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How Long Memory Affects True Dependence Structure? Evidence From the Dow Jones Islamic Sub-indexes

Braiek, Sana - 2018

Long memory is a stylized fact found in most financial return series. In this paper, we seek to examine the impact of the presence of long memory on the dependence structure. First, we fit the multivariate dependence structure using R-vine copulas for pairs of raw and filtered returns. Second,...

Persistent link: https://www.econbiz.de/10012930466

Efficient Semi-Parametric Estimation of Non-Gaussian GARCH Processes

Godin, Frédéric - 2016

estimators incorporate information related to the skewness and kurtosis of residuals. This improves their efficiency in … Variance-Gamma innovations. The methodology is also shown to be applicable for the estimation of multivariate GARCH processes …

Persistent link: https://www.econbiz.de/10012978175

A Semiparametric Intraday GARCH Model

Malec, Peter - 2016

We propose a multiplicative component model for intraday volatility. The model consists of a seasonality factor, as well as a semiparametric and parametric component. The former captures the well-documented intraday seasonality of volatility, while the latter two account for the impact of the...

Persistent link: https://www.econbiz.de/10012990974

Multistep Quantile Forecasts for Supply Chain and Logistic Operations : Bootstrapping, the GARCH model and Quantile Regression Based Approaches

Bruzda, Joanna - 2018

distribution or parametric and nonparametric bootstrap techniques. These methods lead to large reductions in the total costs of …

Persistent link: https://www.econbiz.de/10012932647

Efficient Estimation in Semiparametric GARCH Models

Drost, Feike C.; Klaassen, Chris A. J. - 1998

It is well-knownthat financial data sets exhibit conditional heteroskedasticity. GARCH type models are often used to model this phenomenon. Since the distribution of the rescaled innovations is generally far froma normal distribution, a semiparametric approach is advisable. Several publications...

Persistent link: https://www.econbiz.de/10014155199

Splines for Financial Volatility

Audrino, Francesco; Buhlmann, Peter - 2007

We propose a flexible GARCH-type model for the prediction of volatility in financial time series. The approach relies on the idea of using multivariate B-splines of lagged observations and volatilities. Estimation of such a B-spline basis expansion is constructed within the likelihood framework...

Persistent link: https://www.econbiz.de/10014051065

Efficient Estimation of Conditional Risk Measures in a Semiparametric GARCH Model

Yan, Yang - 2012

constraints. Moment constraints are often used to identify and estimate the mean and variance parameters and are however discarded …. At the same time, the efficiency gain in error quantile estimation hinges on the efficiency of estimators of the variance … leads to interesting implications of residual bootstrap for dynamic models. We find that these proposed estimators for …

Persistent link: https://www.econbiz.de/10013105447

A New Semiparametric Volatility Model

Ji, Jiangyu - 2012

We propose a new semiparametric observation-driven volatility model where the form of the error density directly influences the volatility dynamics. This feature distinguishes our model from standard semiparametric GARCH models. The link between the estimated error density and the volatility...

Persistent link: https://www.econbiz.de/10013106178

Hedging the Black Swan : Conditional Heteroskedasticity and Tail Dependence in S&P500 and Vix

Abbas, Sawsan - 2010

The recent financial crisis has accentuated the fact that extreme outcomes have been overlooked and not dealt with adequately. While extreme value theories have existed for a long time, the multivariate variant is difficult to handle in the financial markets due to the prevalent...

Persistent link: https://www.econbiz.de/10013148084

Volatility of Volatility : Estimation and Tests Based on Noisy High Frequency Data with Jumps

Li, Yingying; Liu, Guangying; Zhang, Zhiyuan - 2021

We establish a feasible central limit theorem with convergence rate $n^{1/8}$ for the estimation of the {integrated volatility of volatility} (VoV) based on noisy high-frequency data with jumps. This is the first inference theory ever built for VoV estimation under such a general setup. The...

Persistent link: https://www.econbiz.de/10013242977