Peter Molnár získal cenu děkana FFÚ za nejlepší článek

Peter Molnár zvítězil v soutěži děkana Fakulty financí a účetnictví o nejlepší článek v časopise s oponentním řízením.

Vítězný článek Stock market volatility forecasting: Do we need high-frequency data? byl publikován v International Journal of Forecasting v roce 2021 v čísle 3 a jeho dalšími autory byli Štefan Lyosca a Tomáš Výrost.

International Journal of Forecasting je časopis vydávaný společností Elsevier a je indexován ve Web of Science v prvním kvartilu (druhý decil) v oboru 5.2 Economics and Business dle Fordu.

Abstrakt článku: The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.

Odkaz na článek: https://www.sciencedirect.com/science/article/abs/pii/S0169207020301874