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Xingdong Feng, Xuming He, Jianhua Hu (2011)
Wild bootstrap for quantile regression.Biometrika, 98 4
J. Hahn (1995)
Bootstrapping Quantile Regression EstimatorsEconometric Theory, 11
Journal of Business, 77
Elena Diaz, J. Molero, F. Gracia (2016)
Oil price volatility and stock returns in the G7 economiesEnergy Economics, 54
Xingguo Luo, Shihua Qin (2017)
Oil price uncertainty and Chinese stock returns: New evidence from the oil volatility indexFinance Research Letters, 20
Journal of Financial Economics, 31
G. Fagioli (2014)
Behavioural corporate finance
A. Hibbert, R. Daigler, Brice Dupoyet (2008)
A behavioral explanation for the negative asymmetric return–volatility relationJournal of Banking and Finance, 32
T. Agbeyegbe (2015)
An inverted U-shaped crude oil price return-implied volatility relationshipReview of Financial Economics, 27
Ihsan Badshah (2010)
Quantile Regression Analysis of Asymmetric Return-Volatility Relation
S. Shapiro, M. Wilk (1965)
An Analysis of Variance Test for Normality (Complete Samples)Biometrika, 52
Finance Research Letters, 13
A. Belloni, V. Chernozhukov, Iván Fernándex-Val, Iv´an Fern´andez-Val, Andrew Chesher, R. Koenker, Oliver Linton, Tatiana Komarova
Massachusetts Institute of Technology Department of Economics Working Paper Series Conditional Quantile Processes Based on Series or Many Regressors Conditional Quantile Processes Based on Series or Many Regressors
Biometrika, 70
Sofiane Aboura, Julien Chevallier (2012)
Leverage vs. Feedback: Which Effect Drives the Oil Market?Financial Crises eJournal
T. Bollerslev, Julia Litvinova, George Tauchen (2005)
Leverage and Volatility Feedback Effects in High-Frequency DataCapital Markets: Market Microstructure
D. Kahneman, A. Tversky (1979)
Prospect theory: An analysis of decision under risk Econometrica 47
J. Junior (2017)
An S-Shaped Crude Oil Price Return-Implied Volatility Relation: Parametric and Nonparametric EstimationsInternational journal of economics and finance, 9
Econometrica, 47
Cheekiat Low (2000)
The Fear and Exuberance from Implied Volatility of S&P 100 Index OptionsBehavioral & Experimental Finance
D. Kahneman (2007)
Prospect Theory : An Analysis of Decision under Risk Author ( s ) :
R. D'Agostino (1970)
Transformation to normality of the null distribution of g1Biometrika, 57
(1976)
Studies of stock volatility changes
M. Kocherginsky, Xuming He, Yunming Mu (2005)
Practical Confidence Intervals for Regression QuantilesJournal of Computational and Graphical Statistics, 14
Journal of International Financial Markets, Institutions, and Money, 41
H. Kinateder, N. Wagner (2017)
Oil and stock market returns: direction, volatility or liquidity
Peter Craven, G. Wahba (1978)
Smoothing noisy data with spline functionsNumerische Mathematik, 31
F. Anscombe, William Glynn (1983)
Distribution of the Kurtosis Statistic b2 for Normal Samples.Biometrika, 70
A. Belloni, V. Chernozhukov, D. Chetverikov, Α. Fernández-Val (2016)
Conditional quantile processes based on series or many regressors
P. Giot (2005)
Relationships Between Implied Volatility Indexes and Stock Index Returns, 31
G. Bekaert, Guojun Wu (1997)
Asymmetric Volatility and Risk in Equity MarketsCorporate Finance: Valuation
Jushan Bai, Pierre Perron (1998)
Computation and Analysis of Multiple Structural-Change Models, 18
Journal of Financial Econometrics, 4
J. Batten, Harald Kinateder, P. Szilagyi, N. Wagner (2019)
Liquidity, surprise volume and return premia in the oil marketEnergy Economics
R. Koenker, G. Bassett (2007)
Regression Quantiles
The R Journal, 8
The Journal of Finance, 59
Chaiyuth Padungsaksawasdi, R. Daigler (2014)
The Return‐Implied Volatility Relation for Commodity ETFsJournal of Futures Markets, 34
M. Lipsitz, A. Belloni, V. Chernozhukov, Iv'an Fern'andez-Val (2016)
quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile RegressionR J., 8
P. Eilers, B. Marx (2010)
Splines, knots, and penaltiesWiley Interdisciplinary Reviews: Computational Statistics, 2
Journal of Economic Perspectives—Volume 15, Number 4—Fall 2001—Pages 143–156 Quantile Regression
Sofiane Aboura, N. Wagner (2015)
Extreme Asymmetric Volatility: Stress and Aggregate Asset PricesParis-Dauphine: Finance (Topic)
Nicolas Bollen, R. Whaley (2002)
Does Net Buying Pressure Affect the Shape of Implied Volatility Functions?Vanderbilt: Finance (Topic)
The purpose of this study is to investigate empirically the pattern of co-movement between prices and implied volatility in the future markets for crude oil.Design/methodology/approachThe tool of non-parametric quantile regression is applied to daily price returns and implied volatility changes from 2007 to 2018.FindingsFor the total sample period, the link between price returns and forward-looking volatility expectations is contemporaneous, negative and asymmetric, and it exhibits an (approximately) inverted U-shaped pattern suggesting that: the pricing of implied volatility is heavier for large (in absolute value terms) changes relative to small ones and it is lighter for large positive changes relative to large negative ones. The pattern of co-movement, therefore, appears to be in line with the theoretical postulates of fear, exuberance and loss aversion. The main characteristics of the relationship are present in some (but not in all) sub-periods, which are also considered in this study.Originality/valueLess than a handful of works have assessed the link between implied volatility and prices for commodity ETFs. This is the first one relying on flexible non-parametric quantile regressions.
Studies in Economics and Finance – Emerald Publishing
Published: Jun 21, 2019
Keywords: Volatility; Non-parametric quantile regression; Oil prices; G10; C12
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