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In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating...
Persistent link: https://www.econbiz.de/10005556396
Kim, Shephard and Chib (1998) provided a Bayesian analysis of stochastic volatility models based on a very fast and reliable Markov chain Monte Carlo (MCMC) algorithm. Their method ruled out the leverage effect, which limited its scope for applications. Despite this, their basic method has been...
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In this paper we study the detailed distributional properties of integrated non-Gaussian OU (intOU) processes. Both exact results and approximate results are given. We emphasise the study of the tail behaviour of the intOU process. Our results have many potential applications in financial...
Persistent link: https://www.econbiz.de/10005812247
The availability of intra-data on the prices of speculative assets means that we can use quadratic variation like measures of activity in financial markets, called realised volatility, to study the stochastic properties of returns. Here we derive the moments and the asymptotic distribution of...
Persistent link: https://www.econbiz.de/10005812268
In this paper we study the reliability of the mixed normal asymptotic distribution of realised volatility error, which we have previously derived using the theory of realised power variation. Our experiments suggests that the asymptotics is reliable when we work with the logarithmic transform of...
Persistent link: https://www.econbiz.de/10005549199
In this paper we provide an asymptotic distribution theory for some non-parametric tests of the hypothesis that asset prices have continuous sample paths. We study the behaviour of the tests using simulated data and see that certain versions of the tests have good finite sample behaviour. We...
Persistent link: https://www.econbiz.de/10005730260
This paper provides limit distribution results for power variation, that is sums of powers of absolute increments, for certain types of time-changed Brownian motion and $\alpha $-stable processes. Special cases of these processes are stochastic volatility models used extensively in financial...
Persistent link: https://www.econbiz.de/10005730319