EVI-Emerging Asia - EDHEC-Risk

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Methodology: Robustness • We employ techniques from robust statistics to reduce noise and increase resistance to outliers. The assumption is that cross-sectional data is contaminated. • Using equal weights w it = 1/N in the estimator or e.g. cap-weights, results in a lot of noise. • We adjust the weights to the sample so that the outliers get smaller weights (w it = 0 for severe outliers) and the returns from the center of the distribution get equal weights.
Value-Added of Robustness 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Emerging Asia: Equally-weighted vs Robust EW CSV Robust 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Emerging Asia: Cap-weighted vs Robust CW CSV Robust
Page 1 and 2: EDHEC-Risk Days Europe 2012 May 10,
Page 3 and 4: Outline • Volatility Indicators -
Page 5 and 6: Introduction • Information about
Page 7 and 8: Volatility Indices • Since inform
Page 9 and 10: Problems with Option-Implied Volati
Page 11 and 12: Introducing a New Volatility Index
Page 13 and 14: 1 Day Before Fukushima • The cros
Page 15 and 16: Cross-Sectional Volatility • We p
Page 17 and 18: • Volatility Indicators - Backgro
Page 19 and 20: Interpretations of CSV • Among al
Page 23: Methodology: Liquidity • Lack of
Page 27 and 28: Introducing EVI • The methodology
Page 31 and 32: Pan-Asian Volatility Indicators 1 0
Page 33 and 34: EVI-Developed Asia ex Japan vs VKOS
Page 35 and 36: Correlations between Asian Option-
Page 37 and 38: 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6
Page 39 and 40: Conclusions • We introduce a new

EVI-Emerging Asia - EDHEC-Risk

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