Quantile Regression for Long Memory Testing: A Case of Realized Volatility
نویسندگان
چکیده
منابع مشابه
Refined Inference on Long Memory in Realized Volatility
There is an emerging consensus in empirical finance that realized volatility series typically display long range dependence with a memory parameter (d) around 0.4 (Andersen et. al. (2001), Martens et. al. (2004)). The present paper provides some analytical explanations for this evidence and shows how recent results in Lieberman and Phillips (2004a, 2004b) can be used to refine statistical infer...
متن کاملRealized stochastic volatility with leverage and long memory
! ! The daily return and the realized volatility are simultaneously modeled in the stochastic volatility model with leverage and long memory. In addition to the stochastic volatility model with leverage for the daily returns, ARFIMA process is jointly considered for the realized volatilities. Using a state space representation of the model, we estimate parameters by Markov chain Monte Carlo met...
متن کاملRealized Stochastic Volatility with General Asymmetry and Long Memory∗
The paper develops a novel realized stochastic volatility model of asset returns and realized volatility that incorporates general asymmetry and long memory (hereafter the RSV-GALMmodel). The contribution of the paper ties in with Robert Basmann’s seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of...
متن کاملVolatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution
The predictive performance of the realized stochastic volatility model of Takahashi, Omori, and Watanabe (2009), which incorporates the asymmetric stochastic volatility model with the realized volatility, is investigated. Considering well known characteristics of financial returns, heavy tail and negative skewness, the model is extended by employing a wider class distribution, the generalized h...
متن کاملA Copula-based Quantile Model for Crude oil Return-Volatility Dependence Modelling: Case of Iran Heavy Oil
The main purpose of this study is to investigate the relationship between Iran’s heavy crude oil price returns and volatility dependence using the Copula-based quantile model (CQM). CQM is an efficient tool for analyzing nonlinear time series models as it has no need for initial assumptions. We use monthly data from January 1990 to December 2019. We use the Hadrick-Prescott filter to calculate...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Financial Econometrics
سال: 2016
ISSN: 1479-8409,1479-8417
DOI: 10.1093/jjfinec/nbw001