Description
Book SynopsisThis rigorous textbook introduces graduate students to the principles of econometrics and statistics with a focus on methods and applications in financial research. Financial Econometrics, Mathematics, and Statistics introduces tools and methods important for both finance and accounting that assist with asset pricing, corporate finance, options and futures, and conducting financial accounting research.
Divided into four parts, the text begins with topics related to regression and financial econometrics. Subsequent sections describe time-series analyses; the role of binomial, multi-nomial, and log normal distributions in option pricing models; and the application of statistics analyses to risk management. The real-world applications and problems offer students a unique insight into such topics as heteroskedasticity, regression, simultaneous equation models, panel data analysis, time series analysis, and generalized method of moments.
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Trade Review
“The main readers of the book are seen as upper-undergraduate and graduate students in finance, economics, and statistics. But practitioners in financial analysis will find much use in this. The book contains many examples of statistical analysis of data, both hypothetical and real; computational implementation codes for various algorithms … . All these features and themes, as well as a rigorous explanation make this book an exclusive item in the sector of education and professional literature.” (Vladimir Gorbunov, zbMATH 1460.62001, 2021)
Table of ContentsIntroduction to Financial Econometrics and Statistics.- Part A: Regression and Financial Econometrics.- Multiple Linear Regression.- Other Topics in Applied Regression Analysis.-Simultaneous Equation Models.-Econometric Approach to Financial Analysis, Planning, and Forecasting.- Fixed Effect vs Random Effect in Finance Research.- Alternative Methods to Deal with Measurement Error.-Three Alternative Errors-in-Variables Estimation Methods in Testing Capital Asset Pricing Model.- Spurious Regression and Data Mining in Conditional Asset Pricing Models.-Time-Series Analysis and Its Applications.-Time-Series: Analysis, Model, and Forecasting.-Hedge Ratio and Time-Series Analysis.- The Binomial, Multi-Nominal Distributions and Option Pricing Model.- Two Alternative Binomial Option Pricing Model Approaches to Derive Black-Scholes Option Pricing Model.-Normal, Lognormal Distribution, and Option Pricing Model.-Copula, Correlated Defaults, and Credit VaR.-Multivariate Analysis: Discriminant Analysis and Factor Analysis.-Stochastic Volatility Option Pricing Models.- Alternative Method to Estimate Implied Variance: Review and Comparison.- Numerical Valuation of Asian Options with Higher Moments in the Underlying Distribution.-Itô’s Calculus: Derivation of the Black-Scholes Option Pricing Model.-Alternative Methods to Derive Option Pricing Models.-Constant Elasticity of Variance Option Pricing Model: Integration and Detailed Derivation.- Option Pricing and Hedging Performance under Stochastic Volatility and Stochastic Interest Rates.-Non-Parametric Method for European Option Bounds.
