Quantitative Methods for Portfolio Analysis: MTV Model Approach
This text aims to provide practical models and methods for the quantitative analysis of financial asset prices, construction of various portfolios, and computer-assisted trading systems. In particular, it should be helpful reading for Quants (quantitatively-inclined analysts) in financial industries, financial engineers in investment banks; securities companies, derivative-trading companies, and software houses who are developing portfolio trading systems; graduate students and specialists in the areas of finance, business, hardbound economics, statistics, financial engineering; investors who are interested in Japanese financial markets. Throughout the book the emphasis is placed on the originality and usefulness of models and methods for the construction of portfolios and investment decision making, and examples are provided to demonstrate, analysis, models for Japanese financial markets.
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Selection of Portfolio Population 177
Empirical Features of Financial Returns
Univariate Financial Time Series Models
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analysis approach arbitrage pricing theory ARCH model asset allocation asset prices assumed assumption autocorrelations autocovariance autoregressive bond CAPM Chapter coefficient conditional variance correlation covariance matrix cross-sectional discount function distribution efficient market hypothesis error term estimated evaluate exchange rates fact finance theory financial assets financial time series fjt's follows forecasted future values geometric Brownian motion given Hence heteroscedastic Hitachi hypothesis iid normal implies investment stance investors Kalman filter kurtosis linear market model method MTV model multifactor model multivariate time series Nikkei nonlinear observed obtained optimal portfolio option parameters portfolio population portfolio return prediction problem procedure quants random variables return process returns and risks sample period Section selection series model series structure specific stationary process statistical stochastic process stock prices Theorem tion uncorrelated univariate variance components variational features vector white noise Yamaichi Securities
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