This book is an introduction to financial valuation and financial data analyses using econometric methods. It is intended for advanced finance undergraduates and graduates. Most chapters in the book would contain one or more finance application examples where finance concepts, and sometimes theory, are taught.
This book is a modest attempt to bring together several important domains in financial valuation theory, in econometrics modelling, and in the empirical analyses of financial data. These domains are highly intertwined and should be properly understood in order to correctly and effectively harness the power of data and statistical or econometrics methods for investment and financial decision-making.
The contribution in this book, and at the same time, its novelty, is in employing materials in basic econometrics, particularly linear regression analyses, and weaving into it threads of foundational finance theory, concepts, ideas, and models. It provides a clear pedagogical approach to allow very effective learning by a finance student who wants to be well equipped in both theory and ability to research the data.
This is a handy book for finance professionals doing research to easily access the key techniques in data analyses using regression methods. Students learn all 3 skills at once — finance, econometrics, and data analyses. It provides for very solid and useful learning for advanced undergraduate and graduate students who wish to work in financial analyses, risk analyses, and financial research areas.
Probability Distribution and Statistics
Statistical Laws and Central Limit Theorem / Application: Stock Return Distributions
Two-Variable Linear Regression/Application: Financial Hedging
Model Estimation / Application: Capital Asset Pricing Model
Constrained Regression / Application: Cost of Capital
Time Series Analysis / Application: Inflation Forecasting
Random Walk / Application: Market Efficiency
Autoregression and Persistence / Application: Predictability
Estimation Errors and T-Tests / Application: Event Studies
Multiple Linear Regression and Stochastic Regressors
Dummy Variables and ANOVA / Application: Time Effect Anomalies
Cross-Sectional Regression / Application: Testing CAPM
More Multiple Linear Regressions / Application: Multi-Factor Asset Pricing
Errors-in-Variable / Application: Exchange Rates and Risk Premium
Unit Root Processes / Application: Purchasing Power Parity
Conditional Heteroskedasticity / Application: Risk Estimation
Maximum Likelihood and Goodness of Fit / Application: Choice of Copulas
Mean Reverting Continuous Time Process / Application: Bonds and Term Structures
Implied Parameters / Application: Option Pricing
Generalised Method of Moments / Application: Consumption-Based Asset Pricing
Multiple Time Series Regression / Application: Term Structure of Volatilities
Fixed and Random Effects Model / Application: Synchronicity of Stock Returns
LOGIT and PROBIT Regressions / Application: Categorization and Prediction
Readership: Advanced undergraduates and 1st year post-graduate students in finance and econometrics.
The book is a handy one for finance professionals doing research to easily access the key techniques in data analyses using regression methods
Students learn all 3 skills at once — finance, econometrics, and data analyses
It provides for very solid and useful learning for advanced undergraduate and graduate students who wish to work in financial analyses, risk analyses, and financial research areas