Journal article | Zeitschriftenartikel

Local Likelihood Estimators in a Regression Model for Stock Returns

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.

Urheber*in: Jönck, Uwe Christian

Free access - no reuse

Extent: Seite(n): 619-635

Language: Englisch

Notes: Status: Postprint; begutachtet (peer reviewed)

Bibliographic citation: Quantitative Finance, 8(6)

Subject: Wirtschaft
Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik
Allgemeines, spezielle Theorien und Schulen, Methoden, Entwicklung und Geschichte der Wirtschaftswissenschaften
Theorieanwendung

Event: Geistige Schöpfung

(who): Jönck, Uwe Christian

Event: Veröffentlichung

(where): Vereinigtes Königreich

(when): 2008

DOI: https://doi.org/10.1080/14697680701656181

URN: urn:nbn:de:0168-ssoar-221111

Rights: GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln

Last update: 21.06.2024, 4:26 PM CEST

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Zeitschriftenartikel

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Jönck, Uwe Christian

Time of origin

2008

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Local Likelihood Estimators in a Regression Model for Stock Returns

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Other Objects (12)

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

Maximum likelihood estimators in linear regression models with Ornstein-Uhlenbeck process

Empirical likelihood estimators for the error distribution in nonparametric regression models

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

Maximum likelihood estimators in linear regression models with Ornstein-Uhlenbeck process

Empirical likelihood estimators for the error distribution in nonparametric regression models

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

Maximum likelihood estimators in linear regression models with Ornstein-Uhlenbeck process

Empirical likelihood estimators for the error distribution in nonparametric regression models

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

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