Comparison of profitability of speculation in the foreign exchange market and investment in Tehran Stock Exchange during Iran's currency crisis using conditional Sharpe ratio
محورهای موضوعی : Financial EconomicsMohsen Mehrara 1 , Saeid Tajdini 2
1 - Department of Theoretical Economics, Faculty of Economics, University of Tehran, Tehran, Iran
2 - Department of Theoretical Economics, Faculty of Economics, University of Tehran, Tehran, Iran
کلید واژه: conditional risk, exchange rate, conditional Sharpe ratio, dynamic condition correlation,
چکیده مقاله :
In the first nine months of 2018, the triple increase of dollar price made the stock market an attractive place for speculation, especially for non-professional investors. Hence, this study was aimed to investigate the profitability of speculation in the foreign exchange market (dollar) and to compare it with investment in three indices of sugar, oil products, and basic metals. First, the conditional Sharpe ratio was calculated separately for these four assets. Then, six investment portfolios were developed for these four assets. The results showed although dollar speculation with mean daily return of 0.6% had the highest return among the ten investment assets, dollar speculation was ranked last, or tenth (0.096) in terms of performance and profitability by considering the standard deviation or daily conditional risk using conditional Sharpe ratio. Moreover, the results indicated that from among the six portfolios with equal weight, three investment portfolios consisting of merely Tehran Stock Exchange indices had a better performance than three investment portfolios comprising dollar speculation and each stock exchange index. It was also found that the risk of lack of capital diversification by investors was higher than that of accepting a higher-level risk.
[1] Abbasinejad, H., Mohammadi Sh., Ebrahimi S., Dynamics of the Relation between Macroeconomic Variables and Stock Market Index, Journal of Asset Management and Financing, 2017, 5( 1 ), P. 61-82.(In Persian)
[2] Abdelaal, M. A., Modelling and forecasting time varying stock return volatility in the Egyptian stock market, International Research Journal of Finance and Economics, 2011, 78, P.96–113.
[3] Andreea–Cristina, P., Stelian, S., Empirical results of modeling EUR/RON exchange rate using ARCH, GARCH, EGARCH, TARCH and PARCH models, Romanian Statistical Review, 2017, 65(1), P. 57–72.
[4] Aliakbarpoor, Z., Izadikhah, M., Evaluation and ranking DMUs in the presence of both undesirable and ordinal factors in data envelopment analysis, International Journal of Automation and Computing, 2012, 9, P. 609–615, Doi: 10.1007/s11633-012-0686-5
[5] Bailey, D., Lopez de Prado, M., The Strategy Approval Decision: A Sharpe Ratio Indifference Curve approach, Algorithmic Finance, 2013, 2(1), P. 99-109
[6] Barillas, F., Kan, R., Robotti, C., Shanken, J. A., Model comparison with sharpe ratios, Rotman School of Management Working, 2017, Paper No. 3013149.
[7] Bailey, D., López de Prado, M., The sharpe ratio efficient frontier.Journal of Risk, 2012, 15(2), P.3–44.
[8] Bernanke, B., Gertler, M., Monetary policy and asset price volatility, in New Challenges for Monetary Policy:A Symposium Sponsored by the Federal Reserve Bank of Kansas City, 1999, P. 77-128.
[9] Bernardo, A. E., Ledoit, O., Gain, loss and asset pricing.Journal of Political Economy, 2000, 108(1), P.144–172. Doi:10.1086/262114.
[10] Black, F., Studies of stock price volatility changes. Proceedings of the business and economics section of the american statistical association, Washington, DC, 1976, P. 177–181.
[11] Bollerslev, T., Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 1986, 31(3), P. 307-327.
[12] Bollerslev, T. Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCHApproach, Review of Economics and Statistics, 1990, 72, P.498-505.
[13] Brooks, C., Kat, H. M., The statistical properties of hedge fund index returns and their implications for investors, Journal of Alternative Investments, 2002, 5(2), P. 26–44. Doi:10.3905/jai.2002.319053.
[14] Celık, S., The more contagion effect on emerging markets: The evidence of DCC-GARCH model, Economic Modelling, 2012, 29(5), P. 946-1959.
[15] Coffie, W., Tackie, G., Bedi, I. F., Aboagye-Otchere. Alternative Models for the Conditional Hetroscedasticity and the Predictive Accuracy of variance Models_Emprical Evidence from East and North Africa Stock Markets, Journal of Accounting and Finance, 2017, 17(2).
[16] Constanza, M., Manuel, R., Dynamic Conditional Correlation in Latin-American Asset Markets, Serie Documentos De Trabajo, 2011, 107.
[17] Dritsaki, Ch., An mpirical Evaluation in GARCH Volatility Modeling: Evidence from the Stockholm Stock Exchange. Journal of Mathematical Finance, 2017, 7, P. 366-390.
[18] Engle, R., Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 1982, 50, P. 987–1007.
[19] Engle, R., Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models, 2002, P. 1-9.
[20] Engle, R., Sheppard, K., Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH, Working paper, 2001, P. 1- 25.
[21] Fallahi, F., Haghighat, J., Sanoubar, N., Jahangiri, KH. Study of Correlation Between Volatility of Stock Exchange and Gold Coin Markets in Iran with DCC-GARCH Model. Economic Research Review, 2014, 14(52), P. 23 -147.
[22] French, K., Schwert, W., Stambaugh, R., Expected stock returns and volatility, Journal of Financial Economics, 1987, 19, P. 3-29.
