Dynamic Structural Change of Financial Intermediation and Restructuring of Business Models in Japanese Banking

在这项研究中，我的总体研究目标是分析日本银行业风险回报优化与风险承担决策之间的关系。自马科维茨创立现代投资组合理论以来，量化金融中的风险分析广泛地研究了资本市场中的市场风险。在综合风险管理的背景下，美国和欧洲已经推进了对银行业各种风险的扩展，但这在日本银行业仍然是一个挑战。然而，为了提高风险收益状况，日本银行有必要实施以风险为基础的政策和做法，从而利用量化方法对银行风险进行适当的建模和衡量。在金融领域，“风险”一词有两种不同但又相互关联的用法：投资组合潜在收益的标准差的大小，或p…在一段时间内更多的潜在损失。定量金融中的风险模型使用随机方法来分析风险。首先，我研究了均值-方差分析，其中投资组合风险是通过使用协方差矩阵来统计计算的，其中波动性和相关性是风险的两个关键决定因素。理论上，均值-方差分析假设股票收益为正态分布，投资者行为理性。然而，事实上，股票收益的直方图表现出超额峰值和厚尾分布，而不是被称为风格化事实的正态分布。在实现随机均值-方差优化的过程中，我使用了蒙特卡罗模拟作为优化器。通过检验蒙特卡罗模拟的预测结果，我将风险的第二个观点扩展到风险：潜在损失的大小，其中风险是在分布的尾部评估的，如风险价值的概念。现在，在银行业综合风险管理的背景下，VaR框架被用作巴塞尔协议的风险度量方法。较少

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