The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
A forecast comparison of volatility models: does anything beat a GARCH(1,1)?
Open accessAbstract
We 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. Copyright © 2005 John Wiley & Sons, Ltd.
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Cites
Generalized autoregressive conditional heteroskedasticity
Bollerslev, T. (1986) · Journal of Econometrics · doi:10.1016/0304-4076(86)90063-1 · Cited by 22,254
Engle, R. F. (1982) · Econometrica · doi:10.2307/1912773 · Cited by 20,795
Diebold, F., & Mariano, R. (1994) · National Bureau of Economic Research · doi:10.3386/t0169 · Cited by 5,185
Measuring and Testing the Impact of News on Volatility
ENGLE, R. F., & NG, V. K. (1993) · The Journal of Finance · doi:10.1111/j.1540-6261.1993.tb05127.x · Cited by 3,611
Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts
Andersen, T. G., & Bollerslev, T. (1998) · International Economic Review · doi:10.2307/2527343 · Cited by 3,375
A long memory property of stock market returns and a new model
Ding, Z., Granger, C. W. J., & Engle, R. F. (1993) · Journal of Empirical Finance · doi:10.1016/0927-5398(93)90006-d · Cited by 3,250
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Diebold, F. X., & Mariano, R. S. (1995) · Journal of Business and Economic Statistics · doi:10.2307/1392185 · Cited by 2,340
Nonparametric Regression and Generalized Linear Models
Green, P. J., & Silverman, B. W. (1994) · Springer eBooks · doi:10.1007/978-1-4899-4473-3 · Cited by 1,925
A Reality Check for Data Snooping
White, H. (2000) · Econometrica · doi:10.1111/1468-0262.00152 · Cited by 1,851
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Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards
Antweiler, W., & Frank, M. Z. (2004) · The Journal of Finance · doi:10.1111/j.1540-6261.2004.00662.x · Cited by 2,373
Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns
Cappiello, L., Engle, R. F., & Sheppard, K. (2006) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbl005 · Cited by 1,767
A Test for Superior Predictive Ability
Hansen, P. R. (2005) · Journal of Business & Economic Statistics · doi:10.1198/073500105000000063 · Cited by 1,230
Self-citationValue-at-Risk Prediction: A Comparison of Alternative Strategies
Kuester, K. (2005) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbj002 · Cited by 632
Risk Everywhere: Modeling and Managing Volatility
Bollerslev, T., Hood, B., Huss, J., & Pedersen, L. H. (2018) · The Review of Financial Studies · doi:10.1093/rfs/hhy041 · Cited by 359
Metadata
- Authors
- Hansen, P. R., & Lunde, A.
- Venue
- Journal of Applied Econometrics
- Publisher
- Wiley
- Published
- 2005-03-30
- Volume
- 20
- Issue
- 7
- Pages
- 873-889
- ISSN
- 0883-7252 (ISSN-L)
- 1099-1255
- OpenAlex
- W3122175640
- Cited by
- 1,773
- References
- 41
- FWCI
- 74.97
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
