Local Likelihood Estimators in a Regression Model for Stock Returns
Abstract: We consider a non-stationary regression type model for stock returns in which the innovations are described by four-parameter distributions and the parameters are assumed to be smooth, deterministic functions of time. Incorporating also normal distributions for modelling the innovations, our model is capable of adapting to light-tailed innovations as well as to heavy-tailed ones. Thus, it turns out to be a very flexible approach. Both, for the fitting of the model and for forecasting the distributions of future returns, we use local likelihood methods for estimation of the parameters. We apply our model to the S&P 500 return series, observed over a period of twelve years. We show that it fits these data quite well and that it yields reasonable one-day-ahead forecasts
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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Postprint
begutachtet (peer reviewed)
In: Quantitative Finance ; 8 (2008) 6 ; 619-635
- Classification
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Wirtschaft
- DOI
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10.1080/14697680701656181
- URN
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urn:nbn:de:0168-ssoar-221111
- Rights
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Open Access unbekannt; Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.01.2202, 6:29 AM CET
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
Time of origin
- 2008
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Local Likelihood Estimators in a Regression Model for Stock Returns
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Tail Probabilities for Regression Estimators
Specification tests for non-Gaussian maximum likelihood estimators
The Relative Performance of Targeted Maximum Likelihood Estimators
Circular local likelihood
Response smoothing estimators in binary regression
Relative efficiency of maximum likelihood and other estimators in a nonlinear regression model with small measurement errors
Influence functions of trimmed likelihood estimators for lifetime experiments
Local Likelihood Estimators in a Regression Model for Stock Returns
Local regression and likelihood
Empirical likelihood estimators for the error distribution in nonparametric regression models
Empirical likelihood estimators for the error distribution in nonparametric regression models
Maximum likelihood estimators in linear regression models with Ornstein-Uhlenbeck process
Tail Probabilities for Regression Estimators
Specification tests for non-Gaussian maximum likelihood estimators
The Relative Performance of Targeted Maximum Likelihood Estimators
Circular local likelihood
Response smoothing estimators in binary regression
Relative efficiency of maximum likelihood and other estimators in a nonlinear regression model with small measurement errors
Influence functions of trimmed likelihood estimators for lifetime experiments
Local Likelihood Estimators in a Regression Model for Stock Returns
Local regression and likelihood
Empirical likelihood estimators for the error distribution in nonparametric regression models
Empirical likelihood estimators for the error distribution in nonparametric regression models
Maximum likelihood estimators in linear regression models with Ornstein-Uhlenbeck process
Tail Probabilities for Regression Estimators
Specification tests for non-Gaussian maximum likelihood estimators
The Relative Performance of Targeted Maximum Likelihood Estimators
Circular local likelihood
Response smoothing estimators in binary regression
Relative efficiency of maximum likelihood and other estimators in a nonlinear regression model with small measurement errors