True and Apparent Scaling: The Proximity of the Markov- Switching Multifractal Model to Long-Range Dependence
In this paper, we consider daily financial data of a collection of different stock market indices, exchange rates, and interest rates, and we analyze their multi-scaling properties by estimating a simple specification of the Markov-switching multifractal model (MSM). In order to see how well the estimated models capture the temporal dependence of the data, we estimate and compare the scaling exponents $H(q)$ (for $q = 1, 2$) for both empirical data and simulated data of the estimated MSM models. In most cases the multifractal model appears to generate `apparent' long memory in agreement with the empirical scaling laws.
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- Thierry Roncalli & Gael Riboulet & Ashkan Nikeghbali & Vado Durrleman & Erick Bouy?, 2001. "Copulas: an Open Field for Risk Management," Working Papers wp01-01, Warwick Business School, Finance Group.
- Calvet, Laurent & Fisher, Adlai, 2001.
"Forecasting multifractal volatility,"
Journal of Econometrics,
Elsevier, vol. 105(1), pages 27-58, November.
- Laurent Calvet & Adlai Fisher, 1999. "Forecasting Multifractal Volatility," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-017, New York University, Leonard N. Stern School of Business-.
- Laurent-Emmanuel Calvet & Adlai J. Fisher, 2001. "Forecasting multifractal volatility," Post-Print hal-00477952, HAL.
- Laurent Calvet, 2000. "Forecasting Multifractal Volatility," Harvard Institute of Economic Research Working Papers 1902, Harvard - Institute of Economic Research.
- Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
- Tom Doan, "undated". "RATS programs to replicate Baillie, Bollerslev, Mikkelson FIGARCH results," Statistical Software Components RTZ00009, Boston College Department of Economics.
- Hall, Anthony D. & Hwang, Soosung & Satchell, Stephen E., 2002. "Using Bayesian variable selection methods to choose style factors in global stock return models," Journal of Banking & Finance, Elsevier, vol. 26(12), pages 2301-2325.
- Stephen Satchell & Soosung Hwang & Anthony Hall, 1999. "Using Bayesian Variable Selection Methods to Choose Style Factors in Global Stock Return Models," Working Papers wp99-01, Warwick Business School, Finance Group.
- Anthony D. Hall & Soosung Hwang & Steve Satchell, 2000. "Using Bayesian Variable Selection Methods to Choose Style Factors in Global Stock Return Models," Research Paper Series 31, Quantitative Finance Research Centre, University of Technology, Sydney.
- Anthony Hall & Soosung Hwang & Stephen E. Satchell, 2000. "Using Bayesian Variable Selection Methods to Choose Style Factors in Global Stock Return Models," Econometric Society World Congress 2000 Contributed Papers 1213, Econometric Society.
- Reiner Franke & Simone Alfarano, 2007. "A Simple Asymmetric Herding Model to Distinguish Between Stock and Foreign Exchange Markets," Working Papers wp07-01, Warwick Business School, Finance Group. Full references (including those not matched with items on IDEAS)
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