In recent years support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving...

In recent years, support vector regressions (SVRs), a novel artificial neural network (ANN) technique, has been successfully used as a nonparametric tool for regression estimation and forecasting time series data. In this thesis, we deal with the application of SVRs in financial markets...

The paper aims to assess, from an empirical viewpoint, the advantages of a stablecoin whose value is derived from a basket of underlying currencies, against a stablecoin which is pegged to the value of one major currency, such as the dollar. To this aim, we first find the optimal weights of the...

This paper studies the nature of volatility spillovers across countries from the per-spective of network theory and by relying on data of US-listed ETFs. I use a Lasso-related technique to estimate the International Volatility Network (IVN) where the nodes correspond to large-cap international...

This paper studies the undirected partial-correlation stock network for the Spanish market that considers the constituents of IBEX-35 as nodes and their partial correlations of returns as links. I propose a novel methodology that combines a recently developed variable selection method, Graphical...

This paper studies the undirected partial-correlation stock network for the Spanish market that considers the constituents of IBEX-35 as nodes and their partial correlations of returns as links. I propose a novel methodology that combines a recently developed variable selection method, Graphical...

This paper proposes a multivariate fuzzy logic approach to boosting the profitability of technical analysis for currency trading. The approach incorporates information on underlying market volatility in addition to order-flow-based exchange-rate return forecasts. We show the superiority of our...

We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular we analyse the hedging performance of the original architecture under rough volatility models with view to existing theoretical results for those....

The financial markets are found to be finite Hilbert space, inside which the stocks are displaying their wave-particle duality. The Reynolds number, an age old fluid mechanics theory, has been redefined in investment finance domain to identify possible explosive moments in the stock exchange....

This paper provides statistical learning techniques for determining the full own-price market impact and the relevance and effect of cross-price and cross-asset spillover channels from intraday transactions data. The novel tools allow extracting comprehensive information contained in the limit...