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Risks
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Data Analysis and Financial Risk Management in Financial Markets
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Data Analysis and Financial Risk Management in Financial Markets

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 5396

Special Issue Editors

Dr. Xinwen Ni

E-Mail Website
Guest Editor
School of Business and Economics, Humboldt University of Berlin, 10117 Berlin, Germany
Interests: quantitative finance; machine learning methods; FinTech; NLP; quantitative economics; policy risks
Prof. Dr. Wolfgang Karl Härdle

E-Mail Website
Guest Editor
School of Business and Economics, Humboldt University of Berlin, 10117 Berlin, Germany
Interests: nonparametric methods; quantitative methods; machine learning; FinTech; cryptocurrency; risk management

Special Issue Information

Dear Colleagues,

Based on the present situation, we invite you to submit papers to be published in the Special Issue “Data Analysis and Financial Risk Management in Financial Markets”. The main motivation for this volume is to provide the recent results of research in the area of methodological development and new types of data to be applied in widely understood risk management in financial markets.

We welcome submissions that address theoretical and empirical research, as well as policy-oriented research papers. We encourage sharing the results to strengthen the knowledge of all areas of finance, risk management, insurance and FinTech for a broad audience of academic researchers, industry professionals and regulators.

We especially encourage research that focuses on statistical methods for quantitative risk management, new proposals of machine learning, analysis of new types of data, discussions of increased systematic risks and risk assessment criteria, including but not limited to: Decentralized Finance, Sustainable Finance, Climate Change, AI/Machine Learning/Big Data, Energy and Environmental Challenges.

Dr. Xinwen Ni
Prof. Dr. Wolfgang Karl Härdle
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. 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

  • risk management
  • credit risks
  • systematic risks
  • risk criteria
  • new types of data
  • machine learning

Published Papers (2 papers)

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Research

18 pages, 2313 KiB  
Article
Pricing Kernels and Risk Premia implied in Bitcoin Options
by Julian Winkel and Wolfgang Karl Härdle
Risks 2023, 11(5), 85; https://doi.org/10.3390/risks11050085 - 30 Apr 2023
Viewed by 1530
Abstract
Bitcoin Pricing Kernels (PKs) are estimated using a novel data set from Deribit, the leading Bitcoin options exchange. The PKs, as the ratio between risk-neutral and physical density, dynamically reflect the change in investor preferences. Thus, the PKs improve the understanding of investor [...] Read more.
Bitcoin Pricing Kernels (PKs) are estimated using a novel data set from Deribit, the leading Bitcoin options exchange. The PKs, as the ratio between risk-neutral and physical density, dynamically reflect the change in investor preferences. Thus, the PKs improve the understanding of investor expectations and risk premiums in a new asset class. Bootstrap-based confidence bands are estimated in order to validate the results. Investors are heterogeneous in their risk profiles and preferences with respect to volatility and investment horizon. The empirical PKs turn out to be U-shaped for short-dated instruments and W-shaped for long-dated instruments. We find that investors are willing to pay a substantial risk premium to insure themselves against short-term price movements. The risk premium is smaller for longer-dated instruments and their traders are risk averse. The shape of the empirical PKs reveals the existence of a time-varying risk premium. The similarity between the shape of empirical PKs for Bitcoin and other markets that represent aggregate wealth shows that Bitcoin is becoming an established asset class. Full article
(This article belongs to the Special Issue Data Analysis and Financial Risk Management in Financial Markets)
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21 pages, 2093 KiB  
Article
A Wavelet Analysis of the Dynamic Connectedness among Oil Prices, Green Bonds, and CO2 Emissions
by Nini Johana Marín-Rodríguez, Juan David González-Ruiz and Sergio Botero
Risks 2023, 11(1), 15; https://doi.org/10.3390/risks11010015 - 9 Jan 2023
Cited by 13 | Viewed by 3402
Abstract
Wavelet power spectrum (WPS) and wavelet coherence analyses (WCA) are used to examine the co-movements among oil prices, green bonds, and CO2 emissions on daily data from January 2014 to October 2022. The WPS results show that oil returns exhibit significant volatility [...] Read more.
Wavelet power spectrum (WPS) and wavelet coherence analyses (WCA) are used to examine the co-movements among oil prices, green bonds, and CO2 emissions on daily data from January 2014 to October 2022. The WPS results show that oil returns exhibit significant volatility at low and medium frequencies, particularly in 2014, 2019–2020, and 2022. Also, the Green Bond Index presents significant volatility at the end of 2019–2020 and the beginning of 2022 at low, medium, and high frequencies. Additionally, CO2 futures’ returns present high volatility at low and medium frequencies, expressly in 2015–2016, 2018, the end of 2019–2020, and 2022. WCA’s empirical findings reveal (i) that oil returns have a negative impact on the Green Bond Index in the medium term. (ii) There is a strong interdependence between oil prices and CO2 futures’ returns, in short, medium, and long terms, as inferred from the time–frequency analysis. (iii) There also is evidence of strong short, medium, and long terms co-movements between the Green Bond Index and CO2 futures’ returns, with the Green Bond Index leading. Full article
(This article belongs to the Special Issue Data Analysis and Financial Risk Management in Financial Markets)
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