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Modeling Stochastic Volatility with Application to Stock Returns [electronic resource] Krichene, Noureddine.

Author/Creator:
Krichene, Noureddine.
Publication:
Washington, D.C. : International Monetary Fund, 2003.
Format/Description:
Government document
Book
1 online resource (27 p.)
Series:
IMF eLibrary
IMF Working Papers; Working Paper No. 03/125.
IMF Working Papers; Working Paper No. 03/125
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Local subjects:
Autocorrelation. (search)
Bayes theorem. (search)
Bayesian analysis. (search)
Computation. (search)
Conditional expectation. (search)
Correlation. (search)
Covariance. (search)
Cumulative distribution function. (search)
Diffusion process. (search)
Diffusion processes. (search)
Econometric analysis. (search)
Econometrics. (search)
Economic models. (search)
Economic statistics. (search)
Equation. (search)
Equations. (search)
Fitted model. (search)
Forecasting. (search)
Gaussian distribution. (search)
Goodness of fit. (search)
Heteroscedasticity. (search)
Integral. (search)
Kurtosis. (search)
Logarithm. (search)
Markov chain. (search)
Markov chains. (search)
Markov process. (search)
Mathematical statistics. (search)
Maximum likelihood methods. (search)
Mean square. (search)
Monte Carlo Methods. (search)
Multivariate distribution. (search)
Normal density. (search)
Normal distribution. (search)
Normal distributions. (search)
Outliers. (search)
Parameter vector. (search)
Prediction. (search)
Probabilities. (search)
Probability. (search)
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Probability models. (search)
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Semiparametric and Nonparametric Methods. (search)
Skewness. (search)
Standard deviation. (search)
Standard errors. (search)
Statistic. (search)
Statistical analysis. (search)
Statistical methods. (search)
Statistical Simulation Methods. (search)
Statistics. (search)
Stock markets. (search)
Surveys. (search)
Time series. (search)
Time series analysis. (search)
Time series models. (search)
Time-Series Models. (search)
Grenada. (search)
United Kingdom. (search)
Summary:
A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was consistent with both volatility clustering and mean reversion. Filtering showed highly volatile markets, reflecting frequent pertinent news. Diagnostics showed no model failure, although specification improvements were always possible. The model corroborated stylized findings in volatility modeling and has potential value for market participants in asset pricing and risk management, as well as for policymakers in the design of macroeconomic policies conducive to less volatile financial markets.
Notes:
Description based on print version record.
Contributor:
Krichene, Noureddine.
Other format:
Print Version:
ISBN:
1451854846:
9781451854848
ISSN:
1018-5941
Publisher Number:
10.5089/9781451854848.001
Access Restriction:
Restricted for use by site license.