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Forecasting Based on Decomposed Financial Return Series: A Wavelet Analysis
Author(s) -
Berger Theo
Publication year - 2016
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2384
Subject(s) - econometrics , volatility (finance) , series (stratigraphy) , wavelet , value at risk , parametric statistics , economics , wavelet transform , financial market , computer science , finance , mathematics , statistics , artificial intelligence , risk management , paleontology , biology
We transform financial return series into its frequency and time domain via wavelet decomposition to separate short‐run noise from long‐run trends and assess the relevance of each frequency to value‐at‐risk (VaR) forecast. Furthermore, we analyze financial assets in calm and turmoil market times and show that daily 95% VaR forecasts are mainly driven by the volatility that is captured by the first scales comprising the short‐run information, whereas more timescales are needed to adequately forecast 99% VaR. As a result, individual timescales linked via copulas outperform classical parametric VaR approaches that incorporate all information available. Copyright © 2015 John Wiley & Sons, Ltd.