
- Cheah, E.-T., & Fry, J. (2015). Speculative bubbles in BitCoin markets? An empirical investigation into the fundamental value of BitCoin. Economics Letters, 130, 32-36.
- Chen, J. M. (2018). On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles. Risks, 6, 61.
- Chifurira, R., & Chinhamu, K. (2019). Evaluating South Africa’s market risk using asymmetric power auto-regressive conditional heteroscedasticity model under heavy-tailed distributions. Journal of Economic and Financial Sciences, 12(1), a475.
- Danielsson, J., & Vries, C. G. (1997). Value-at-Risk and Extreme Returns. London School of Economics and Institute of Economic Studies, University of Iceland.
- Dasman, S. (2021). Analysis of return and risk of cryptocurrency BitCoin asset as investment instrument. In Accounting and Finance Innovations.
- Davidson, J. (2004). Moment and memory properties of linear conditional heteroscedasticity models, and a new model. Journal of Business and Economic Statistics, 22(1), 16-29.
- Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83-106.
- Dowd, K. (2014). New private monies: a bit-part player? (Hobart Paper 174). Institute of Economic Affairs Monographs.
- Dyhrberg, A. H. (2016). BitCoin, gold and the dollar–a Garch volatility analysis. Finance Research Letters, 16, 85-92.
- Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
- Engle, R. F., & Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews, 5(1), 1-50.
- 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.
- Hu, A. S., Parlour, C. A., & Rajan, U. (2019). Cryptocurrencies: Stylized facts on a new investible instrument. Financial Management, 48(4), 1049-1068.
- Hull, J. C. (2006). Risk Management and Financial Institutions (1st ed.). Prentice Hall.
- Jakata, O., & Chikobvu, D. (2019). Modelling extreme risk of the South African Financial Index (J580) using the generalised Pareto distribution. Journal of Economic and Financial Sciences, 12(1), a407.
- Kaseke, F., Ramroop, S., & Mhwambi, H. (2021). A Comparison of the Stylised Facts of BitCoin, Ethereum and the JSE Stock Returns. African Finance Journal, 23(2), 50-64.
- Katsiampa, P., Corbet, S., & Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. Finance Research Letters, 29, 68-74.
- Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk management models. Journal of Derivatives, 3(2), 73-84.
- Makatjane, K., & Moroke, N. (2021). Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange – All Share Index. International Journal of Financial Studies, 9, 18.
- Markowitz, H. M. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons.
- McMillan, D. G., & Kambouroudis, D. (2009). Are RiskMetrics forecasts good enough? Evidence from 31 stock markets. International Review of Financial Analysis, 18, 117-124.
- McMillan, D. G., Speight, A., & Apgwilym, O. (2000). Forecasting UK stock market volatility: A comparative analysis of alternate methods. Applied Financial Economics, 10(4), 435-448.
- Mina, J., & Xiao, J. (2001). Return to RiskMetrics: The Evolution of a Standard. RiskMetrics group.
- Morgan, J. P. (1996) Risk Metrics – Technical Document. New York: J.P. Morgan/Reuters.
- Ndlovu, T., & Chikobvu, D. (2022). Comparing riskiness of exchange rate volatility using the Value at Risk and Expected Shortfall methods. Investment Management and Financial Innovations, 19(2), 360-371.
- Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370.
- Paolella, M. S. (2016). Stable-GARCH models for financial returns: Fast estimation and tests for stability. Econometrics, 4(25), 1-28.
- Shanaev, S., & Ghimire, B. (2021). A fitting return to fitting returns: Cryptocurrency distributions revisited.
- So, M., & Yu, P. (2006). Empirical analysis of GARCH models in value at risk estimation. Journal of International Financial Markets, Institutions and Money, 16, 180-197.
- Tabasi, H., Yousefi, V., Tamošaitiene, J., & Ghasemi, F. (2019). Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models. Administrative Sciences, 9(2), 40.
- Takaishi, T. (2018). Statistical properties and multifractality of BitCoin. Physica A: statistical mechanics and its applications, 506, 507-519.
- Tse, Y. K. (1998). The conditional heteroskedasticity of the yen–dollar exchange rate. Journal of Applied Econometrics, 193(1), 49-55.
- Zhang, C., Zhang, Y., Cucuringu, M., & Qian, Z. (2022). Volatility forecasting with machine learning and intraday commonality. Quantitative Finance.
- Zhang, W., Wang, P., Li, X., & Shen, D. (2018). Some stylized facts of the cryptocurrency market. Applied Economics, 50(55), 5950-5965.
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Testing for explosive bubbles in the South African-US exchange rate using the sequential ADF procedures
Banks and Bank Systems Volume 12, 2017 Issue #1 (cont.) pp. 105-112 Views: 1173 Downloads: 502 TO CITE АНОТАЦІЯThis paper tests for the existence of speculative bubbles in the South African-US exchange rate using the sequential ADF procedures. In particular, the paper uses the SADF and GSADF right-tailed unit root tests to explore the existence of explosive bubbles in the South African-US exchange rate for the time period running from January1980 through July 2012. The results provide evidence in support of the existence of explosive bubbles in the nominal rand-dollar exchange rate, the real exchange rate of traded and non-traded goods. The explosive behavior exhibited by the South African rand-US dollar exchange rate can be interpreted as evidence of rational bubbles given that this behavior is driven by the fundamentals including relative prices of traded and non-traded goods.
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Rajesh Mamilla ,
Mehul Mehta ,
Abhijay Shukla ,
Piyush Agarwal
doi: http://dx.doi.org/10.21511/imfi.16(2).2019.27
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doi: http://dx.doi.org/10.21511/imfi.18(3).2021.33
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