In this article we derive conditions which ensure the non-negativity of the conditional variance in the Hyperbolic GARCH(p; d; q) (HYGARCH) model of Davidson (2004). The conditions are necessary and sufficient for p < 2 and sufficient for p > 2 and emerge as natural extensions of the inequality constraints derived in...</2>

In this paper, we analyze the forecasting performance of several parametric extensions of the popular Dynamic Nelson-Siegel (DNS) model for the yield curve. Our focus is on the role of additional and time-varying decay parameters, conditional heteroscedasticity, and macroeconomic variables. We...

We consider the long memory and leverage properties of a model for the conditional variance of an observable stationary sequence, where the conditional variance is the square of an inhomogeneous linear combination of past values of the observable sequence, with square summable weights. This...

This paper shows that combinations of option implied and time series volatility forecasts that are conditional on current information are statistically superior to individual models, (unconditional) combinations, and hybrid forecasts. Hence, it finds empirical evidence that both, combining...

Volatility had been used as an indirect means for predicting risk accompanied with the asset. Volatility explains the variations in returns. Forecasting volatility had been a stimulating problem in the financial systems. The study examined the different volatility estimators and determined the...

In this study, we demonstrate that a common approach in using the Autoregressive Integrated Moving Average model is not efficient to forecast all types of time series data and most specially, the out-of-sample forecasting of the time series that exhibits clustering volatility. This gap leads to...

We document the forecasting gains achieved by incorporating measures of signed, finite and infinite jumps in forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that vary by sector, volume and degree of jump activity. We use...

This paper presents a new volatility model with time-varying volatility persistence (TVP) that is governed by the dynamics of an explanatory variable. We extend the GJR-GARCH model by introducing a time-varying GARCH coefficient that is linked to the variable in a parsimonious way using MIDAS...

Volatility has been used as an indirect means for predicting risk accompanied with an asset. Volatility explains the variations in returns. Forecasting volatility has been a stimulating problem in the financial systems. This study examined the different volatility estimators and determined the...

Leverage Exchange Traded Funds (LETF's) returns tend to deviate from their underlying assets' multiple returns as their holding period increase, a phenomenon known as volatility decay. Algebraically, it is shown that volatility decay is intensified for inverse leveraged funds and as the leverage...