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.