Efficient computational methods for quantitative financial risk management
| Project/Area Number |
24510200
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Keio University |
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Principal Investigator |
IMAI Junichi 慶應義塾大学, 理工学部, 教授 (10293078)
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2014: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2013: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2012: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | ファイナンス / シミュレーション |
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| Outline of Final Research Achievements |
The goal of this research was to develop a new series of computational methods that can help management handle financial risk more efficiently. Most existing numerical methods used in current financial institutions are based on a classical assumption that returns of financial assets, interest rates, and exchange rates are under normal distribution. However, many empirical studies indicate that historical financial data do not support the assumption and we need more accurate models. Motivated by these findings, we first investigated the effects of new models on practical risk management in terms of valuation, hedging, and risk measure computation. Then, we have proposed a couple of simulation methods that can generate samples from any class of distribution. We have also proposed an enhanced quasi-Monte Carlo method that can evaluate financial instruments as well as financial risk measures more accurately and more efficiently.
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Report
(4 results)
Research Products
(25 results)
