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Abstract Details
Activity Number:
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636
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Type:
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Invited
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #300128 |
Title:
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Haar-Fisz Methodology for Interpretable Estimation of Large, Sparse, Time-Varying Volatility Matrices
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Author(s):
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Piotr Fryzlewicz*+
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Companies:
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London School of Economics
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Address:
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Department of Statistics, London, International, WC2A 2AE, United Kingdom
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Keywords:
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Haar-Fisz ;
volatility matrix ;
non-stationarity ;
high-dimensionality ;
sparsity ;
financial time series
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Abstract:
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The emergence of the recent financial crisis, during which many markets underwent changes in their statistical structure over a short period of time, illustrates the importance of non-stationary modelling in financial time series. We start this talk by advocating a simple non-stationary multivariate model for financial returns. One task of critical importance to a financial analyst is accurate estimation of the volatility matrix, and in our model, this will be a time-varying quantity. Our estimation method is based on Haar wavelet thresholding, supplemented with the essential variance-stabilising Fisz transform (hence the name Haar-Fisz). Thanks to the use of Haar wavelets, our estimator: (a) has a natural in-built sparsity, i.e. local cross-market correlations are naturally estimated as zero wherever possible, which enhances the invertibility of the estimated matrix; (b) adequately captures sudden regime changes; (c) is theoretically tractable, also in the pointwise sense; (d) is rapidly computable, which is important if the matrix is large. We use real-data examples to illustrate our methodology.
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Authors who are presenting talks have a * after their name.
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