We propose a method to construct a proposal density for the Metropolis-Hastings algorithm in Markov Chain Monte Carlo (MCMC) simulations of the GARCH model. The proposal density is constructed adaptively by using the data sampled by the MCMC method itself. It turns out that autocorrelations between the data generated with our adaptive proposal density are greatly reduced. Thus it is concluded t...

Recent research suggests that long memory can be caused by regime switching and is easily confused with it. However, if the causes of confusion were properly controlled, they could distinguished. Motivated this idea, our study aims to distinguish between financial volatility. We firstly modeled volatility using Fractionally Integrated Exponential GARCH (FIEGARCH) Markov Regime-Switching EGARCH ...

Extreme value theory is widely used financial applications such as risk analysis, forecasting and pricing models. One of the major difficulties in the applications to finance and economics is that the assumption of independence of time series observations is generally not satisfied, so that the dependent extremes may not necessarily be in the domain of attraction of the classical generalised ex...

The hybrid Monte Carlo (HMC) algorithm is used for Bayesian analysis of the generalized autoregressive conditional heteroscedasticity (GARCH) model. The HMC algorithm is one of Markov chain Monte Carlo (MCMC) algorithms and it updates all parameters at once. We demonstrate that how the HMC reproduces the GARCH parameters correctly. The algorithm is rather general and it can be applied to other ...

This paper investigates and compares the performances of the optimal portfolio selected by using the Orthogonal GARCH (OGARCH) Model, Markov Switching Model and the Exponentially Weighted Moving Average (EWMA) Model in a fund of hedge funds. These models are used to calibrate the returns of four HFRX indices from which the optimal portfolio is constructed using the Mean-Variance method. The per...

We study the asymptotic behavior of kernel estimators of asymptotic variances (or long-run variances) for a class of adaptive Markov chains. The convergence is studied both in L and almost surely. The results apply to Markov chains as well and improve on the existing literature by imposing weaker conditions. We illustrate the results with applications to the GARCH(1, 1) Markov model and to an a...

Abstract: The aim of international economic sanctions is imposing economic restrictions on target countries. In order to decrease the sanctions negative brunt on citizenry and make it ineffective, government may respond to sanctions through policies such as increasing the supply of public goods. This paper studies the regime changes of government expenditures in Iranian economy in response to e...

In this paper, the effects of oil and gold prices on stock market index are investigated. We use a cointegrated vector autoregressive Markov-switching model to examine the nonlinear properties of these three variables during the period of January 2003 - December 2014. The Markov-switching vector-equilibrium-correction model with three regimes representing "deep recession", "mild recession" and ...

Traditional GARCH models describe volatility levels that evolve smoothly over time, generated by a single GARCH regime. However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes. Further, the number and times of regime changes are not always obvious. This article outlines a nonparametric mixture of GARCH models that is a...

We simulate daily trading of straddles on financial indexes. The straddles are traded based on predictions of daily volatility differences in the indexes. The main predictive models studied are recurrent neural nets (RNN). Such applications have often been studied in isolation. However, due to the special character of daily financial time-series, it is difficult to make full use of RNN represen...