Undergraduate Introduction to Financial Mathematics or An Introduction to Modern Bayesian Econometrics

Undergraduate Introduction to Financial Mathematics

Author: J Robert Buchanan

This textbook provides an introduction to financial mathematics and financial engineering for undergraduate students who have completed a three or four semester sequence of calculus courses. It introduces the theory of interest, random variables and probability, stochastic processes, arbitrage, option pricing, hedging, and portfolio optimization. The student progresses from knowing only elementary calculus to understanding the derivation and solution of the Black-Scholes partial differential equation and its solutions. This is one of the few books on the subject of financial mathematics which is accessible to undergraduates having only a thorough grounding in elementary calculus. It explains the subject matter without "hand waving" arguments and includes numerous examples. Every chapter concludes with a set of exercises which test the chapter's concepts and fill in details of derivations.

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An Introduction to Modern Bayesian Econometrics

Author: Tony Lancaster

In this new and expanding area, Tony Lancaster’s text is the first comprehensive introduction to the Bayesian way of doing applied economics.

• Uses clear explanations and practical illustrations and problems to present innovative, computer-intensive ways for applied economists to use the Bayesian method;

• Emphasizes computation and the study of probability distributions by computer sampling;

• Covers all the standard econometric models, including linear and non-linear regression using cross-sectional, time series, and panel data;

• Details causal inference and inference about structural econometric models;

• Includes numerical and graphical examples in each chapter, demonstrating their solutions using the S programming language and Bugs software

• Supported by online supplements, including Data Sets and Solutions to Problems, at blackwellpublishing.com/lancaster

Table of Contents:

	Preface
	Introduction
1	The Bayesian algorithm	1
2	Prediction and model criticism	70
3	Linear regression models	112
4	Bayesian calculations	183
5	Non-linear regression models	227
6	Randomized, controlled and observational data	265
7	Models for panel data	277
8	Instrumental variables	311
9	Some time series models	342
App. 1	A conversion manual	259
App. 2	Programming	365
App. 3	BUGS code	375
	References	383
	Index	391