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Risks
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Time Series Modeling for Finance and Insurance
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Time Series Modeling for Finance and Insurance

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

Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 12095

Special Issue Editors

Prof. Dr. Lorenzo Mercuri

E-Mail Website
Guest Editor
Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy
Interests: computational finance; option pricing; stochastic processes; stochastic mortality models
Dr. Edit Rroji

E-Mail Website
Guest Editor
Department of Statistics and Quantitative Methods, University of Milano-Bicocca, 20126 Milan, Italy
Interests: quantitative finance; insurance; computational finance

Special Issue Information

Dear Colleagues,

Recent developments in the insurance and financial industry call for appropriate forecasting techniques. The wide set of dependence structures of the financial and insurance datasets has required a rapid development of complex dynamic models and, at same time, has given a boost to the search for new methodologies. Standard discrete and continuous-time models have been extended in order to be able to reproduce well known stylized facts of financial and insurance time series and to reduce forecasting error.

In this context, this Special Issue is devoted to the collection of the latest developments on topics from many areas, such as modelling of time series, machine learning, evaluation of financial/insurance contracts, stochastic volatility modelling, and modelling of big data. Codes and/or pseudocodes for the used algorithms/estimation procedures will be made available.

Prof. Dr. Lorenzo Mercuri
Dr. Edit Rroji
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

  • forecasting
  • time-series modeling
  • uncertainty
  • mathematical finance
  • insurance
  • machine learning

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Published Papers (2 papers)

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Research

16 pages, 976 KiB  
Article
Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models
by Abdullah H. Alenezy, Mohd Tahir Ismail, Sadam Al Wadi and Jamil J. Jaber
Risks 2023, 11(7), 121; https://doi.org/10.3390/risks11070121 - 4 Jul 2023
Cited by 5 | Viewed by 1825
Abstract
We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the [...] Read more.
We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the model’s parameters based on the error function gradient, while the HyFIS approach combines the advantages of neural networks and fuzzy logic systems to create a more robust and accurate learning model. The MODWT uses five mathematical functions to form a discrete wavelet basis. The dataset used includes the daily closing prices of the Tadawul stock market from August 2011 to December 2019. Inputs were selected based on multiple regression, tolerance, and variance inflation factor tests, and the oil price (Loil) and repo rate (Repo) were identified as input variables. The output variable is represented by the logarithm of the Tadawul stock market price (LSCS). MODWT-LA8 (ARIMA(1,1,0) with drift) outperforms other WT functions on the 80% dataset, with an ME of (0.00000532), MAE of (0.003214182), and MAPE of (0.06449683). The addition of WT functions to the FS.HGD and HyFIS models increases their forecasting ability. Based on the reduced RMSE (0.048), MAE (0.038), and MAPE (0.538), the MODWT-LA8-FS.HGD outperforms traditional models in predicting the remaining 20% of datasets. Full article
(This article belongs to the Special Issue Time Series Modeling for Finance and Insurance)
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24 pages, 1780 KiB  
Article
Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data
by Andrea Frattini, Ilaria Bianchini, Alessio Garzonio and Lorenzo Mercuri
Risks 2022, 10(12), 225; https://doi.org/10.3390/risks10120225 - 25 Nov 2022
Cited by 6 | Viewed by 9463
Abstract
The aim of this paper is to introduce a two-step trading algorithm, named TI-SiSS. In the first step, using some technical analysis indicators and the two NLP-based metrics (namely Sentiment and Popularity) provided by FinScience and based on relevant news spread [...] Read more.
The aim of this paper is to introduce a two-step trading algorithm, named TI-SiSS. In the first step, using some technical analysis indicators and the two NLP-based metrics (namely Sentiment and Popularity) provided by FinScience and based on relevant news spread on social media, we construct a new index, named Trend Indicator. We exploit two well-known supervised machine learning methods for the newly introduced index: Extreme Gradient Boosting and Light Gradient Boosting Machine. The Trend Indicator, computed for each stock in our dataset, is able to distinguish three trend directions (upward/neutral/downward). Combining the Trend Indicator with other technical analysis indexes, we determine automated rules for buy/sell signals. We test our procedure on a dataset composed of 527 stocks belonging to American and European markets adequately discussed in the news. Full article
(This article belongs to the Special Issue Time Series Modeling for Finance and Insurance)
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