By examining data on the gold forward offered rate (GOFO) and lease rates over the period 1996- 2009, we conclude that the convenience yield of gold is better approximated by the lease rate than the interest-adjusted spread of Fama amp; French (1983). Using the latter quantity, we study the...

This paper introduces a new approach to modelling the conditional variance in a multivariate setting. It is essentially a combination of the popular GARCH model class with a spatial component, inspired by generalized space-time models. The resulting spatial GARCH model takes into account both...

In September 2009, G20 paved the way for the mandatory central clearing of over-the-counter (OTC) derivatives, which came into effect in December 2012. This new regulation involves a central clearing counterparty (CCP): a financial institution acting as an intermediary between buyers and sellers...

We investigate the impact of Thompson Reuters News Analytics (TRNA) news sentiment on the price dynamics of natural gas futures traded on the New York Mercantile Exchange (NYMEX). We propose a Local News Sentiment Level (LNSL) model, based on the Local Level model of Durbin and Koopman (2001),...

The media influence our perception of reality and, since we act on those perceptions, reality is in turn affected by the media. News is a rich source of information, but, in addition, the sentiment (i.e., the tone of financial news) tells us how others perceive the financial system and how that...

In this paper we describe an application of machine learning algorithms to the problem of intraday forecasting of a large stock index (EUROSTOXX 50), where we use aggregated high-frequency sentiment in news about equities in the index as the main predictor. We utilize an ingenious combination of...

We propose an ensemble of Long-Short Term Memory (LSTM) Neural Networks for intraday stock predictions, using a large variety of Technical Analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent...