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 598

Special Issue Editors


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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

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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

Published Papers (1 paper)

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Research

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 431
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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