Dynamic Structural Change of Financial Intermediation and Restructuring of Business Models in Japanese Banking
| Project/Area Number |
15530227
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Public finance/Monetary economics
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| Research Institution | Toyo University |
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Principal Investigator |
MUNECHIKA Midori Toyo University, Faculty of Economics, Professor, 経済学部, 教授 (10209992)
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2005: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2004: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2003: ¥800,000 (Direct Cost: ¥800,000)
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| Keywords | Mean-Variance Optimization / Stylized facts / Monte Carlo Simulation / Anomaly / Correlation / Covariance matrix / Value at Risk / Stochastic finance / 共分散行列 / バリュー・アット・リスク / Stylized facts / ダウンサイド・リスク / 資産価格の過剰変動性 / time deformation / 分布混合仮説 / Stochastic finance / CAPM / アノマリー / Single-Index Mode / 平均・分散モデル |
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| Research Abstract |
In this study, my overall research objective was to analyze the relationship between risk-reward optimization and risk-taking decisions in Japanese banking. Since the emergence of modern portfolio theory originated with Markowitz, risk analysis in quantitative finance has extensively addressed market risk in the capital markets. In the context of integrated risk management, the extension to the various risks in banking has advanced in the United States and Europe, but it still remains a challenge in the Japanese banking industry. However, in order to enhance the risk-return profile, it is necessary for Japanese banks to implement risk-based policies and practices, and thus, to adequately model and measure banking risk utilizing a quantitative approach.
I concentrated on the issue of modeling and measuring market risk. The term ‘risk' is used in finance in two different but related ways : as the magnitude of the standard deviation of the potential return of investment portfolio, or the p
… More
otential loss over some period of time. Risk models in quantitative finance analyze risk using stochastic approaches.
First, I studied mean-variance analysis, in which portfolio risk is statistically calculated by using a covariance matrix, in which volatilities and correlations are the two key determinants of risk. Theoretically, mean-variance analysis postulates normally distributed stock returns and rational behavior of investors. In fact, histograms of stock returns, however, exhibit excess peakness and fat-tailed distributions rather than normal distributions, which are referred to as stylized facts. These features need to be accounted for in the process of implementing optimization.
In the process of implementing stochastic mean-variance optimization, I used Monte Carlo simulation as an optimizer. By examining forecasting results of Monte Carlo simulation, I extended to the second view of risk : the magnitude of the potential loss, in which risk is evaluated in the tail of the distribution, such as the concept of value at risk. Now the VaR framework is used as the methodology of risk measurement of the Basel Accord in the context of integrated risk management in banking. Less
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Report
(4 results)
Research Products
(9 results)
