Value At Risk Research Paper

Value At Risk Research Paper-48
Financial institutions have for many years sought measures which cogently summarise the diverse market risks in portfolios of financial instruments.This quest led institutions to develop Value-at-Risk (Va R) models for their trading portfolios in the 1990s.

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Including mitigation costs, the present value of global financial assets is an expected 0.2% higher when warming is limited to no more than 2 °C, compared with business as usual. Limiting warming to no more than 2 °C makes financial sense to risk-neutral investors—and even more so to the risk averse.

However, for the sake of transparency, the authors would like to make clear that they were employed by Vivid Economics Ltd during the production of this research.

These estimates would constitute a substantial write-down in the fundamental value of financial assets.

Cutting emissions to limit warming to no more than 2 °C reduces the climate Va R by an expected 0.6 percentage points, and the 99th percentile reduction is 7.7 percentage points. However, it is no less important to ask, what might be the impact of climate change itself on asset values? Here we show how a leading integrated assessment model can be used to estimate the impact of twenty-first-century climate change on the present market value of global financial assets. The procyclicality of filtered historical simulation models is also discussed and compared to that of unfiltered Va R.A key consideration in the design of risk management models is whether the model’s purpose is simply to estimate some percentile of the return distribution, or whether its aims are broader.This paper is based on the research project conducted by Nicklas Larsen during his visit to the University of Florida in Spring 2000.This research project has not been conducted in the framework of any legal agreement between the University of Florida and Algorithmic, Inc.For the credit risk problem studied in this paper, Va R minimization leads to about 16% increase of the average loss for the worst 1% scenarios (compared to the worst 1% scenarios in CVa R minimum solution).1% includes 200 of 20000 scenarios, which were used for estimating credit risk in this case study.This paper explores the properties of various filtered historical simulation models.We explain how these models are constructed and illustrate their performance, examining in particular how filtering transforms various properties of return distribution.


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