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Showing 2 results for Rostami

Sirous Soleyman, Ali Falahati, Alireza Rostami,
Volume 7, Issue 25 (10-2016)
Abstract

In this study by using Markov Regime Switching Heteroscedasticity Models (MRSH) in the form of state-space model the behavior of stock returns is examined. This approach endogenously permits the volatility to switch as the date and regime change and allows us to decompose the permanent and transitory component of stock returns. The period of the study is the fourth month of 2000 to the seventh month of 2013. The durations of the high-variance regimes for permanent components short-lived and revert to normal levels quickly and low variance regime for this components is more lasting, but durations of high-variance regime for transitory component is reverse. Also, in during periods of study low variance regime is dominant by a permanent component of stock returns but for the transitory component the high variance state is true captured.


Mojtaba Rostami, Seyed Nezamuddin Makiyan,
Volume 11, Issue 41 (10-2020)
Abstract

Volatility is a measure of uncertainty that plays a central role in financial theory, risk management, and pricing authority. Turbulence is the conditional variance of changes in asset prices that is not directly observable and is considered a hidden variable that is indirectly calculated using some approximations. To do this, two general approaches are presented in the literature of financial economics for modeling and calculating volatility. In the first approach, conditional variance is modeled as a function of the square of the past shocks of return on assets. Models of the GARCH type fall into this category. In the alternative approach, volatility is assumed to be a random variable, which evolves using nonlinear patterns of Gaussian state space. This type of model is known as Stochastic Volatility (SV).  Because, SV models include two kinds of noise processes, one for observations and another for hidden, volatility, thus, they are more realistic and more flexible in calculating volatility than GARCH type.  This study attempts to analyze the volatility in stock returns of 50 companies, which are active in Tehran Stock Market using symmetric and asymmetric methods of Stochastic Volatility, which is different in the presence of leverage effect. The empirical comparison of these two models by calculating the posterior probability of accuracy of each model using the MCMC Bayesian method represents a significant advantage of the ASV model. The results in both symmetric and asymmetric methods represent the very high stability of the volatility generated by the shocks on stock returns; therefore, the Tehran Stock market changes in returns due to this high sustainability will be predictable.


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