[23] Glosten, L., Jagannathan, R. Runke, D., Relationship between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance, 1993, 48, P. 1779– 1801.
[24] Guo, Z., Models with short-term variations and long-term dynamics in risk management of commodity derivatives, EconStor Preprints 167619, ZBW - Leibniz Information Centre for Economics, 2017,a.
[25] Guo, Z-Y., GARCH Models with the heavy-tailed Distributions and the Hong Kong Stock Market Returns, International Journal of Business and Management, 2017, b 12(9)
[26] Intaz, A., Subhrabaran, D., Niranjan R., Stock Market Volatility, Firm Size and Returns: A Study of Automobile Sector of National Stock Exchange in India, International Journal of Innovative Research and Development, 2016, 5(4), P. 272-281.
[27] Hwang, j.K., Dynamic Correlation Analysis of Asian Stock Markets, International Advanced Economics Research, 2012, 18, P. 227-237.
[28] Jones, P. M., O’Steen, H., Time-varying correlations and Sharpe ratios during quantitative easing, Studies in Nonlinear Dynamics and Econometrics. 2018, 22(1), Article number 20160083.
[30] Liu, H-ChunLiua., Hung, Jui-ch., Forecasting SandP-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models, Expert Systems with Applications, 2010, 37(7), P. 4928-4934.
[31] Longin, F., Solnik, B., Is the correlation in international equity returns constant:1960-1990? Journal of International Money and Finance,1995, 14 (1), P. 3-26, 1995.
[32] Mohammadi, M., Didar H., Mansourfar GH., Comparison of the Behavior of International Optimized Portfolios based on Constant and Dynamic Conditional Correlation approaches, 2013, 1( 1), P. 75-92.
[33] Moghaddam, M. R., Sezavar M. R., Investigating Conditional Correlation between International Capital Markets and the Oil Market with the Tehran Stock Exchange, Quarterly Energy Economics Review, 2015, 12(48).
[34] Muntazir H, Gilney F, Z, Usman B, Ding D., Oil price and exchange rate co-movements in Asian countries: Detrended cross-correlation approach, 2017, 465(1) P. 338-346.
[35] Nelson, D.B., Cao, C.Q., Inequality Constraints in the Univariate GARCH Model, Journal of Business and Economic Statistics, 1992, 10(3), P. 229-235.
[36] Panda, A. K., Nanda S., Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economies. Department of Accounts and Finance, National Institute of Industrial Engineering, Mumbai, India ,2018.
[37] Park, S. Y., Ryu, D., Song, J., The dynamic conditional relationship between stock market returns and implied volatility, Elsevier, September 2017, 482(15) P.638-648.
[38] Poon S.-H., Granger, C., Forecasting volatility in financial markets: areview, Journal of Economic Literature, XLI,2003, P. 478-539.
[39] Rai, Reza., The Design of an Investment Model suitable for Portfolio using Artificial Intelligence (Neural Networks). PhD Thesis submitted to the Faculty ofManagement of Tehran University.1998.
[40] Robiyanto, R., The dynamic correlation between ASEAN-5 stock markets and world oil prices, Jurnal Keuangan dan Perbankan,2018, 22(2), P. 198–210. Doi:10.26905/jkdp. v22i2.1688.
[41] Robiyanto, R., Indonesian Stock Market’s Dynamic Integration with Asian Stock Markets and World Stock Markets. Jurnal Pengurusan, 2018, 52, P.181–192. Doi:10.17576/pengurusan
[42] Sarkar, S., Banerjee, A., Modeling daily volatility of the Indian stock market using intra-day data, Indian Institute of Management Calcutta, Working Paper Series, 2006, P.1- 32.
[43] Sharpe, W., Mutual fund performance. J. Bus. 1966, 39 (1), P. 119–138.
[44] Sharpe, W., Winter. Adjusting for risk in portfolio performance measurement. J. Portf. Manag, 1975, 1 (2), P. 29–34.
[45] Sharpe, W., Fall. The Sharpe ratio. J. Portf. Manag. 1994, 21 (1), P. 49–58.
[46] Syllignakis, M. N., Kouretas, G. P., Dynamic correlation analysis of financial contagion: Evidence from the Central and Eastern European markets, International Review of Economics and Finance, 2011, 20 (4), P.717-732.
[47] Syriopoulos, T. Roumpis, E., Dynamic correlations and volatility effects in the Balkan equity markets, Journal of International Financial Markets, Institutions and Money ,2009, 19(4), P. 565-587.
[48] Tajdini, S., Mehrara, M., Tehrani, R., Double-sided balanced conditional Sharpe Ratio, Cogent Economic and Finance, 2019,7(1).
[49] Tse, Y.K., Tsui, A. K.C, A multivariate GARCHmodel with time-varying correlations, Journal of Business and Economic Statistics, 2002, 20, P.351-362.
[50] Wen, F., Xiao, J., Huang, Ch., Xia, X., Interaction between oil and US dollar exchange rate: nonlinear causality, time-varying influence and structural breaks in volatility. Applied Economics, 2018, 50( 3), P.319-334.
[51] Yoshihiko, T., Junji, Sh., Bond market integration in East Asia: Multivariate GARCH with dynamic conditional correlations approach, Elsevier, 2017, 51, P.193-213.
[52] Zhang, X., Modeling and simulation of value at risk in the finance Market area, louisiana tech university, ProQuest Dissertations,2006.
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[55] www.tse.ir/archive.html