Arbeitspapier
Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model
We introduce a dynamic Skellam model that measures stochastic volatility from high-frequency tick-by-tick discrete stock price changes. The likelihood function for our model is analytically intractable and requires Monte Carlo integration methods for its numerical evaluation. The proposed methodology is applied to tick-by-tick data of four stocks traded on the New York Stock Exchange. We require fast simulation methods for likelihood evaluation since the number of observations per series per day varies from 1000 to 10,000. Complexities in the intraday dynamics of volatility and in the frequency of trades without price impact require further non-trivial adjustments to the dynamic Skellam model. In-sample residual diagnostics and goodness-of-fit statistics show that the final model provides a good fit to the data. An extensive forecasting study of intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling approaches.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 15-076/IV/DSF94
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Financial Econometrics
- Thema
-
non-Gaussian time series models
volatility models
importance sampling
numerical integration
high-frequency data
discrete price changes.
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Koopman, Siem Jan
Lit, Rutger
Lucas, Andre
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2015
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den 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
- Lit, Rutger
- Lucas, Andre
- Tinbergen Institute
Entstanden
- 2015
Ähnliche Objekte (12)
The Stochastic Volatility in Mean Model
Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility
Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions
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
Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series
Finding the European crime drop using a panel data model with stochastic trends
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
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
The Stochastic Volatility in Mean Model
Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility
Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions
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
Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series
Finding the European crime drop using a panel data model with stochastic trends
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
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
The Stochastic Volatility in Mean Model
Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility
Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions
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
Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series
Finding the European crime drop using a panel data model with stochastic trends
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area