We develop a stochastic volatility option pricing model that exploits the informative content of historical high frequency data. Using the Two Scales Realized Volatility as a proxy for the unobservable returns volatility, we propose a simple (affine) but effective long-memory process: the Heterogeneous Auto-Regressive Gamma (HARG) model. This discrete–time process, combined with an exponential ...

Recent evidence suggests that ignoring structural breaks in volatility financial asset returns can result overestimation of spillover among markets. This paper examines major US equity sectors (i.e. Financial, Technology, Energy, Health, Consumer and Industrial) with bivariate GARCH models utilizing daily data from April 2006 to March 2021 after adjusting for breaks. I find significantly less b...

Volatility is the most widespread measure of risk. modeling allows investors to capture potential losses and investment opportunities. This work aims examine impact two waves COVID-19 infections on return volatility stock market indices euro area countries. The study also focuses other important aspects such as time-varying risk premium leverage effect. investigation employed Threshold GARCH(1,...

In our project we have used parametric simulation and filtered historical simulation by GARCH processes to model the future position on a portfolio of some actively trading S&P bonds and related credit default swaps. The portfolio is marked to market daily based on the daily prices and CDS spreads over a seven year period. The Credit default swaps are priced daily based on the shifts in the def...

Efficient financial markets with high degree of transparency do not substantiate the hypothesis that there are differences in the volatility of return. Generally, there are factors rejecting any perfect similarity in the volatility of return in the emerging stock markets, as previous studies in Iran have confirmed the complete difference. On the other hand, the hybrid model PANEL-GARCH has the ...

We examine the joint predictability of return and cash flow within a present value framework, by imposing the implications from a long-run risk model that allow for both time-varying volatility and volatility uncertainty. We provide new evidence that the expected return variation and the variance risk premium positively forecast both short-horizon returns and dividend growth rates. We also conf...

This article compares the performance of bivariate error correction GARCH and FIGARCH models when estimating long term dynamic minimum variance hedge ratios (MVHRs) on the Australian All Ordinaries Index. The paper therefore introduces the bivariate error correction FIGARCH model into the hedging literature, which to date has only employed the GARCH class of processes. This is important for tho...

(ARES). We would also particularily like to thank the Editor, Ed Coulson, and two referees for their helpful comments, but are responsible for any remaining errors. Cotter's contribution to the study has been supported by a University College Dublin School of Business research grant. Abstract One stylized feature of financial volatility impacting the modeling process is long memory. This paper ...