Navigating Economic Volatility: Predictive Models and Risk Analysis of Business Cycles
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: 31 December 2024 | Viewed by 3963
Special Issue Editor
Interests: risk management and insurance; mathematical optimization; business cycle prediction; tail risk analysis; moment problems; portfolio management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Understanding and predicting business cycles are crucial for risk assessment and financial management for both public and private sectors. As the world is facing more post-pandemic uncertainties, including inflation, government deficits, stagnant housing markets, and more catastrophic events driven by global warming, the prediction of business cycles and the assessment of associated risks have become more indispensable. This Special Issue aims to collate research that advances our understanding of business cycle dynamics, predictive models, and the corresponding risks that affect various sectors (including finance, risk management and insurance, investment, etc.).
Original research or practitioner papers may explore the following topics:
- Advanced models for business cycles forecasting;
- Quantitative and qualitative risk assessments at different phases of business cycles;
- The role of fiscal and monetary policy in mitigating risks caused by business cycles;
- The impact of business cycles on risk management strategies;
- The interaction between international economic activities and domestic business cycles.
We hope submissions in the Special Issue advance theoretical modelling, present empirical evidence, and provide constructive insights to professionals and policymakers.
Dr. Ruilin Tian
Guest Editor
Manuscript Submission Information
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Keywords
- business cycle forecasting
- risk assessment
- economic volatility
- financial management
- predictive modelling
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