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Statistical Models in Finance: Applications to Price Change and Credit Risk
https://ir.soken.ac.jp/records/757
https://ir.soken.ac.jp/records/7573e1cead1928f4cb6b8202757e27d9e38
名前 / ファイル  ライセンス  アクション 

要旨・審査要旨 / Abstract, Screening Result (243.7 kB)

Item type  学位論文 / Thesis or Dissertation(1)  

公開日  20100222  
タイトル  
タイトル  Statistical Models in Finance: Applications to Price Change and Credit Risk  
タイトル  
タイトル  Statistical Models in Finance: Applications to Price Change and Credit Risk  
言語  en  
言語  
言語  eng  
資源タイプ  
資源タイプ識別子  http://purl.org/coar/resource_type/c_46ec  
資源タイプ  thesis  
著者名 
高橋, 久尚
× 高橋, 久尚 

フリガナ 
タカハシ, ヒサナオ
× タカハシ, ヒサナオ 

著者 
TAKAHASHI, Hisanao
× TAKAHASHI, Hisanao 

学位授与機関  
学位授与機関名  総合研究大学院大学  
学位名  
学位名  博士（学術）  
学位記番号  
内容記述タイプ  Other  
内容記述  総研大甲第647号  
研究科  
値  数物科学研究科  
専攻  
値  15 統計科学専攻  
学位授与年月日  
学位授与年月日  20030324  
学位授与年度  
値  2002  
要旨  
内容記述タイプ  Other  
内容記述  The aim of this study is to make simple statistical models to analyze the risks in finance. Comparing with real data of the exchange rate between the US dollar and the Japanese yen, we make three simple models to explain the distribution of price change and the interaction of traders.<br /> In the first model, we consider change (difference, returns) in stock index prices and exchange rates for currencies. These are said, from empirical studies, to be distributed by a stable law with a characteristic exponent α< 2 for short sampling intervals and by a Gaussian distribution for long sampling intervals. To explain this phenomenon, we introduce an Ehrenfest model with large jumps (ELJ), which explains the empirical density function of price changes for short time intervals as well as for long time intervals. In chapter 3, we discuss mathematical details and related problems of ELJ.<br /> The second model is a majority orienting model which we introduce to show the majority orienting behavior of the traders in a market. It seems that the interaction among the traders must exist not only at the time of crashes and bubbles but also at the time of usual trading. And the interaction makes the time series of the market prices such as an exchange rate between the US dollar and the Japanese yen a typical trajectory.<br /> The third model is the majority orienting model with feed back process which we introduce to understand the oscillation of the market price. We study a simplified market in which the dealers' behavior changes by the influence of the price. We show that in such a market, the price oscillates perpetually by applying the van del Pol equation which is obtained from a deterministic approximation of our model.<br /> The advantage of these models is it is easy to understand the connection with real market such that there are N agents, each of whom is in one of two possible microeconomic states. To explain volatihty clustering, trend of market and nonsymmetrical trading is left for our further study. To combine the above models may be also an interesting next problem.<br /> We also study a default probability of companies by applying the Iogit model to the data from the database as a starting point of making stochastic model on the default of a company. It is important in credit risk management to determine the probability of bankruptcy. Few reliable analyses of bankruptcy have been developed for small and mediumsized enterprises because of the delay in developing of databases to capture credit risks for these enterprises. Recently, a largescale database for estimating credit risks for such enterprises has become available as "Credit Risk Database". We use the Wald statistic to evaluate the significance of the model's parameters. We discuss the differences in explanatory factors of credit risk depending on the enterprise scale. In general, financial data for small and mediumsized companies contain many missing data, and many statistical difficulties are caused by it. To avoid these difficulties, 01 dummy variables were incorporated into the Iogit model. This method can be also interpreted that the condition of a certain company's missing data in its financial indices is valuable for predicting a company's default. To make a simple stochastic model for this problem is our next problem.  
所蔵  
値  有 