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Revisiting the long memory dynamics of implied-realized volatility relation : a new evidence from wavelet band spectrum regression
Barunik, Jozef; Barunikova, Michaela - 2015
This paper revisits the fractional co-integrating relationship between ex-ante implied volatility and ex-post realized … volatility. Previous studies on stock index options have found biases and inefficiencies in implied volatility as a forecast of … future volatility. It is argued that the concept of corridor implied volatility (CIV) should be used instead of the popular …
Persistent link: https://www.econbiz.de/10011280711
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Revisiting the Fractional Cointegrating Dynamics of Implied-Realized Volatility Relation with Wavelet Band Spectrum Regression
Baruník, Jozef - 2013
This paper revisits the fractional cointegrating relationship between ex-ante implied volatility and ex-post realized … volatility. We argue that the concept of corridor implied volatility (CIV) should be used instead of the popular model …-free option-implied volatility (MFIV) when assessing the fractional cointegrating relation as the latter may introduce bias to the …
Persistent link: https://www.econbiz.de/10013090381
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High-Frequency Factor Models and Regressions
Ait-Sahalia, Yacine - 2019
individual stocks. We provide an inference framework for all components of the model, including idiosyncratic volatility and …
Persistent link: https://www.econbiz.de/10012894411
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Optimal Bandwidth Choice for Interval Estimation in GMM Regression
Sun, Yixiao - 2008
In time series regression with nonparametrically autocorrelated errors, it is now standard empirical practice to construct confidence intervals for regression coefficients on the basis of nonparametrically studentized t-statistics. The standard error used in the studentization is typically...
Persistent link: https://www.econbiz.de/10012771849
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Semi-Parametric Single-Index Predictive Regression Models with Cointegrated Regressors
Zhou, Weilun - 2020
This paper considers the estimation of a semi-parametric single-index regression model that allows for nonlinear predictive relationships. This model is useful for predicting financial asset returns, whose observed behavior is described by a stationary process, when the multiple non-stationary...
Persistent link: https://www.econbiz.de/10012822931
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Bayesian Nonparametric Sparse Seemingly Unrelated Regression Model (SUR)
Billio, Monica - 2017
Seemingly unrelated regression (SUR) models are useful in studying the interactions among different variables. In a high dimensional setting or when applied to large panel of time series, these models require a large number of parameters to be estimated and suffer of inferential problems.To...
Persistent link: https://www.econbiz.de/10012968298
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Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts
De Gooijer, Jan G. - 2019
We propose a hybrid penalized averaging for combining parametric and non-parametric quantile forecasts when faced with a large number of predictors. This approach goes beyond the usual practice of combining conditional mean forecasts from parametric time series models with only a few predictors....
Persistent link: https://www.econbiz.de/10012859663
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Application of the Scaling Functions to Nonparametric Regression
Manole, Daniel Sorin - 2007
For estimating regression function we can use many proceedings. In this paper, we have chosen to apply scaling functions to the estimation of regression functions. When one knows many bivariate date with the values of two variables, in the goal to express a correlation between the two variables...
Persistent link: https://www.econbiz.de/10014051848
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Consistent Hac Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation
Phillips, Peter C. B.; Sun, Yixiao; Jin, Sainan - 2003
A new family of kernels is suggested for use in heteroskedasticity and autocorrelation consistent (HAC) and long run variance (LRV) estimation and robust regression testing. The kernels are constructed by taking powers of the Bartlett kernel and are intended to be used with no truncation (or...
Persistent link: https://www.econbiz.de/10014088395
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Data-driven jump detection thresholds for application in jump regressions
Davies, Robert; Tauchen, George Eugene - 2015
This paper develops a method to select the threshold in threshold-based jump detection methods. The method is motivated by an analysis of threshold-based jump detection methods in the context of jump-diffusion models. We show that over the range of sampling frequencies a researcher is most...
Persistent link: https://www.econbiz.de/10011524214