Arbeitspapier
A Markov-switching multifractal approach to forecasting realized volatility
The volatility specification of the Markov-switching Multifractal (MSM) model is proposed as an alternative mechanism for realized volatility (RV). We estimate the RV-MSM model via Generalized Method of Moments and perform forecasting by means of best linear forecasts derived via the Levinson-Durbin algorithm. The out-of-sample performance of the RV-MSM is compared against other popular time series specfications usually employed to model the dynamics of RV as well as other standard volatility models of asset returns. An intra-day data set for five major international stock market indices is used to evaluate the various models out-of-sample. We find that the RV-MSM seems to improve upon forecasts of its baseline MSM counterparts and many other volatility models in terms of mean squared errors (MSE). While the more conventional RV-ARFIMA model comes out as the most successful model (in terms of the number of cases in which it has the best forecasts for all combinations of forecast horizons and criteria), the new RV-MSM model seems often very close in its performance and in a non-negligible number of cases even dominates over the RV-ARFIMA model.
- Sprache
-
Englisch
- Erschienen in
-
Series: Kiel Working Paper ; No. 1737
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: General
Asset Pricing; Trading Volume; Bond Interest Rates
- Thema
-
realized volatility
multiplicative volatility models
long memory
international volatility forecasting
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Lux, Thomas
Morales-Arias, Leonardo
Sattarhoff, Cristina
- Ereignis
-
Veröffentlichung
- (wer)
-
Kiel Institute for the World Economy (IfW)
- (wo)
-
Kiel
- (wann)
-
2011
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 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
- Lux, Thomas
- Morales-Arias, Leonardo
- Sattarhoff, Cristina
- Kiel Institute for the World Economy (IfW)
Entstanden
- 2011
Ähnliche Objekte (12)
The Markov-Switching Multifractal Model of asset returns: GMM estimation and linear forecasting of volatility
The Markov-switching multi-fractal model of asset returns: GMM estimation and linear forecasting of volatility
Inference for nonlinear state space models: A comparison of different methods applied to Markov-switching multifractal models
Exact solutions for the transient densities of continuous-time Markov switching models: With an application to the poisson multifractal model
Forecasting the Variability of Stock Index Returns with the Multifractal Random Walk Model for Realized Volatilities
Rational Forecasts or Social Opinion Dynamics? Identification of Interaction Effects in a Business Climate Survey
Forecasting volatility under fractality, regime-switching, long memory and student-t innovations
Bayesian estimation of agent-based models via adaptive particle Markov chain Monte Carlo
Bayesian Estimation of Agent-Based Models via Adaptive Particle Markov Chain Monte Carlo
Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching
The multi-fractal model of asset returns: Its estimation via GMM and its use for volatility forecasting
True and Apparent Scaling: The Proximity of the Markov- Switching Multifractal Model to Long-Range Dependence
The Markov-Switching Multifractal Model of asset returns: GMM estimation and linear forecasting of volatility
The Markov-switching multi-fractal model of asset returns: GMM estimation and linear forecasting of volatility
Inference for nonlinear state space models: A comparison of different methods applied to Markov-switching multifractal models
Exact solutions for the transient densities of continuous-time Markov switching models: With an application to the poisson multifractal model
Forecasting the Variability of Stock Index Returns with the Multifractal Random Walk Model for Realized Volatilities
Rational Forecasts or Social Opinion Dynamics? Identification of Interaction Effects in a Business Climate Survey
Forecasting volatility under fractality, regime-switching, long memory and student-t innovations
Bayesian estimation of agent-based models via adaptive particle Markov chain Monte Carlo
Bayesian Estimation of Agent-Based Models via Adaptive Particle Markov Chain Monte Carlo
Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching
The multi-fractal model of asset returns: Its estimation via GMM and its use for volatility forecasting
True and Apparent Scaling: The Proximity of the Markov- Switching Multifractal Model to Long-Range Dependence
The Markov-Switching Multifractal Model of asset returns: GMM estimation and linear forecasting of volatility
The Markov-switching multi-fractal model of asset returns: GMM estimation and linear forecasting of volatility
Inference for nonlinear state space models: A comparison of different methods applied to Markov-switching multifractal models
Exact solutions for the transient densities of continuous-time Markov switching models: With an application to the poisson multifractal model
Forecasting the Variability of Stock Index Returns with the Multifractal Random Walk Model for Realized Volatilities
Rational Forecasts or Social Opinion Dynamics? Identification of Interaction Effects in a Business Climate Survey
Forecasting volatility under fractality, regime-switching, long memory and student-t innovations
Bayesian estimation of agent-based models via adaptive particle Markov chain Monte Carlo
Bayesian Estimation of Agent-Based Models via Adaptive Particle Markov Chain Monte Carlo
Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching
The multi-fractal model of asset returns: Its estimation via GMM and its use for volatility forecasting