The Elements of Financial Econometrics
Financial econometrics is an interdisciplinary subject that uses statistical methods and economic theory to address a variety of quantitative problems in finance. This compact, master's-level textbook focuses on methodology and includes real financial data illustrations throughout. The mathematical level is purposely kept moderate, allowing the power of the quantitative methods to be understood without too much technical detail. Wherever possible, the authors indicate where to find the relevant R codes to implement the various methods. This book grew out of a course at Princeton University which is one of the world's flagship programs in computational finance and financial engineering. It will therefore be useful for those with an economics and finance background who are looking to sharpen their quantitative skills, and also for those with strong quantitative skills who want to learn how to apply them to finance.
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Linear Time Series Models
Heteroscedastic Volatility Models
Multivariate Time Series Analysis
Efficient Portfolios and Capital Asset Pricing Model
Factor Pricing Models
Portfolio Allocation and Risk Assessment
ˆΣ ACF plot allocation vector approximately AR(p ARMA models asset pricing assumption asymptotic autocorrelation CAPM coefficients cointegration compute conditional constraint correlation daily returns defined denote density Dickey–Fuller dividend EACF efficient market hypothesis equation example excess return expected return factor model Fama–French Figure follows forecast FTSE GARCH model given Goldman Sachs Granger causality gross exposure Hence January likelihood function linear log returns MACD market portfolio method monthly normal distribution null hypothesis obtain optimal portfolio P-value PACF panel parameters period prediction pricing model principal component R(wopt random walk regression residuals returns of S&P risk RiskMetrics risky assets sample ACF sample covariance matrix Section series plot Sharpe ratio squared standard errors stationary stationary process stochastic test statistic Theorem trend variables variance volatility matrix Wald test white noise yields