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The effect of volatility temporal changes on the predictability and return of optimal portfolio using the DMA model | ||
| Journal of Mathematics and Modeling in Finance | ||
| دوره 1، شماره 2، اسفند 2021، صفحه 1-11 اصل مقاله (219.73 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2021.13835 | ||
| نویسندگان | ||
| Fatemeh Samadi* 1؛ Hossein Eslami Mofid Abadi2 | ||
| 1Department of Accounting and Management, East Tehran Branch, Islamic Azad University, East Tehran, Iran | ||
| 2Department of Accounting and Management, Shahryar Branch, Islamic Azad University, Shahryar, Iran | ||
| چکیده | ||
| According to most financial experts, it is not possible to study the predictability of stock prices without considering the risks affecting stock returns. On the other hand, identifying risks requires determining the share of risk in the total risk and the probability of risk occurrence in different regimes. Accordingly, different DMA models with full dynamics compared to TVP-BMA, BMA and TVP models have been used in the present study to provide this predictability. Findings showed that the DMA model is more efficient than other research models based on MAFE and MSFE indices. The present research was conducted in the period of 1-2003 to 12-2013 (including 144 periods) and was implemented in MATLAB 2014 software space. As the research results show, the bank interest rate coefficient in 45 periods, the rst lag rate of the bank in terest rate in 37 periods, the in ation rate coefficient in 17 periods, rst lag coefficient of in ation rate in 26 periods, oil price coefficient in 78 periods, rst lag rate of oil price in 85 periods, exchange rate coefficient in 64 periods and rst lag rate of the exchange rate in 35 periods have a signi cant effect on stock returns. The nal conclusion shows that the stock variables of oil price and the exchange rate had the highest impact on stock returns during the studied period. | ||
| کلیدواژهها | ||
| Volatility Temporal؛ Predictability؛ Optimal Portfolio؛ Stock Returns؛ DMA Model | ||
| مراجع | ||
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[1] A. Ang, G. Bekaert, and M. Wei, Do Macro Variables, Asset Markets, or Surveys Forecast In ation Better? Journal of Monetary Economics, 54 (2007), pp.1163-1212. [2] Fruhwirth-Schnatter, Finite Mixture and Markov Switching Models, NewYork: Springer, 2006. [3] A. Garratta, J. Mitchellb, and P. Shaun, Real-time in ation forecast densities from ensemble Phillips curves, North American Journal of Economics and Finance, 22 (2011), pp.78-88. [4] J. Geweke, and G. Amisano, Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns, Journal of Applied Econometrics forthcoming, 26 (2011), pp.1-29. [5] J. Groen, R. Paap, and F. Ravazzolo, Real-time In. action Forecasting in a Changing World, Econometric Institute Report, Erasmus University Rotterdam,2009. [6] J.D. Hamilton, Oil and the macro-economy, In: Durlauf, S., Blume, L. (Eds.), The New Palgrave Dictionary of Economics, 2nd Ed. Palgrave Mac Milan Ltd, 2009. [7] J.D. Hamilton, and R. Susmel, Autoregressive conditional heteroscedasticity and changes in regime Journal of Econometrics, 64(1994), pp.307-333. [8] O. Henry, Regime switching in the relationship between equity returns and short-term interest rates Journal of Banking and Finance, 33 (2009), pp.405-414. [9] L. Hoogerheide, R. Kleijn, F. Ravazzolo, H. van Dijk, and M. Verbeek, Forecast Accuracy and Economic Gains from Bayesian Model Averaging using Time-Varying Weights, Tinbergen Institute Discussion Paper, 2009-061/4, 2009. [10] A. Hornstein, Introduction to the New Keynesian Phillips Curve Economic Quarterly, 94 (2009), pp.301-309. [11] Y. Hwang, Causality between in ation and real growth Economics Letters, 94 (2009),146-153. [12] R. Jammazi, and C. Aloui, Wavelet Decomposition and Regime Shifts: As- sessing the Effects of Crude Oil Shocks on Stock Market Returns, Energy Policy. 38 (2010), pp.1415-1435. [13] J. Nakajima, K. Munehisa, and W. Toshiaki, Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy, Journal of The Japanese and International Economies, 25 (2011), pp.225-245. [14] R.G. King, The Phillips Curve and U. S. Macroeconomic Policy: Snapshots, 1958-1996, Economic Quarterly, 94 (2008), pp.311-359. [15] G. Koop, and D. Korobilis, Forecasting In ation using Dynamic Model Av-eraging, 2010. [16] G. Koop, R. Leon-Gonzalez, R. Strachan, On the Evolution of the Monetary Policy Transmission Mechanism, Journal of Economic Dynamics and Control, 33 (2009), pp.997-1017. [17] D. Korobilis, Assessing the Transmission of Monetary Policy Shocks using Dynamic Factor Models Discussion Paper 9 14, University of Strathclyde, 2009. [18] S. Mazumder, Cost-based Phillips Curve forecasts of in ation, Journal of Macroeconomics, 33 (2011), pp. 553-567. [19] S. Moser, and F. Rumler, Forecasting Austrian in ation, Economic Modeling, 24 (2007) pp.470-480. [20] J. Nakajima, W. Toshiaki, Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy, Journal of The Japanese and International Economies, 25 (2005), pp.225-245. [21] G. Primiceri, Time Varying Structural Vector Auto regressions and Monetary Policy, Review of Economic Studies, 72 (2005), pp.821-852. [22] A. Raftery, M. Karny, and P. Ettler, Online Prediction Under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill,Technimetrics, 52 (2010), pp.52-66. [23] J. Stock, and M. Watson, Phillips Curve In. acion Forecast, NBER Working Paper, 2008. [24] J. Stock, and M. Watson, and U.S. Why Has, In ation Become Harder to Forecast, Journal of Monetary Credit and Banking, 39 (2008), pp.3-33. | ||
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