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 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...

The rough path-dependent volatility (RPDV) model (Parent 2022) effectively captures key empirical features that are characteristic of volatility dynamics, making it a suitable choice for volatility forecasting. However, its complex structure presents challenges when it comes to estimating the...

Investors care about the probability density of tomorrow's Government bond yields, while the literature on term structure models has focused on monthly or weekly yields. Past literature has long since documented the Garch-type conditional heteroscedasticity of daily yields, but again the...

Current price jump tests assume a constant intra-day volatility pattern (IVP) over sample period. We test this assumption by allowing IVP to depend on some state variables such as the sign of previous returns or the relative levels of volatility. Estimation results from 5-minute GARCH model for...

It turns out that stock return volatility responds asymmetrically following negative stock returns. A negative return shock causes next-period volatility to increase more than that of a positive return shock. The article examines the asymmetric property of return volatility using the volatility...