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Risks
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Advances in Volatility Modeling and Risk in Markets
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Advances in Volatility Modeling and Risk in Markets

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

Deadline for manuscript submissions: 1 January 2025 | Viewed by 3824

Special Issue Editors

Dr. Evangelos Giouvris

E-Mail Website
Guest Editor
School of Business and Management, Royal Holloway, Univeristy of London, Egham TW20 0EX, UK
Interests: asset pricing; behavioral finance; portfolio; business cycles; volatility; BRICS; and exchange rates
Dr. Mohammad Sharik Essa

E-Mail Website
Guest Editor
The Claude Littner Business School, University of West London, London W5 5RF, UK
Interests: asset pricing; behavioral finance; and portfolio optimization

Special Issue Information

Dear Colleagues,

Modeling volatility and risks in financial markets/insurance is a classic topic in the area of risk modeling. Although significant research has been conducted within this stream, modelling volatility and risk is ever evolving due to the identification of new risks (or risks that are still not well understood) or unexpected events in financial markets/insurance/commodities/specific industries/countries, the speed at which information travels and the connection between markets.

In the Special Issue, we aspire to provide a ‘showcase’ for all the latest developments in the area of volatility and risk modelling, from a market perspective along with assessing these on a firm level (or countries). We are also interested in extrapolating this to the existence of factor-based premiums, style-based investment strategies and portfolio optimization. We are also keen to look at risks within portfolio construction, be it behavioral from an investor’s perspective (attitudes towards risk) or statistical classifications/characteristics (variance, skewness, kurtosis), along with assessing the impact of macro-level factors/policy decisions (monetary policy/market liquidity) on firm-level risks (or markets) while looking at these facets within recessionary and non-recessionary settings. Finally, we are interested in incorporating behavioral factors within volatility/risk modelling and seeing how this might impact traditional views of modeling.

Keywords: modeling risks; portfolio construction; portfolio risk; factor-based premiums; style investing; time series modeling; panel data modeling; herding; asset pricing; interest rates; firm-level and market-wide illiquidity; default risk; business cycles; extreme events/risks

Dr. Evangelos Giouvris
Dr. Mohammad Sharik Essa
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

  • modeling risks
  • portfolio construction
  • portfolio risk
  • factor based premiums
  • style investing
  • time series modeling
  • panel data modeling
  • herding
  • asset pricing
  • interest rates
  • monetary policy
  • firm level and market–wide illiquidity
  • default risk
  • business cycles
  • extreme events/risks

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Published Papers (3 papers)

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Research

33 pages, 7452 KiB  
Article
Mapping Financial Connections: Market Integration in Emerging Economies through Graph Theory
by Marc Cortés Rufé and Jordi Martí Pidelaserra
Risks 2024, 12(10), 154; https://doi.org/10.3390/risks12100154 - 29 Sep 2024
Viewed by 1053
Abstract
In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial [...] Read more.
In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial stability and market risk. The research addresses a critical gap in the literature; while political and economic linkages between nations have been widely studied, the specific connectivity between sectors within these economies remains underexplored. Our methodology utilizes eigenvector centrality and Euclidean distance to construct a comprehensive network of 106 publicly listed firms from 2013 to 2022, across sectors such as energy, telecommunications, retail, and technology. The primary hypothesis is that sectors with higher centrality scores—indicative of their interconnectedness within the broader financial network—demonstrate greater resilience to market volatility and contribute disproportionately to sectoral profitability. The analysis yielded several key insights. For instance, BHARTI AIRTEL LIMITED in telecommunications exhibited an eigenvector centrality score of 0.9615, positioning it as a critical node in maintaining sectoral stability, while AMBEV SA in the retail sector, with a centrality score of 0.9938, emerged as a pivotal player influencing both profitability and risk. Sectors led by companies with high centrality showed a 20% increase in risk-adjusted returns compared to less connected entities, supporting the hypothesis that central firms act as stabilizers in fluctuating market conditions. The findings underscore the practical implications for policymakers and investors alike. Understanding the structure of these networks allows for more informed decision making in terms of investment strategies and macroeconomic policy. By identifying the central entities within these economic systems, both policymakers and investors can target their efforts more effectively, either to support growth initiatives or to mitigate systemic risks. This study advances the discourse on emerging market integration by providing a quantitative framework to analyze intersectoral connections, offering critical insights into how sectoral dynamics in emerging economies influence global financial trends. Full article
(This article belongs to the Special Issue Advances in Volatility Modeling and Risk in Markets)
18 pages, 3414 KiB  
Article
The Impact of Financial Stress and Uncertainty on Green and Conventional Bonds and Stocks: A Nonlinear and Nonparametric Quantile Analysis
by Muhammad Mar’I, Mehdi Seraj and Turgut Tursoy
Risks 2024, 12(8), 120; https://doi.org/10.3390/risks12080120 - 31 Jul 2024
Viewed by 982
Abstract
This study aims to investigate the impact of financial stress and uncertainty on the returns of green and conventional bonds and stocks in the United States from 2010 to 2022. The research utilizes nonlinear and nonparametric analysis, which includes the quantile-on-quantile and nonparametric [...] Read more.
This study aims to investigate the impact of financial stress and uncertainty on the returns of green and conventional bonds and stocks in the United States from 2010 to 2022. The research utilizes nonlinear and nonparametric analysis, which includes the quantile-on-quantile and nonparametric causality-in-quantiles approaches to examine the relationship between variables. The data analyzed using R programming language show that financial stress positively impacts the middle quantiles of both conventional and green equity, while financial uncertainty negatively impacts upper quantiles. The study also finds that financial stress has a more significant impact on all types of bonds compared to financial uncertainty, with conventional bonds being more affected. This study proposes a pyramid that classifies financial assets based on their susceptibility to financial stress, which could help investors evaluate risk levels and make better investment decisions. The study recommends that policymakers should encourage green investments by offering incentives, such as tax credits. They should also focus on enhancing the efficiency of volatile assets by implementing new investment rules and regulations. Full article
(This article belongs to the Special Issue Advances in Volatility Modeling and Risk in Markets)
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10 pages, 2477 KiB  
Article
Multi-Timescale Recurrent Neural Networks Beat Rough Volatility for Intraday Volatility Prediction
by Damien Challet and Vincent Ragel
Risks 2024, 12(6), 84; https://doi.org/10.3390/risks12060084 - 22 May 2024
Viewed by 1172
Abstract
We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or highly disparate timescales. We compare the ability of vanilla and extended long short-term memory [...] Read more.
We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or highly disparate timescales. We compare the ability of vanilla and extended long short-term memory networks (LSTMs) to predict the intraday volatility of a collection of equity indices known to have a long memory. Generally, the number of epochs needed to train the extended LSTMs is divided by about two, while the variation in validation and test losses among models with the same hyperparameters is much smaller. We also show that the single model with the smallest validation loss systemically outperforms rough volatility predictions for the average intraday volatility of equity indices by about 20% when trained and tested on a dataset with multiple time series. Full article
(This article belongs to the Special Issue Advances in Volatility Modeling and Risk in Markets)
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