Arbeitspapier
The Stochastic Volatility in Mean Model
In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable in the mean equation. The same extension isdeveloped elsewhere for Autoregressive ConditionalHeteroskedastic (ARCH) models, known as the ARCH in Mean (ARCH-M)model. The estimation of ARCH models isrelatively easy compared with that of the Stochastic Volatility (SV)model. However, efficient Monte Carlo simulationmethods for SV models have been developed to overcome some of theseproblems. The details of modificationsrequired for estimating the volatility-in-mean effect are presentedin this paper together with a Monte Carlo study toinvestigate the small-sample properties of the SVM estimators. Takingthese developments of estimation methods intoaccount, we regard SV and SVM models as practical alternatives totheir ARCH counterparts and therefore it is ofinterest to study and compare the two classes of volatility models.We present an empirical study about theintertemporal relationship between stock index returns and theirvolatility for the United Kingdom, United States andJapan. This phenomenon has been discussed in the financial literaturebut has proved hard to find empirically; we findevidence of a negative but weak relationship.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 00-024/4
- Klassifikation
-
Wirtschaft
- Thema
-
Forecasting
GARCH
Simulated maximum likelihood
Stochastic volatility
Stock indices
Börsenkurs
Volatilität
ARCH-Modell
CAPM
Theorie
USA
Großbritannien
Japan
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Koopman, Siem Jan
Uspensky, Eugenie Hol
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2000
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Koopman, Siem Jan
- Uspensky, Eugenie Hol
- Tinbergen Institute
Entstanden
- 2000
Ähnliche Objekte (12)
Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility
The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures
Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction
Asymmetric Stable Stochastic Volatility Models: Estimation, Filtering, and Forecasting
Stock Index Volatility Forecasting with High Frequency Data
Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model
Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production
Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
Finding the European crime drop using a panel data model with stochastic trends
A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area
Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model
Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility
The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures
Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction
Asymmetric Stable Stochastic Volatility Models: Estimation, Filtering, and Forecasting
Stock Index Volatility Forecasting with High Frequency Data
Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model
Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production
Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
Finding the European crime drop using a panel data model with stochastic trends
A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area
Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model
Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility
The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures
Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction
Asymmetric Stable Stochastic Volatility Models: Estimation, Filtering, and Forecasting
Stock Index Volatility Forecasting with High Frequency Data
Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model
Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production
Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
Finding the European crime drop using a panel data model with stochastic trends
A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area