This study investigates the Topological Tail Dependence (TTD) theory's applicability to individual stock volatility and high dimensions. Utilizing a comprehensive dataset from the S&P 100, the research employs various methodologies to test the predictions and implications of the TTD theory. The...

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...

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...

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...

"These lecture notes are thought for Master courses in Finance, Fintech and Quantitative Finance programmes. We fully subscribe to the philosophy that post-graduate students should be offered courses that are really at the cutting edge of the technologies and advances that are disrupting the...

Financial Econometrics is a contribution to modern financial econometrics, overviewing both theory and application. It covers, in detail, three important topics in the field that have recently drawn the attention of the academic community and practitioners, with low-frequency data (trend...

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,...

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...

Machine learning have made a large number of novel applications in various domains in finance. Though more complex and advanced models have been proposed and explored in literatures, the input data to the models and how the raw features are preprocessed, remains important. This article provides...

"A modern introduction to risk and portfolio management for advanced undergraduate and beginning graduate students who will become practitioners in the field of quantitative finance, including extensive live data and Python code which allow the application of theory to real-world situations."

We investigate the economic consequences of statistical learning for arbitrage pricing in a high-dimensional setting. Arbitrageurs learn about alphas from historical data. When alphas are weak and rare, estimation errors hinder arbitrageurs--even those employing optimal machine learning...

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...

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 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 theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

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...

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...

The book has been tested and refined through years of classroom teaching experience. With an abundance of examples, problems, and fully worked out solutions, the text introduces the financial theory and relevant mathematical methods in a mathematically rigorous yet engaging way. This textbook...

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...

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...

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...

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...

One of the most exciting recent developments in financial research is the availability of new administrative, private sector and micro-level datasets that did not exist a few years ago. The unstructured nature of many of these observations, along with the complexity of the phenomena they...

This article reviews ten notable financial applications where ML has moved beyond hype and proven its usefulness. This success does not mean that the use of ML in finance does not face important challenges. The main conclusion is that there is a strong case for applying ML to current financial...

A look at regulatory challenges and recommendation in the age of AI. Investigating topics like monopoly formation, machine learning auditability, bias mitigation strategies and automated regulatory monitoring

In this paper, we will first discuss the theories in financial economics we have been developed. We will then theories on financial econometrics and statistics we have been developed. Thereafter, we will discuss the applications of financial economic theories and financial econometric theories...

We characterize when physical probabilities, marginal utilities, and the discount rate can be recovered from observed state prices for several future time periods. We make no assumptions of the probability distribution, thus generalizing the time-homogeneous stationary model of Ross (2015)....

A representative investor confronts two levels of model uncertainty. The investor has a set of well defined parametric “structured models” but does not know which of them is best. The investor also suspects that all of the structured models are misspecified. These uncertainties about...

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...

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...