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Forecasting volatility under fractality, regime-switching, long memory and student-t innovations. (2010). Lux, Thomas ; Morales-Arias, Leonardo .
In: Computational Statistics & Data Analysis.
RePEc:eee:csdana:v:54:y:2010:i:11:p:2676-2692.

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  1. The predictive power of power-laws: An empirical time-arrow based investigation. (2022). Kalda, Jaan ; di Tollo, Giacomo ; Andria, Joseph.
    In: Chaos, Solitons & Fractals.
    RePEc:eee:chsofr:v:162:y:2022:i:c:s096007792200635x.

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  2. Analytic moments for GJR-GARCH (1, 1) processes. (2021). Stanescu, Silvia ; Lazar, Emese ; Alexander, Carol.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:37:y:2021:i:1:p:105-124.

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  3. Forecasting volatility in bitcoin market. (2020). Bekiros, Stelios ; Segnon, Mawuli.
    In: Annals of Finance.
    RePEc:kap:annfin:v:16:y:2020:i:3:d:10.1007_s10436-020-00368-y.

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  4. Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices. (2020). Ho, Kin-Yip ; Gao, Guangyuan ; Shi, Yanlin.
    In: Pacific-Basin Finance Journal.
    RePEc:eee:pacfin:v:61:y:2020:i:c:s0927538x18300441.

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  5. Day-of-the-week and month-of-the-year effects on French Small-Cap Volatility: the role of asymmetry and long memory. (2019). Chikhi, Mohamed ; BENDOB, ALI ; Siagh, Ahmed Ramzi.
    In: Eastern Journal of European Studies.
    RePEc:jes:journl:y:2019:v:10:p:221-248.

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  6. Forecasting Volatility in Cryptocurrency Markets. (2019). Bekiros, Stelios ; Segnon, Mawuli.
    In: CQE Working Papers.
    RePEc:cqe:wpaper:7919.

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  7. How to Measure Financial Market Efficiency? A Multifractality-Based Quantitative Approach with an Application to the European Carbon Market. (2018). Gronwald, Marc ; Sattarhoff, Cristina.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_7102.

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  8. Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models. (2017). GUPTA, RANGAN ; Segnon, Mawuli ; Lux, Thomas.
    In: Renewable and Sustainable Energy Reviews.
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  9. Generalized Method of Moment estimation of multivariate multifractal models. (2017). Liu, Ruipeng ; Lux, Thomas.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:67:y:2017:i:c:p:136-148.

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  10. Forecasting Market Risk of Portfolios: Copula-Markov Switching Multifractal Approach. (2017). Trede, Mark ; Segnon, Mawuli.
    In: CQE Working Papers.
    RePEc:cqe:wpaper:6617.

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  11. A Markov-switching regression model with non-Gaussian innovations: estimation and testing. (2017). De Angelis, Luca ; Cinzia, Viroli.
    In: Studies in Nonlinear Dynamics & Econometrics.
    RePEc:bpj:sndecm:v:21:y:2017:i:2:p:22:n:3.

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  12. Binomial Markov-Switching Multifractal model with Skewed t innovations and applications to Chinese SSEC Index. (2016). Liu, Yufang ; Fu, Junhui ; Zhang, Weiguo.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:462:y:2016:i:c:p:56-66.

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  13. Financial power laws: Empirical evidence, models, and mechanisms. (2016). Alfarano, Simone ; Lux, Thomas.
    In: Chaos, Solitons & Fractals.
    RePEc:eee:chsofr:v:88:y:2016:i:c:p:3-18.

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  14. Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-type Volatility Models. (2015). GUPTA, RANGAN ; Segnon, Mawuli ; Lux, Thomas.
    In: FinMaP-Working Papers.
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  15. Out?of?sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini?futures markets. (2015). VORTELINOS, DIMITRIOS.
    In: Review of Financial Economics.
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  16. Out-of-sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini-futures markets. (2015). Vortelinos, Dimitrios I.
    In: Review of Financial Economics.
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  17. Clustering financial time series: New insights from an extended hidden Markov model. (2015). Ramos, Sofia ; Vermunt, Jeroen K. ; Dias, Jose G..
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:243:y:2015:i:3:p:852-864.

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  18. Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model. (2014). GUPTA, RANGAN ; Ben Nasr, Adnen ; Ajmi, Ahdi Noomen.
    In: Applied Financial Economics.
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  19. Realized Volatility Forecast: Structural Breaks, Long Memory, Asymmetry, and Day-of-the-Week Effect. (2014). Yang, KE ; Chen, Langnan.
    In: International Review of Finance.
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  20. Multifractal models in finance: Their origin, properties, and applications. (2013). Segnon, Mawuli ; Lux, Thomas.
    In: Kiel Working Papers.
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  21. Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices. (2013). Weron, Rafał ; Janczura, Joanna.
    In: AStA Advances in Statistical Analysis.
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  22. Modeling the Volatility of the Dow Jones Islamic Market World Index Using a Fractionally Integrated Time Varying GARCH (FITVGARCH) Model. (2013). GUPTA, RANGAN ; Ben Nasr, Adnen ; Ajmi, Ahdi Noomen.
    In: Working Papers.
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  23. A new model for explaining long-range correlations in human time interval production. (2012). Crato, Nuno ; Diniz, Ana ; Barreiros, Joo .
    In: Computational Statistics & Data Analysis.
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  24. Long memory and nonlinearities in realized volatility: A Markov switching approach. (2012). Raggi, Davide ; Bordignon, Silvano .
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:56:y:2012:i:11:p:3730-3742.

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  25. On the estimation and diagnostic checking of the ARFIMA–HYGARCH model. (2012). Li, Guodong ; Kwan, Wilson .
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:56:y:2012:i:11:p:3632-3644.

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  26. Goodness-of-fit testing for the marginal distribution of regime-switching models. (2011). Weron, Rafał ; Janczura, Joanna.
    In: MPRA Paper.
    RePEc:pra:mprapa:32532.

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  27. Flexible and robust modelling of volatility comovements: a comparison of two multifractal models. (2010). Liu, Ruipeng ; Lux, Thomas.
    In: Kiel Working Papers.
    RePEc:zbw:ifwkwp:1594.

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  28. Relative forecasting performance of volatility models: Monte Carlo evidence. (2010). Lux, Thomas ; Morales-Arias, Leonardo .
    In: Kiel Working Papers.
    RePEc:zbw:ifwkwp:1582.

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  47. Forecasting employment growth in Missouri with many potentially relevant predictors: an analysis of forecast combining methods. (2005). Strauss, Jack ; Rapach, David E..
    In: Regional Economic Development.
    RePEc:fip:fedlrd:y:2005:i:nov:p:97-112:n:v.1no.1.

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  48. Forecasting Time Series Subject to Multiple Structural Breaks. (2004). Timmermann, Allan ; Pettenuzzo, Davide ; Pesaran, M.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp1196.

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  49. Forecasting Time Series Subject to Multiple Structural Breaks. (2004). Timmermann, Allan ; Pettenuzzo, Davide ; Pesaran, M.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_1237.

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  50. ‘Forecasting Time Series Subject to Multiple Structural Breaks’. (2004). Timmermann, Allan ; Pettenuzzo, Davide ; Pesaran, M.
    In: Cambridge Working Papers in Economics.
    RePEc:cam:camdae:0433.

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