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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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Dependent conditional value-at-risk for aggregate risk models
Bony Parulian Josaphat1, Khreshna Syuhada1
1Statistics Research Division, Institut Teknologi Bandung 40132, Indonesia.
Heliyon
|August 17, 2021
Summary
We introduce Dependent CoVaR (DCoVaR), a new coherent risk measure that improves forecast accuracy over existing methods. DCoVaR aids optimal investment positioning and enhances financial risk protection for businesses.
Area of Science:
- Quantitative Finance
- Risk Management
- Econometrics
Background:
- Value-at-Risk (VaR) is widely used but lacks coherence.
- Conditional VaR (CoVaR) and its extensions (MCoVaR, CCoVaR) address coherence but have limitations.
Purpose of the Study:
- Propose a novel risk measure, Dependent CoVaR (DCoVaR), for dependent random losses.
- Evaluate DCoVaR's forecasting performance against MCoVaR and CCoVaR.
- Demonstrate DCoVaR's applicability in financial and actuarial contexts.
Main Methods:
- Developed the Dependent CoVaR (DCoVaR) risk measure.
- Conducted numerical simulations to illustrate DCoVaR.
- Empirical analysis using GARCH(1,1) models on financial returns data.
- Utilized Gumbel and Clayton Copulas to model dependence structures.
Main Results:
- DCoVaR demonstrated superior forecast accuracy compared to MCoVaR and CCoVaR.
- Gumbel Copula provided a better fit for the dependence structure of financial returns than Clayton Copula.
- DCoVaR effectively captures bivariate loss dependencies, aiding investment and risk management.
Conclusions:
- DCoVaR offers enhanced risk forecasting and management capabilities.
- It enables optimized capital allocation and improved financial risk protection.
- DCoVaR has practical applications in insurance for premium determination and risk reduction.
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