The nonlinear, asymmetric effects of oil prices, technological innovation, and investor sentiment on the clean energy stock market are quantified in this study using the quantile autoregressive distribution lag (QARDL) method and the quantile Granger causality test. This approach is based on...

This paper develops the asymptotic theory for stable autoregressive models in which the noise variance grows in a polynomial-like fashion. It is shown that the asymptotic distribution of the OLS estimator of the coefficient vector is multivariate normal with a covariance matrix that depends on...

This paper analyzes conditional threshold effects of stock market volatility on crude oil market volatility. We use the conditional threshold autoregressive (CoTAR) model, a novel extension of TAR from a constant to time-varying threshold. The conditional threshold is specified as an empirical...

We examine the predictive value of expected skewness of oil returns for the realized volatility using monthly data from 1859:11 to 2023:04. We utilize a quantile predictive regression model, which is able to accommodate nonlinearity and structural breaks. In-sample results show that the...

This paper proposes an unbiased combined weighted (CW) volatility measure and weighted volatility indicator (WVI) that integrates the return- and range-based volatility measures to model the dynamics volatility of stock returns. The main feature of the CW measure is that it is formulated based...

This paper explores the predictive ability of volatility in the crude oil market. A comparison in the CBOE crude oil volatility index (OVX), GARCH and Stochastic Volatility Models are employed to investigate the forecasting performance. The daily price dataset, spanning from 2010 to 2022,...

Accurate prediction of high-dimensional covariance matrices is crucial for portfolio and risk management. In the model developed in this work, high-frequency financial data is used to obtain the realized covariance matrix, and the realized semicovariance is used to decompose the covariance...

We propose an asymptotic N(0, 1) inferential strategy to test for volatility spillover between markets consisting of multiple sectors. First, we use nonparametric kernel method to derive test statistics that assign flexible weight to each lag order and are able to check a growing number of lags...

We provide a comprehensive assessment of volatility connectedness between the currencies of Central European (CE) countries using high-frequency data from 2009 to 2022. We assess asymmetries in connectedness (not investigated for CE currencies before) and document domination of the negative...