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...

Recent research shows that time-varying volatility plays a crucial role in nonlinear modeling. Contributing to this literature, we suggest a DSGE-GARCH approach that allows for straightforward computation of DSGE models with time-varying volatility, where the volatility component is formulated...

It is well known that volatility is time-varying and clustered. However, few studies have explored the information content of volatility clustering and its implications for investors’ risk aversion. This information is particularly important in turbulent periods, such as financial crisis. We...

High frequency regression has received more and more attention recent years. This is the first paper about detecting the change points in the beta process of high frequency regression. As an intermediate modelling approach between the constant beta and continuous beta process, this paper employs...

We show that a volatility-managed strategy using equity options provides higher alphas, increases Sharpe ratios, and generates significant utility gains for investors, exceeding those of the statistical volatility-managed counterparts. Return and volatility expectations embedded in options...

Purpose: The Group Method of Data Handling (GMDH) neural network has demonstrated good performance in data mining, prediction, and optimization. Scholars have used it to forecast stock and real estate investment trust (REIT) returns in some countries and region, but not in the United States (US)...