Competition in the banking sector has been analysed by, amongst other methods, measuring market
power (i.e. a reduction in competitive pressure) and efficiency. A well-known approach to measuring
market power is suggested by Bresnahan (1982) and Lau (1982), recently used by Bikker (2003) and
Uchida and Tsutsui (2005). They analyse bank behaviour on an aggregate level and estimate the
average conjectural variation of banks.
Basic principles underlying the transactions of financial markets are tied to
probability and statistics. Accordingly it is natural that books devoted to
mathematical finance are dominated by stochastic methods. Only in recent
years, spurred by the enormous economical success of financial derivatives,
a need for sophisticated computational technology has developed. For example,
to price an American put, quantitative analysts have asked for the
numerical solution of a free-boundary partial differential equation.
Before you start working your way through this book, you may ask
Why analyze data? This is an important, basic question, and it has
several compelling answers.
The simplest need for data analysis arises most naturally in disciplines addressing
phenomena that are, in all likelihood, inherently nondeterministic
(e.g., feelings and psychology or stock market behavior). Since such fields of
knowledge are not governed by known fundamental equations, the only way to
generalize disparate observations into expanded knowledge is to analyze those