The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility
Abstract
The paper develops an empirical return volatility‐trading volume model from a microstructure framework in which informational asymmetries and liquidity needs motivate trade in response to information arrivals. The resulting system modifies the so‐called “Mixture of Distribution Hypothesis” (MDH). The dynamic features are governed by the information flow, modeled as a stochastic volatility process, and generalize standard ARCH specifications. Specification tests support the modified MDH representation and show that it vastly outperforms the standard MDH. The findings suggest that the model may be useful for analysis of the economic factors behind the observed volatility clustering in returns.
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Cites
An introduction to probability theory and its applications
(1958) · Journal of the Franklin Institute · doi:10.1016/0016-0032(58)90688-4 · Cited by 29,269
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
Newey, W. K., & West, K. D. (1987) · Econometrica · doi:10.2307/1913610 · Cited by 16,936
Convergence of Probability Measures
Billingsley, P. (1999) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316962 · Cited by 13,910
Large Sample Properties of Generalized Method of Moments Estimators
Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix
Newey, W., & West, K. (1986) · National Bureau of Economic Research · doi:10.3386/t0055 · Cited by 12,278
Conditional Heteroskedasticity in Asset Returns: A New Approach
Nelson, D. B. (1991) · Econometrica · doi:10.2307/2938260 · Cited by 10,236
Convergence of Probability Measures
Kingman, J. F. C., & Billingsley, P. (1969) · Revue de l'Institut International de Statistique / Review of the International Statistical Institute · doi:10.2307/1402124 · Cited by 6,812
Bid, ask and transaction prices in a specialist market with heterogeneously informed traders
Glosten, L. R., & Milgrom, P. R. (1985) · Journal of Financial Economics · doi:10.1016/0304-405x(85)90044-3 · Cited by 6,115
Cited by
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
Self-citationForecasting Volatility in Financial Markets: A Review
Poon, S., & Granger, C. W. J. (2003) · Journal of Economic Literature · doi:10.1257/002205103765762743 · Cited by 1,315
Guillaume, D. M., Dacorogna, M. M., Davé, R. R., Müller, U. A., Olsen, R. B., & Pictet, O. V. (1997) · Finance and Stochastics · doi:10.1007/s007800050018 · Cited by 522
Model Uncertainty and Option Markets with Heterogeneous Beliefs
BURASCHI, A., & JILTSOV, A. (2006) · The Journal of Finance · doi:10.1111/j.1540-6261.2006.01006.x · Cited by 346
Portfolio Choice and Trading Volume with Loss‐Averse Investors
Gomes, F. J. (2005) · The Journal of Business · doi:10.1086/427643 · Cited by 281
Metadata
- Authors
- ANDERSEN, T. G.
- Venue
- The Journal of Finance
- Publisher
- Wiley
- Published
- 1996-03
- Volume
- 51
- Issue
- 1
- Pages
- 169-204
- ISSN
- 0022-1082 (ISSN-L)
- 1540-6261
- OpenAlex
- W2033815796
- Cited by
- 1,020
- References
- 36
- FWCI
- 31.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- ANDERSEN, T. G. (1996). Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility. The Journal of Finance. https://doi.org/10.1111/j.1540-6261.1996.tb05206.x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
