* 5 Results enter search term Search Reset filter Suchfilter Content Type Publications (5) Publication Type Working Paper (4) Journal Article (1) Research International Macroeconomics (5) Macroeconomic Research (5) Experts Thomas Lux (5) Leonardo Morales-Arias (4) Cristina Sattarhoff (1) Ruipeng Liu (1) Date Last Month Last Year Select Period start date to end date Sort by Relevance Date Aktive Filter Tags: long memory Remove all filters Working Paper Relative Forecasting Performance of Volatility Models: Monte Carlo Evidence 01.01.2010 A Monte Carlo (MC) experiment is conducted to study the forecasting performance of a variety of volatility models under alternative data generating processes (DGPs). The models included in the MC study are the (Fractionally Integrated) Generalized... Working Paper Forecasting Volatility under Fractality, Regime-Switching, Long Memory and Student-t Innovations 01.07.2009 The Markov-Switching Multifractal model of asset returns with Student-t innovations (MSM-t henceforth) is introduced as an extension to the Markov-Switching Multifractal model of asset returns (MSM). The MSM-t can be estimated via Maximum Likelihood... Journal Article Forecasting Volatility under Fractality, Regime-Switching, Long Memory and Student-t Innovations 01.11.2010 The Markov-Switching Multifractal model of asset returns with Student-t innovations (MSM-t henceforth) is introduced as an extension to the Markov-Switching Multifractal model of asset returns (MSM). The MSM-t can be estimated via Maximum Likelihood... Working Paper Flexible and Robust Modelling of Volatility Comovements: A Comparison of Two Multifractal Models 01.02.2010 Long memory (long-term dependence) of volatility counts as one of the ubiquitous stylized facts of financial data. Inspired by the long memory property, multifractal processes have recently been introduced as a new tool for modeling financial time... Working Paper A Markov-switching Multifractal Approach to Forecasting Realized Volatility 01.10.2011 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...
Working Paper Relative Forecasting Performance of Volatility Models: Monte Carlo Evidence 01.01.2010 A Monte Carlo (MC) experiment is conducted to study the forecasting performance of a variety of volatility models under alternative data generating processes (DGPs). The models included in the MC study are the (Fractionally Integrated) Generalized...
Working Paper Forecasting Volatility under Fractality, Regime-Switching, Long Memory and Student-t Innovations 01.07.2009 The Markov-Switching Multifractal model of asset returns with Student-t innovations (MSM-t henceforth) is introduced as an extension to the Markov-Switching Multifractal model of asset returns (MSM). The MSM-t can be estimated via Maximum Likelihood...
Journal Article Forecasting Volatility under Fractality, Regime-Switching, Long Memory and Student-t Innovations 01.11.2010 The Markov-Switching Multifractal model of asset returns with Student-t innovations (MSM-t henceforth) is introduced as an extension to the Markov-Switching Multifractal model of asset returns (MSM). The MSM-t can be estimated via Maximum Likelihood...
Working Paper Flexible and Robust Modelling of Volatility Comovements: A Comparison of Two Multifractal Models 01.02.2010 Long memory (long-term dependence) of volatility counts as one of the ubiquitous stylized facts of financial data. Inspired by the long memory property, multifractal processes have recently been introduced as a new tool for modeling financial time...
Working Paper A Markov-switching Multifractal Approach to Forecasting Realized Volatility 01.10.2011 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...