We propose a new clustering approach for comparing financial time series and employ it to study how the COVID-19 pandemic affected the U.S. stock market. Essentially, we compute the forecast accuracy of asymmetric GARCH models applied to S&P500 industries and use the model forecast errors for...

This paper explores the structural driving sources of oil market volatility and correlation dynamics. Our results highlight a steadily declining oil production volatility and concurrent increasing real oil price volatility since the mid-1980s. We find that a decrease in the persistence of the...

Did Covid19 indiced market turmoil impact the intraday volatility spillovers between energy and other ETFs?. To examine this , we first estimate the realized volatility of ETFs using the 5-minute high-frequency data. Next, we employ time-varying parameter vector autoregressions (TVP-VAR)....

Modeling futures market risk simultaneously influenced by macro low-frequency information and daily risk factors is a valuable challenge. We propose a new general framework for it based on the flexible GARCH-MIDAS model. It uses a skewed t distribution to describe the asymmetry of long and short...

This paper investigates the weekly stock market data of the Hungarian stock index BUX, the Czech stock index PX and the Polish stock index WIG20 spanning from January 7, 2001 to April 18, 2021. The period of more than 20 years enabled to analyse the behaviour of returns and their volatility...

The book addresses the problem of a time-varying unconditional variance of return processes utilizing a spline function. The knots of the spline functions are estimated as free parameters within a joined estimation process together with the parameters of the mean, the conditional variance and...