This simple question does not have a simple answer. The boundary of such an interdisciplinary area is always moot and any attempt to give a formal definition is unlikely to be successful. Broadly speaking, financial econometrics is to study quantitative problems arising from finance. It uses statistical techniques and economic theory to address a variety of problems from finance. These include building financial models, estimation and inferences of financial models, volatility estimation, risk management, testing financial economics theory, capital asset pricing, derivative pricing, portfolio allocation, risk-adjusted returns, simulating financial systems, hedging strategies, among others. Technological invention and trade globalization have brought us into a new era of financial markets. Over the last three decades, enormous number of new financial products have been created to meet customers demands. For example, to reduce the impact of the fluctuations of currency exchange rates on a firms finance, which makes its profit more predictable and competitive, a multinational corporation may decide to buy the options on the future of foreign exchanges; to reduce the risk of price fluctuations of a commodity (eg lumbers, corns, soybeans), a farmer may enter into the future contracts of the commodity; to reduce the risk of weather exposures, amuse parks (too hot or too cold reduces the number of visitors) and energy companies may decide to purchase the financial derivatives based on the temperature. An important milestone is that in the year 1973, the worlds first options exchange opened in Chicago. At the very same year, Black and Scholes (1973
Past, Present, and Future of Statistical Science was commissioned in 2013 by the Committee of Presidents of Statistical Societies (COPSS) to celebrate its 50th anniversary and the International Year of Statistics. COPSS consists of five charter member statistical societies in North America and is best known for sponsoring prestigious awards in stat
We present an evolution method for designing the styling curves of garments. The procedure of evolution is driven by
aesthetics-inspired scores to evaluate the quality of styling designs, where the aesthetic considerations are represented in the form of
streamlines on human bodies. A dual representation is introduced in our platform to process the styling curves of designs, based on
which robust methods for realizing the operations of evolution are developed. Starting from a given set of styling designs on human
bodies, we demonstrate the effectiveness of set evolution inspired by aesthetic factors. The evolution is adaptive to the change of
aesthetic inspirations. By this adaptation, our platform can automatically generate new designs fulfilling the demands of variations in
different human bodies and poses.
This is the <i>Leonardo da Vinci Lecture</i> given in Milan in March 2006. It is a survey on the concept of space-time over the last 3000years: it starts with Euclidean geometry, discusses the contributions of Gauss and Riemannian geometry, presents the dynamic concept of space-time in Einsteins general relativity, describes the importance of symmetries, and ends with Calabi-Yau manifolds and their importance in todays string theories in the attempt for a unified theory of physics.
The estimation of large covariance and precision matrices is fundamental in modern multivariate analysis. However, problems arise from the statistical analysis of large panel economic and financial data. The covariance matrix reveals marginal correlations between variables, while the precision matrix encodes conditional correlations between pairs of variables given the remaining variables. In this paper, we provide a selective review of several recent developments on the estimation of large covariance and precision matrices. We focus on two general approaches: a rankbased method and a factormodelbased method. Theories and applications of both approaches are presented. These methods are expected to be widely applicable to the analysis of economic and financial data.