Computational Statistics for Finance and Econophysics

Dear Colleagues,

The complexity of financial markets has been well acknowledged for a long time. Many scholars in the field of mathematics and statistics have developed tools with the aim of improving our understanding about financial markets. Nowadays, investors make choices with more awareness, having a deeper knowledge about financial market functioning. Financial analysts make more reliable and accurate predictions about future market conditions thanks to the use of these always more sophisticated statistical models. New estimation techniques make portfolio selection more profitable and effective, including in very large dimensional setting. These achievements have been possible thanks to the positivist approach of econophysics and to advances in disciplines such as mathematical finance, statistics and financial econometrics.

However, despite the incredible results already obtained by the scientific community in the last fifty years, there is still a strong interest in these topics. This is because financial markets are constantly changing and reacting in different ways to external environments and events. Although a level of sophistication has been achieved in recent years, the need for further insights into this complex and fascinating world is, nowadays, stronger than ever. What is true for a given asset class does not necessarily hold for other asset types. Exogenous shocks, which seemed similar to others in the past, generate completely different effects depending on the time of occurrence and, sometimes, the geographical space.

The aim of this Special Issue is to generate a debate among colleagues from different research areas that are interested in finance. There is a need for a deeper understanding of alternative financial markets and their connectiveness with other markets and with the real economy. New mathematical and statistical tools should be developed to model the increasing complexity of the financial markets and to obtain more accurate predictions. Novel empirical regularities of financial returns need to still be explored.

This Special Issue seeks both theoretical and empirical contributions in the area of finance and econophysics. Some of the relevant topics include (but are not limited to) the following:

• New computational and statistical tools for the analysis of financial markets;
• New mathematical and statistical approaches to risk analysis;
• Machine learning and artificial intelligence techniques for uncovering grouping structures in the financial markets;
• Network analysis of financial markets, with a special emphasis on (although not limited to) cryptocurrencies and commodities;
• Models for financial markets’ predictions and forecasting of stock returns, indices, cryptocurrencies and commodities;
• Machine learning and deep learning for asset allocation and portfolio selection;
• Application of reinforcement learning to econophysics;
• Connectiveness analysis of alternative markets with real economy and other markets;
• Event studies and empirical analyses, with a specific emphasis on the effects of recent geopolitical events on financial markets;
• Spillover analyses in the financial markets;
• Application of georeferenced statistical techniques in the financial domain. 

Prof. Dr. Roy Cerqueti
Dr. Raffaele Mattera
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• statistical finance
• econophysics
• financial econometrics
• mathematical finance
• stochastic processes
• machine learning
• deep learning
• financial forecasting
• stock market
• cryptocurrencies
• commodities
• applications of Artificial Intelligence (AI) techniques to finance
• risk management
• computational finance
• empirical finance
• time series