The COVID-19 pandemic has introduced significant uncertainty across various economic sectors, most notably in the industrial sector due to the high volatility in copper futures markets. These markets play a crucial role in the construction, electrical networks, electronic products, and...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

The research field related to finance has made great progress in recent years due to the development of information processing technology and the availability of large-scale data. This special issue is a collection of 16 articles on empirical finance and one book review. The content is six...

We propose a belief-generating model from which we build a statistical measure of investor disagreement. We simulate differences in beliefs across investors by endowing them with different machine learning models for forecasting returns from the same set of inputs. We measure disagreement as the...

We propose a multiplicative dynamic factor structure for the conditional modelling of the variances of an N-dimensional vector of financial returns. We identify common and idiosyncratic conditional volatility factors. The econometric framework is based on an observation-driven time series model...

This paper empirically evidences the role played by board characteristics (skills, diversity, structure, independence) in supporting risk management disclosure and shaping the financial performance of European companies operating in the financial services sector. We exploit data selected from...

This study is the first to investigate the efficient market hypothesis in its weak form and the random walk behaviour of globally listed private equity (LPE) markets represented by nine global, regional, and style indices based on weekly data covering the period from January 2004 to December...

The purpose of the Special Issue "Quantitative Methods in Economics and Finance" of the journal Risks was to provide a collection of papers that reflect the latest research and problems of pricing complex derivates, simulation pricing, analysis of financial markets, and volatility of exchange...

A representative investor does not know which member of a set of well-defined parametric "structured models'' is best. The investor also suspects that all of the structured models are misspecified. These uncertainties about probability distributions of risks give rise to components of...

This paper empirically evidences the role played by board characteristics (skills, diversity, structure, independence) in supporting risk management disclosure and shaping the financial performance of European companies operating in the financial services sector. We exploit data selected from...

The purpose of the Special Issue "Quantitative Methods in Economics and Finance" of the journal Risks was to provide a collection of papers that reflect the latest research and problems of pricing complex derivates, simulation pricing, analysis of financial markets, and volatility of exchange...

This study is the first to investigate the efficient market hypothesis in its weak form and the random walk behaviour of globally listed private equity (LPE) markets represented by nine global, regional, and style indices based on weekly data covering the period from January 2004 to December...

We propose a multiplicative dynamic factor structure for the conditional modelling of the variances of an N-dimensional vector of financial returns. We identify common and idiosyncratic conditional volatility factors. The econometric framework is based on an observation-driven time series model...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

This open access book is the documentary of the Second International Forum on Financial Mathematics and Financial Technology, with focus on selected aspects of the current and upcoming trends in FinTech. In detail, the included scientific papers cover financial mathematics and FinTech,...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

The research field related to finance has made great progress in recent years due to the development of information processing technology and the availability of large-scale data. This special issue is a collection of 16 articles on empirical finance and one book review. The content is six...

We consider a general class of observation-driven models with exogenous regressors for double bounded data that are based on the beta distribution. We obtain a stationary and ergodic beta observation-driven process subject to a contraction condition on the stochastic dynamic model equation. We...

We consider a general class of observation-driven models with exogenous regressors for double bounded data that are based on the beta distribution. We obtain a stationary and ergodic beta observation-driven process subject to a contraction condition on the stochastic dynamic model equation. We...

The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network...

Between 2009 and 2011, the Spanish banking system underwent a restructuring process based on consolidation of savings banks. The program's design allows us to study how alternative forms of consolidation affect credit supply and financial stability. Compared to bank business groups, we find that...

PART 1. INTRODUCTION AND ANALYTICS MODELS -- Retraining and Reskilling Financial Participators in the Digital Age -- Basics of Financial Data Analytics -- Predictive Analytics Techniques: Theory and Applications in Finance -- Prescriptive Analytics Techniques: Theory and Applications in Finance...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data...

"Financial Economics and Econometrics provides an overview of the core topics in theoretical and empirical finance, with an emphasis on applications and interpreting results. Structured in five parts, the book covers financial data and univariate models; asset returns; interest rates, yields and...

Terms and Essays -- Deposit Insurance Schemes -- Gramm-Leach-Bliley Act: Creating a New Bank for a New Millennium -- Pre-funded Coupon and Zero-Coupon Bonds: Cost of Capital Analysis -- Intertemporal Risk and Currency Risk -- Credit Derivatives -- Foreign exchange risk premium and policy...

After the Covid-shock in March 2020, stock prices declined abruptly, reflecting both the deterioration of investors' expectations of economic activity as well as the surge in aggregate risk aversion. In the following months however, whereas economic activity remained sluggish, equity markets...

Missing data for return predictors is a common problem in cross sectional asset pricing. Most papers do not explicitly discuss how they deal with missing data but conventional treatments focus on the subset of firms with no missing data for any predictor or impute the unconditional mean. Both...

This paper develops a fully-fledged statistical arbitrage strategy based on a mean-reverting jump-diffusion model and applies it to high-frequency data of the S&P 500 constituents from January 1998-December 2015. In particular, the established stock selection and trading framework identifies...

Today we live in a post-truth and highly digitalized era characterized by a flow of (mis-) information around the world. Identifying the impact of this information on stock markets and forecasting stock returns and volatilities has become a much more difficult task, perhaps almost impossible....

Financial econometrics has developed into a very fruitful and vibrant research area in the last two decades. The availability of good data promotes research in this area, specially aided by online data and high-frequency data. These two characteristics of financial data also create challenges...

The first essay presents new empirical findings which are inconsistent with prominent theories on the investment premium. The investment premium is the positive stock return difierential between firms with low and high asset growth. Asset growth is the annual percentage change in total assets...

Today we live in a post-truth and highly digitalized era characterized by a flow of (mis-) information around the world. Identifying the impact of this information on stock markets and forecasting stock returns and volatilities has become a much more difficult task, perhaps almost impossible....