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Wavelet Applications in Finance
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Wavelet Applications in Finance

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Economics and Finance".

Deadline for manuscript submissions: closed (6 December 2021) | Viewed by 12553

Special Issue Editor

Dr. Alessandra Canepa

E-Mail Website
Guest Editor
Department of Economics and Statistics "Cognetti de Martiis", Campus "Luigi Einaudi", Lungo Dora Siena 100 A, 10153 Torino, Italy
Interests: econometrics; financial econometrics; climate change econometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although wavelet methods have been heavily used in other disciplines such as acoustics, astronomy, engineering, medicine, forensics, and physics, applications in economics and finance are relatively new. Wavelet decomposition methods are robust to nonlinearity and structural breaks in the series under investigation and could be used to analyse market behaviour in situations of market distress. Additionally, a key feature of wavelets relates to their capacity to uncover latent processes that affect the structural behaviour of the series, such as cycle patterns, trends, and lead–lag interactions, which are characteristic features of financial time series.

The aim of the Special Issue is to investigate the usefulness of wavelet methods in financial applications. Potential applications of wavelet analysis in finance are in density estimation, time scale decomposition, and forecasting. Empirical studies analysing the forecasting properties of models generated using wavelet decomposition would be particularly welcome.  The study of financial contagion and risk management using wavelet methodology is also of interest.

Prof. Dr. Alessandra Canepa
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. Journal of Risk and Financial Management 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 1400 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

  • wavelet decomposition
  • wavelet forecasting
  • financial market contagion
  • risk management using wavelet.

Published Papers (4 papers)

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Research

22 pages, 3060 KiB  
Article
Multiscale Partial Correlation Clustering of Stock Market Returns
by Antonis A. Michis
J. Risk Financial Manag. 2022, 15(1), 24; https://doi.org/10.3390/jrfm15010024 - 09 Jan 2022
Cited by 8 | Viewed by 2369
Abstract
This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market [...] Read more.
This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation. Full article
(This article belongs to the Special Issue Wavelet Applications in Finance)
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29 pages, 55661 KiB  
Article
A Wavelet Perspective of Crisis Contagion between Advanced Economies and the BRIC Markets
by Constantin Gurdgiev and Conor O’Riordan
J. Risk Financial Manag. 2021, 14(10), 503; https://doi.org/10.3390/jrfm14100503 - 19 Oct 2021
Cited by 4 | Viewed by 1550
Abstract
This paper investigates the relationship between the BRICs’ and the advanced economies’ stock markets from 2000 to 2016 utilizing continuous wavelet transform. The continuous wavelet transform allows us to explore these relationships in the time–frequency domain to capture short- and long-term investors’ perspectives. [...] Read more.
This paper investigates the relationship between the BRICs’ and the advanced economies’ stock markets from 2000 to 2016 utilizing continuous wavelet transform. The continuous wavelet transform allows us to explore these relationships in the time–frequency domain to capture short- and long-term investors’ perspectives. Bi-directional spillovers are captured in terms of returns and volatility. In addition to covering the periods of the dot.com crash, the 11 September 2001 events, the pre-2007 financialization bubble period and the resulting Global Financial Crisis, we study volatility spillovers arising from the BRIC, U.S. and European market shocks post the Global Financial Crisis. Based on our results, we confirm findings in relatively fragmented literature that document time-varying and imperfect BRIC markets’ integration with mature economies. Overall, we show that arbitrage opportunities continue to exist in international stock market portfolios with respect to BRIC assets. In a major addition to the literature, our study captures spillovers from the advanced economies’ shocks to BRIC markets, as well as contagion from BRIC markets’ shocks to advanced economies’ markets. Full article
(This article belongs to the Special Issue Wavelet Applications in Finance)
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18 pages, 1715 KiB  
Article
Evidence of Stock Market Contagion during the COVID-19 Pandemic: A Wavelet-Copula-GARCH Approach
by Huthaifa Alqaralleh and Alessandra Canepa
J. Risk Financial Manag. 2021, 14(7), 329; https://doi.org/10.3390/jrfm14070329 - 15 Jul 2021
Cited by 27 | Viewed by 4008
Abstract
In this study, we propose a wavelet-copula-GARCH procedure to investigate the occurrence of cross-market linkages during the COVID-19 pandemic. To explore cross-market linkages, we distinguish between regular interdependence and pure contagion, and associate changes in the correlation between stock market returns at higher [...] Read more.
In this study, we propose a wavelet-copula-GARCH procedure to investigate the occurrence of cross-market linkages during the COVID-19 pandemic. To explore cross-market linkages, we distinguish between regular interdependence and pure contagion, and associate changes in the correlation between stock market returns at higher frequencies with contagion, whereas changes at lower frequencies are associated with interdependence that relates to spillovers of shocks resulting from the normal interdependence between markets. An empirical analysis undertaken on six major stock markets reveals evidence of long-run interdependence between the markets under consideration before the start of the COVID-19 pandemic in December 2019. However, after the health crisis began, strong evidence of pure contagion among stock markets was detected. Full article
(This article belongs to the Special Issue Wavelet Applications in Finance)
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19 pages, 7887 KiB  
Article
Time-Varying Nexus between Investor Sentiment and Cryptocurrency Market: New Insights from a Wavelet Coherence Framework
by Hashem A. AlNemer, Besma Hkiri and Muhammed Asif Khan
J. Risk Financial Manag. 2021, 14(6), 275; https://doi.org/10.3390/jrfm14060275 - 18 Jun 2021
Cited by 7 | Viewed by 3417
Abstract
This study attempts to investigate the nexus between investor sentiment and cryptocurrencies prices. Our empirical investigation merges bivariate and multivariate wavelet tools to examine the investor sentiment nexus to inter-cryptocurrencies prices. The study outcomes show that the Sentix Investor Confidence index provides significant [...] Read more.
This study attempts to investigate the nexus between investor sentiment and cryptocurrencies prices. Our empirical investigation merges bivariate and multivariate wavelet tools to examine the investor sentiment nexus to inter-cryptocurrencies prices. The study outcomes show that the Sentix Investor Confidence index provides significant information in explaining long-term changes in Bitcoin and Litecoin prices. Moreover, the findings generated from the multiple wavelet coherence illustrate the simultaneous contribution of cryptocurrencies and the Sentix Investor Confidence index in explaining the Bitcoin index movement across frequencies and over horizons, especially during bubble burst periods. The study also suggests a time-dependent relationship of Bitcoin prices with alternative cryptocurrencies and the Sentix Investor Confidence index, mostly pronounced during the Bitcoin bubble. We discuss our results using GSV-based investor sentiment. Our findings remain robust and confirm the strong predictive power of investor sentiment in cryptocurrencies price movements over time and across scales. Full article
(This article belongs to the Special Issue Wavelet Applications in Finance)
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