As the banking crisis and its effects on the world economy have made plain, the stock market is of colossal importance to our livelihoods. In Framing Finance, Alex Preda looks at the history of the market to figure out how we arrived at a point where investing is not only commonplace, but critical, as market fluctuations threaten our plans to send our children to college or retire comfortably. As Preda discovers through extensive research, the public was once much more skeptical. For investing to become accepted, a deep-seated prejudice against speculation had to be overcome, and Preda reveals that over the course of the eighteenth and nineteenth centuries groups associated with stock exchanges in New York, London, and Paris managed to redefine finance as a scientific pursuit grounded in observational technology. But Preda also notes that as the financial data in which they trafficked became ever more difficult to understand, charismatic speculators emerged whose manipulations of the market undermined the benefits of widespread investment. And so, Framing Finance ends with an eye on the future, proposing a system of public financial education to counter the irrational elements that still animate the appeal of finance.

Portfolio theory and much of asset pricing, as well as many empirical applications, depend on the use of multivariate probability distributions to describe asset returns. Traditionally, this has meant the multivariate normal (or Gaussian) distribution. More recently, theoretical and empirical work in financial economics has employed the multivariate Student (and other) distributions which are members of the elliptically symmetric class. There is also a growing body of work which is based on skew-elliptical distributions. These probability models all exhibit the property that the marginal distributions differ only by location and scale parameters or are restrictive in other respects. Very often, such models are not supported by the empirical evidence that the marginal distributions of asset returns can differ markedly. Copula theory is a branch of statistics which provides powerful methods to overcome these shortcomings. This book provides a synthesis of the latest research in the area of copulae as applied to finance and related subjects such as insurance. Multivariate non-Gaussian dependence is a fact of life for many problems in financial econometrics. This book describes the state of the art in tools required to deal with these observed features of financial data. This book was originally published as a special issue of the European Journal of Finance.

Today?s traders want to know when volatility is a sign that the sky is falling (and they should stay out of the market), and when it is a sign of a possible trading opportunity. Inside Volatility Arbitrage can help them do this. Author and financial expert Alireza Javaheri uses the classic approach to evaluating volatility -- time series and financial econometrics -- in a way that he believes is superior to methods presently used by market participants. He also suggests that there may be "skewness" trading opportunities that can be used to trade the markets more profitably. Filled with in-depth insight and expert advice, Inside Volatility Arbitrage will help traders discover when "skewness" may present valuable trading opportunities as well as why it can be so profitable.