This paper presents a Markov chain Monte Carlo based estimation procedure for the COGARCH(1,1) model driven by a compound Poisson process. The COGARCH model is a continuous-time analogue to the discrete-time GARCH model and captures many of the stylized facts of financial time series, as has been shown in various papers. Principles for the estimation of point processes by MCMC are adapted to th...

this paper examines the impact of 2005 presidential election of iran on the tehran stock exchange volatility as a political shock. it uses garch family (fiegarch, egarch, and garch) and markov regime switching (mrs) models as the analytical frameworks for the main the stock daily prices index. our findings confirm statistical validity of arima – fiegarch-x and ar(1) mrs as appropriate specifica...

We analyze the time-dependence of exchange rate correlations using a new multivariate GARCH model. This model consists of two parts. First, we transform the exchange rate changes into their principal components and specify univariate GARCH models for all components. Second, we use the inverse of the principal components construction to transform the conditional component moments back into those...

Following Harrison and Kreps (1979) and Harrison and Pliska (1981), the valuation of contingent claims in continuous-time and discrete-time finite state space settings is generally based on the no-arbitrage principle, and the use of an equivalent martingale measure. In contrast, for some of the most popular discrete time processes used in finance, such as GARCH processes, the existing literatur...

We examine the joint predictability of return and cash flow within a present value framework, by imposing the implications from a long-run risk model that allow for both time-varying volatility and volatility uncertainty. We provide new evidence that the expected return variation and the variance risk premium positively forecast both short-horizon returns and dividend growth rates. We also conf...

We employ a multivariate BEKK GARCH model which allows news to affect conditional volatility in an asymmetric manner. The asymmetric model outperforms the standard symmetric model, implying that efficient financial decision makers should not treat good and bad news as homogenous. We estimate the conditional variance and covariance of the Japanese yen, Swiss franc and British pound vis-à-vis the...

This paper introduces a conditional extreme value volatility estimator (EVT) based on highfrequency returns. The relative performance of the EVT is compared with the discrete-time GARCH and implied volatility models for 1-day and 20-day-ahead forecasts of realized volatility. This is also a first attempt towards detecting any time-series variation in extreme value distributions using high-frequ...

This note investigates impacts of multivariate generalised autoregressive conditional heteroskedasticity (GARCH) errors on hypothesis testing for cointegrating vectors. The study reviews a cointegrated vector autoregressive model incorporating multivariate GARCH innovations and a regularity condition required for valid asymptotic inferences. Monte Carlo experiments are then conducted on a test ...

This paper presents a GARCH type volatility model with a time-varying unconditional volatility which is a function of macroeconomic information. It is an extension of the SPLINE GARCH model proposed by Engle and Rangel (2005). The advantage of the model proposed in this paper is that the macroeconomic information available (and/or forecasts) is used in the parameter estimation process. Based on...

Article history: Received 10 May 2013 Received in revised form 2 September 2014 Accepted 2 September 2014 Available online 11 September 2014 The study investigates the relative performance of Value-at-Risk (VaR) models using daily share price index data from six different countries across Asia, Europe and the United States for a period of 10 years from January 01, 2000 toDecember 31, 2009. Them...