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Algorithms
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Algorithms for Computational Finance
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Algorithms for Computational Finance

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (1 August 2022) | Viewed by 3142

Special Issue Editors

Prof. Dr. Liviu-Adrian Cotfas

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, 010374 București, Romania
Interests: machine learning; deep learning; natural language processing; social media analysis; agent-based modeling; recommender systems; semantic web; grey systems theory
Special Issues, Collections and Topics in MDPI journals
Dr. Stanimir Kabaivanov

E-Mail Website
Guest Editor
Faculty of Economic and Social science, University of Plovdiv "Paisii Hilendarski", 4000 Tsentar, Plovdiv, Bulgaria
Interests: corporate finance; neural networks; computational finance

Special Issue Information

Dear Colleagues,

Algorithms for computational finance have gone a very long way since the first attempts to use computer-aided analysis. Yet we have witnessed also development of new financial tools, different markets and recently the advance in digital assets and cryptocurrencies.

As financial markets evolve, so have done the algorithmic trading and application of machine learning and AI in security analysis and forecasting. This special issue aims to attract submissions that represent state-of-the-art studies in algorithms in computational finance. We welcome high quality, original ideas and research in algorithms in computational finance, and in the following particular areas:

  • Forecasting and trading algorithms for digital assets and cryptocurrencies.
  • Artificial intelligence and machine learning applications in big data finance.
  • Algorithms for market analysis considering differences in trading frequency and data availability.
  • Portfolio optimization algorithms with novel risk metrics.
  • Market simulations, algorithmic trading and hedging strategies.

Prof. Dr. Liviu-Adrian Cotfas
Dr. Stanimir Kabaivanov
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. Algorithms 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 1600 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.

Keywords

  •  forecasting and trading algorithms for digital assets and cryptocurrencies.
  •  artificial intelligence and machine learning applications in big data finance.
  •  algorithms for market analysis considering differences in trading frequency and data availability.
  •  portfolio optimization algorithms with novel risk metrics.
  •  market simulations, algorithmic trading and hedging strategies

Published Papers (2 papers)

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Research

16 pages, 1647 KiB  
Article
Intensity and Direction of Volatility Spillover Effect in Carbon–Energy Markets: A Regime-Switching Approach
by Leon Li
Algorithms 2022, 15(8), 264; https://0-doi-org.brum.beds.ac.uk/10.3390/a15080264 - 28 Jul 2022
Cited by 1 | Viewed by 1130
Abstract
This paper advances a volatility-regime-switching mechanism to investigate the intensity and direction of the volatility spillover effect in carbon–energy markets. Switching between a low-volatility (LV) and high-volatility (HV) regime, our mechanism involves a four-state system (i.e., LV-LV, HV-LV, LV-HV and HV-HV). Our findings [...] Read more.
This paper advances a volatility-regime-switching mechanism to investigate the intensity and direction of the volatility spillover effect in carbon–energy markets. Switching between a low-volatility (LV) and high-volatility (HV) regime, our mechanism involves a four-state system (i.e., LV-LV, HV-LV, LV-HV and HV-HV). Our findings are listed as follows: First, the highest EUA–WTI correlation occurs when both are in an HV regime (i.e., HV-HV), revealing the intensity of the volatility spillover effect. Second, when EUA and WTI are experiencing an opposite volatility regime (one in LV and the other in HV), a higher EUA–WTI correlation is observed when WTI is in an HV regime. This result implies that the direction of the volatility spillover effect is from the energy market to the carbon market. Third, the regime-switching model involving the non-uniform volatility–correlation relations outperforms the conventional GARCH and DCC models in volatility forecasting and portfolio construction. Full article
(This article belongs to the Special Issue Algorithms for Computational Finance)
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17 pages, 351 KiB  
Article
Hypothesis Testing Fusion for Nonlinearity Detection in Hedge Fund Price Returns
by Jean-Marc Le Caillec
Algorithms 2022, 15(8), 260; https://0-doi-org.brum.beds.ac.uk/10.3390/a15080260 - 26 Jul 2022
Viewed by 1344
Abstract
In this paper, we present the results of nonlinearity detection in Hedge Fund price returns. The main challenge is induced by the small length of the time series, since the return of this kind of asset is updated once a month. As usual, [...] Read more.
In this paper, we present the results of nonlinearity detection in Hedge Fund price returns. The main challenge is induced by the small length of the time series, since the return of this kind of asset is updated once a month. As usual, the nonlinearity of the return time series is a key point to accurately assess the risk of an asset, since the normality assumption is barely encountered in financial data. The basic idea to overcome the hypothesis testing lack of robustness on small time series is to merge several hypothesis tests to improve the final decision (i.e., the return time series is linear or not). Several aspects on the index/decision fusion, such as the fusion topology, as well as the shared information by several hypothesis tests, have to be carefully investigated to design a robust decision process. This designed decision rule is applied to two databases of Hedge Fund price return (TASS and SP). In particular, the linearity assumption is generally accepted for the factorial model. However, funds having detected nonlinearity in their returns are generally correlated with exchange rates. Since exchange rates nonlinearly evolve, the nonlinearity is explained by this risk factor and not by a nonlinear dependence on the risk factors. Full article
(This article belongs to the Special Issue Algorithms for Computational Finance)
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