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Risks
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Financial Networks in Fintech Risk Management
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Financial Networks in Fintech Risk Management

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 30079

Special Issue Editor

Prof. Dr. Paolo Giudici

E-Mail Website
Guest Editor
Department of Economics and Management, University of Pavia, 27100 Pavia, Italy
Interests: financial data science; graphical models; network models; financial networks; systemic risk; financial risk management; fintech risk management; explainable artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

FinTech (Financial Technology) means “technology-enabled financial innovation”. Examples of FinTech innovations are peer to peer lending, robot advisory, and blockchain innovative and crypto payments. While FinTech innovations are competitive and bring higher financial inclusion, along with improved user experience, they also increase risks and, particularly, financial risks (credit, market, systemic, cyber, and operational risks).

There is a strong need to improve the competitiveness of FinTech innovations, creating a common regulatory approach across all countries that can make FinTech innovation sustainable. This can help to encourage innovations in the financial industry, in the application of big data, artificial intelligence, and blockchain technologies, while authorities and researchers assess their risks.

I believe that FinTech innovations themselves generate the data which can be leveraged to build appropriate FinTech risk management models. FinTech innovations generate “alternative” data: peer-to-peer financial and social transactions among the users. This source of data can be usefully analyzed, by means of network models, to complement and/or substitute banking and financial data employed in traditional risk management models, leading to more explainable results and more accurate predictions, eventually leading to more trust in FinTech innovations.

This Special Issue aims to collect original papers that contribute to the development of new fintech risk management models, based on the modeling of alternative data by means of network models.

Prof. Dr. Paolo Giudici
Guest Editor

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. Risks 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 1800 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

  • Fintech risk management
  • Peer to Peer data
  • Network models
  • Explainable artificial intelligence
  • Predictive accuracy

Published Papers (5 papers)

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Research

15 pages, 2674 KiB  
Article
Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall?
by Marina Resta, Paolo Pagnottoni and Maria Elena De Giuli
Risks 2020, 8(2), 44; https://doi.org/10.3390/risks8020044 - 06 May 2020
Cited by 20 | Viewed by 6823
Abstract
In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin market by using trend-following and mean-reverting strategies. We applied our strategies on the Bitcoin price series sampled both at 5-min intervals and on a daily basis, during [...] Read more.
In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin market by using trend-following and mean-reverting strategies. We applied our strategies on the Bitcoin price series sampled both at 5-min intervals and on a daily basis, during the period 1 January 2012 to 20 August 2019. Our findings suggest that, overall, trading on daily data is more profitable than going intraday. Furthermore, we concluded that the Buy and Hold strategy outperforms the examined alternatives on an intraday basis, while Simple Moving Averages yield the best performances when dealing with daily data. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management)
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14 pages, 554 KiB  
Article
Financial Bubbles: A Study of Co-Explosivity in the Cryptocurrency Market
by Arianna Agosto and Alessia Cafferata
Risks 2020, 8(2), 34; https://doi.org/10.3390/risks8020034 - 09 Apr 2020
Cited by 40 | Viewed by 6724
Abstract
Cryptocurrencies have recently captured the interest of the econometric literature, with several works trying to address the existence of bubbles in the price dynamics of Bitcoins and other cryptoassets. Extremely rapid price accelerations, often referred to as explosive behaviors, followed by drastic drops [...] Read more.
Cryptocurrencies have recently captured the interest of the econometric literature, with several works trying to address the existence of bubbles in the price dynamics of Bitcoins and other cryptoassets. Extremely rapid price accelerations, often referred to as explosive behaviors, followed by drastic drops pose high risks to investors. From a risk management perspective, testing the explosiveness of individual cryptocurrency time series is not the only crucial issue. Investigating co-explosivity in the cryptoassets, i.e., whether explosivity in one cryptocurrency leads to explosivity in other cryptocurrencies, allows indeed to take into account possible shock propagation channels and improve the prediction of market collapses. To this aim, our paper investigates the relationships between the explosive behaviors of cryptocurrencies through a unit root testing approach. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management)
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32 pages, 3788 KiB  
Article
The Leaders, the Laggers, and the “Vulnerables”
by Veni Arakelian and Shatha Qamhieh Hashem
Risks 2020, 8(1), 26; https://doi.org/10.3390/risks8010026 - 12 Mar 2020
Cited by 2 | Viewed by 2971
Abstract
We examine the lead-lag effect between the large and the small capitalization financial institutions by constructing two global weekly rebalanced indices. We focus on the 10% of stocks that “survived” all the rebalancings by remaining constituents of the indices. We sort them according [...] Read more.
We examine the lead-lag effect between the large and the small capitalization financial institutions by constructing two global weekly rebalanced indices. We focus on the 10% of stocks that “survived” all the rebalancings by remaining constituents of the indices. We sort them according to their systemic importance using the marginal expected shortfall (MES), which measures the individual institutions’ vulnerability over the market, the network based MES, which captures the vulnerability of the risks generated by institutions’ interrelations, and the Bayesian network based MES, which takes into account different network structures among institutions’ interrelations. We also check if the lead-lag effect holds in terms of systemic risk implying systemic risk transmission from the large to the small capitalization, concluding a mixed behavior compared to the index returns. Additionally, we find that all the systemic risk indicators increase their magnitude during the financial crisis. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management)
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14 pages, 6949 KiB  
Article
Lead Behaviour in Bitcoin Markets
by Ying Chen, Paolo Giudici, Branka Hadji Misheva and Simon Trimborn
Risks 2020, 8(1), 4; https://doi.org/10.3390/risks8010004 - 04 Jan 2020
Cited by 7 | Viewed by 8353
Abstract
We aim to understand the dynamics of Bitcoin blockchain trading volumes and, specifically, how different trading groups, in different geographic areas, interact with each other. To achieve this aim, we propose an extended Vector Autoregressive model, aimed at explaining the evolution of trading [...] Read more.
We aim to understand the dynamics of Bitcoin blockchain trading volumes and, specifically, how different trading groups, in different geographic areas, interact with each other. To achieve this aim, we propose an extended Vector Autoregressive model, aimed at explaining the evolution of trading volumes, both in time and in space. The extension is based on network models, which improve pure autoregressive models, introducing a contemporaneous contagion component that describes contagion effects between trading volumes. Our empirical findings show that transactions activities in bitcoins is dominated by groups of network participants in Europe and in the United States, consistent with the expectation that market interactions primarily take place in developed economies. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management)
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18 pages, 856 KiB  
Article
High Frequency Price Change Spillovers in Bitcoin Markets
by Paolo Giudici and Paolo Pagnottoni
Risks 2019, 7(4), 111; https://doi.org/10.3390/risks7040111 - 01 Nov 2019
Cited by 45 | Viewed by 4608
Abstract
The study of connectedness is key to assess spillover effects and identify lead-lag relationships among market exchanges trading the same asset. By means of an extension of Diebold and Yilmaz (2012) econometric connectedness measures, we examined the relationships of five major Bitcoin exchange [...] Read more.
The study of connectedness is key to assess spillover effects and identify lead-lag relationships among market exchanges trading the same asset. By means of an extension of Diebold and Yilmaz (2012) econometric connectedness measures, we examined the relationships of five major Bitcoin exchange platforms during two periods of main interest: the 2017 surge in prices and the 2018 decline. We concluded that Bitfinex and Gemini are leading exchanges in terms of return spillover transmission during the analyzed time-frame, while Bittrexs act as a follower. We also found that connectedness of overall returns fell substantially right before the Bitcoin price hype, whereas it leveled out during the period the down market period. We confirmed that the results are robust with regards to the modeling strategies. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management)
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