The growth of peer-to-peer exchanges and the blockchain technology has led to a proliferation of cryptocurrencies and to a massive increase in the number of investors who actually negotiate digital money. Cryptocurrencies trade at prices which is mainly driven by investor sentiment, becoming a...

This paper tackles a core question of portfolio management: How ‘active' is an active portfolio? To answer this question holistically, we generalise the idea of Active Share by keeping the same calculation methodology but substituting in different types of portfolio and benchmark ‘weights'....

Linear GARCH(1,1) and GJR GARCH(1,1) processes are established as regularly varying, meaning their heavy tails follow a Power Law, under conditions that allow the innovations from the, respective, processes to be either symmetrically distributed or skewed. Skewness is considered a stylized fact...

We introduce tests for multi-horizon superior predictive ability. Rather than comparing forecasts of different models at multiple horizons individually, we propose to jointly consider all horizons within a forecast path. We define the concepts of uniform and average superior predictive ability....

I provide a selective review of recent developments in financial econometrics related to measuring, modeling, forecasting and pricing “good” and “bad” volatilities based on realized variation type measures constructed from high-frequency intraday data. An especially appealing feature of...

We develop FinText, a novel, state-of-the-art, financial word embedding from Dow Jones Newswires Text News Feed Database. Incorporating this word embedding in a machine learning model produces a substantial increase in volatility forecasting performance on days with volatility jumps for 23...

This paper examines, for the first time, the performance of machine learning models in realised volatility forecasting using big data sets such as LOBSTER limit order books and news stories from Dow Jones News Wires for 28 NASDAQ stocks over a sample period of July 27, 2007, to November 18,...

In this supplementary appendix, we first provide a brief R and Python tutorial for the proposed BAC estimator. Then, we describe the implementation of the BAC estimator in case of microstructure noise and jumps. We further present more detailed empirical results for the BAC estimation applied to...

We propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple setting. The volatility forecast function follows a...