Low-volatility investing is typically implemented by sorting stocks based on simple risk measures; for example, the empirical standard deviation of last year's daily returns. In contrast, we understand identifying next-month's ranking of volatilities as a forecasting problem aimed at the ex-post...

We propose a heterogeneous autoregressive (HAR) model with time-varying parameters in the form of a local linear random forest. In contrast to conventional random forests that approximate the volatility nonparametrically using local averaging, the building blocks of our forest are HAR panel...

We study the relation between a comprehensive set of firm characteristics and the entire universe of individual equity option prices. We find that 42 out of 86 characteristics are priced in the option market, in the sense that they significantly explain differences in the implied volatility...

In this paper we model the adjustment process of European Union Allowance (EUA) prices to the releases of announcements at high-frequency controlling for intraday periodicity, volatility clustering and volatility persistence. We find that the high-frequency EUA price dynamics are very well...

This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model which allows for volatility feedback of either sign, i.e., positive or negative. In the previous literature, negative volatility spillovers were ruled out by the assumption that all...

We investigate the impact of the European Central Bank’s monetary policy communication during the press conference held after the monthly Governing Council meeting on the EUR-USD exchange rate in high-frequency. Based on the method of Content Analysis we construct communication indicators for...

We investigate the question whether macroeconomic variables contain information about future stock volatility beyond that contained in past volatility. We show that forecasts of GDP and industrial production growth from the Federal Reserve's Survey of Professional Forecasters predict volatility...

We use the GARCH-MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500...

We propose a multiplicative factor multi frequency component GARCH model which exploits the empirical fact that the daily standardized forecast errors of one-component GARCH models behave counter-cyclical when averaged at a lower frequency. For the new model, we derive the unconditional variance...