Stock Index Volatility Forecasting with High Frequency Data

Hol, Eugenie M. J. H.

doi:10.1007/978-1-4757-5129-1_7

Cited by 6 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Boscher et al (2000) and Hol & Koopman (2002) show that in some empirical studies SV models make better forecasts than GARCH models do. Bollerslev et al (1992), Bauens et al (2004), Taylor (1994), and Shephard (1996), among others, survey the literature on GARCH and SV models.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search

Loddo¹,

Sun³

2011

Journal of Business & Economic Statistics

View full text Add to dashboard Cite

We propose a Bayesian stochastic search approach to selecting restrictions on multivariate regression models where the errors exhibit deterministic or stochastic conditional volatilities. We develop a Markov Chain Monte Carlo (MCMC) algorithm that generates posterior restrictions on the regression coefficients and Cholesky decompositions of the covariance matrix of the errors.Numerical simulations with artificially generated data show that the proposed method is effective in selecting the data-generating model restrictions and improving the forecasting performance of the model. Applying the method to daily foreign exchange rate data, we conduct stochastic search on a VAR model with stochastic conditional volatilities.

show abstract

mentioning

confidence: 99%

“…Numerical simulations demonstrate that restricted models obtained via stochastic search outperforms the unrestricted model in out-of-sample forecasting. Boscher et al (2000) and Hol & Koopman (2002) compare several models with time varying variances, but the number of candidate models in this study is much larger.…”

mentioning

confidence: 99%

Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search

Loddo¹,

Sun³

2011

Journal of Business & Economic Statistics

View full text Add to dashboard Cite

show abstract

“…This paper introduces realized volatility into GARCH model, and a new volatility model named RV-GARCH is constructed. Our work is also built on the former work of Blair et al (2001), and Hol and Koopman (2002), in which the lagged realized volatility is introduced into the variance equation of GARCH model. The variance equation of RV-GARCH model can be transformed to,…”

Section: φ(L)y T = θ(L)ε Tmentioning

confidence: 99%

“…This paper introduces realized volatility to conditional volatility model, and a RV-GARCH model is constructed. Our work is also built on the former work of Blair et al (2001), Hol and Koopman (2002). A RV-EGARCH model that combines with RV and EGARCH model is also introduced in this paper to capture asymmetric dynamics.…”

Section: Introductionmentioning

confidence: 99%

The role of asymmetry: Evidence from Chinese Treasury bond market

Tan

Tian

2009

Statistics and Its Interface

View full text Add to dashboard Cite

Volatility asymmetry widely exists in the stock and exchange market, but little evidence has been provided in the bond market, especially the Treasury bond market with fewer impacts related to private information. This paper provides an empirical study on the volatility asymmetry in the Chinese Treasury bond market (CTBM). The question what we are mostly interested is the role of asymmetry in volatility modeling in the Treasury bond market, including econometric testing of asymmetry and analyzing of its effect on volatility forecasting. Quite different from previous results in the stock or exchange market, by estimating varied volatility models, empirical results in this paper show that volatility asymmetry is insignificant in CTBM, but it also provides important and indispensable information for volatility forecasting. Due to the special pricing way of Treasury bonds as well as low liquidity in CTBM as a result of non-actively trading, the fact that information-based impacts in CTBM are not as common as that in the stock or exchange market may contribute to these results.

show abstract

A forecast comparison of volatility models: does anything beat a GARCH(1,1)?

Hansen

Lunde

2005

J of Applied Econometrics

1,397

595

View full text Add to dashboard Cite

SUMMARYWe compare 330 ARCH-type models in terms of their ability to describe the conditional variance. The models are compared out-of-sample using DM-$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is outperformed by more sophisticated models in our analysis of exchange rates, whereas the GARCH(1,1) is clearly inferior to models that can accommodate a leverage effect in our analysis of IBM returns. The models are compared with the test for superior predictive ability (SPA) and the reality check for data snooping (RC). Our empirical results show that the RC lacks power to an extent that makes it unable to distinguish 'good' and 'bad' models in our analysis.

show abstract

Stock Index Volatility Forecasting with High Frequency Data

Cited by 6 publications

References 21 publications

Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search

Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search

The role of asymmetry: Evidence from Chinese Treasury bond market

A forecast comparison of volatility models: does anything beat a GARCH(1,1)?

Contact Info

Product

Resources

About