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Bayesian analysis of long memory stochastic volatility models. Sankhya: The Indian (2002)
| Content Provider | CiteSeerX |
|---|---|
| Author | So, K. P. |
| Abstract | SUMMARY. Recent studies demonstrate that volatility exhibits long-range depen-dence. This article investigates a long memory stochastic volatility model in which the stochastic process governing the volatility is an Autoregressive Fractionally Integrated Moving Average process. Bayesian estimation via Monte Carlo Markov Chain sampling methods is proposed. Besides, Bayesian prediction and smoothing are introduced in the article. The methodologies are applied to daily returns data for illustration. 1. |
| File Format | |
| Journal | Journal of Statistics |
| Language | English |
| Publisher Date | 2002-01-01 |
| Access Restriction | Open |
| Subject Keyword | Long Memory Stochastic Volatility Model Bayesian Analysis Recent Study Bayesian Prediction Daily Return Data Monte Carlo Markov Chain Stochastic Process Bayesian Estimation Long-range Depen-dence |
| Content Type | Text |
| Resource Type | Article |
