An Application of the IFM Method for the Risk Assessment of Financial Instruments

External influences or behavioral biases can affect the way risk is perceived. This paper studies the prediction of VaR (Value at Risk) as a measure of the risk of loss for investments on financial products. Our aim is to predict the percentage of loss that a financial product would have in the futu...

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Bibliographic Details
Published in:Computational economics 2023, Vol.61 (1), p.295-315
Main Authors: Pons, Adrià, Cristobal-Fransi, Eduard, Vintrò, Carla, Rius, Josep, Querol, Oriol, Vilaplana, Jordi
Format: Article
Language:English
Subjects:
Automation
Autoregressive models
Banks
Banks (Finance)
Behavior
Behavioral economics
Behavioral/Experimental Economics
Computer Appl. in Social and Behavioral Sciences
Data mining
Decision making
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Financial institutions
Financial instruments
Investment analysis
Investments
Math Applications in Computer Science
Methods
Monte Carlo simulation
Operations Research/Decision Theory
Optimization
Portfolio management
Rationality
Risk assessment
Risk perception
Securities markets
Sentiment analysis
Stock markets
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Summary:External influences or behavioral biases can affect the way risk is perceived. This paper studies the prediction of VaR (Value at Risk) as a measure of the risk of loss for investments on financial products. Our aim is to predict the percentage of loss that a financial product would have in the future to assess the risks and determine the potential loss of a security in the stock market, thus reducing reasoning influenced by feelings for bank and financial firms seeking to deploy AI and advanced automation. We used the IFM (inference function for margins) method in different market scenarios, with particular emphasis on the strengths and weaknesses of it. The study is assessed on single product level with the skewed studen-t GARCH(1,1) model and portfolio level with t-copulas for the inter-dependencies. It has been shown that under normal market conditions the risk is predicted properly for both levels. However, when an unexpected market event occurs, the prediction fails. To address this limitation, a combined model with sentiment analysis and regression is proposed for further investigation as a future work.
ISSN:0927-7099
1572-9974
DOI:10.1007/s10614-021-10208-4