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E-raamat: Indexation and Causation of Financial Markets

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?This book presents a new statistical method of constructing a price index of a financial asset where the price distributions are skewed and heavy-tailed and investigates the effectiveness of the method. In order to fully reflect the movements of prices or returns on a financial asset, the index should reflect their distributions. However, they are often heavy-tailed and possibly skewed, and identifying them directly is not easy. This book first develops an index construction method depending on the price distributions, by using nonstationary time series analysis. Firstly, the long-term trend of the distributions of the optimal Box–Cox transformed prices is estimated by fitting a trend model with time-varying observation noises. By applying state space modeling, the estimation is performed and missing observations are automatically interpolated. Finally, the index is defined by taking the inverse Box–Cox transformation of the optimal long-term trend. This book applies the method to various financial data. For example, applying it to the sovereign credit default swap market where the number of observations varies over time due to the immaturity, the spillover effects of the financial crisis are detected by using the power contribution analysis measuring the information flows between indices. The investigations show that applying this method to the markets with insufficient information such as fast-growing or immature markets can be effective.

Arvustused

The book develops a new practical method for constructing an index of prices of a financial asset for which the distributions are skewed and heavy-tailed. The book is valuable and concise reading for professionals in the area of finance and financial econometrics. (Pavel Stoynov, zbMATH 1338.91009, 2016)

1 Introduction
1(12)
1.1 Indexation of Financial Markets
1(2)
1.2 Causation of Financial Markets
3(2)
1.3 Nonstationarity of Financial Time Series
5(3)
1.4 State-Space Modeling
8(2)
1.5 Organization of the Book and Related Web Information
10(3)
References
10(3)
2 Method for Constructing a Distribution-Free Index
13(22)
2.1 Nonstationary Time Series Modeling
13(14)
2.1.1 Trend Estimation
13(3)
2.1.2 Time-Varying Variance Modeling
16(3)
2.1.3 Seasonal Adjustment Modeling
19(3)
2.1.4 Non-Gaussian Distribution Modeling
22(5)
2.2 Transformation of Non-Gaussian Distributed Prices of a Financial Market
27(3)
2.3 Construction of a Distribution-Free Index
30(5)
References
33(2)
3 Power Contribution Analysis of a Multivariate Feedback System
35(14)
3.1 Akaike's Power Contribution and Its Generalization
35(5)
3.2 Algorithm for Decomposing a Variance Covariance Matrix
40(3)
3.3 Example of Power Contribution Analysis
43(6)
References
46(3)
4 Application to Financial and Economic Time Series Data
49(52)
4.1 Detecting Crisis Spillovers in Terms of Sovereign CDS Distribution-Free Indices
49(15)
4.1.1 SCDS Regional Distribution-Free Index Construction
50(6)
4.1.2 Role of the SCDS Distribution-Free Index
56(3)
4.1.3 Causation Between SCDS Regional Distribution-Free Indices
59(5)
4.2 Measuring the Impact of the US Subprime Crisis on Japanese Financial Markets
64(14)
4.2.1 Japanese Corporate CDS Market and Rating Classes
65(2)
4.2.2 Japanese CDS Rating-Based Distribution-Free Index Construction
67(5)
4.2.3 Causation Between Japanese Financial Markets
72(6)
4.3 Other Applications: Usability of the Distribution-Free Index
78(23)
4.3.1 Constructing a GDP Growth Regional Distribution-Free Index
79(11)
4.3.2 Constructing a Japanese SCDS Distribution-Free Index Using SCDS Curves
90(9)
References
99(2)
Index 101