Projective completions of Jordan pairs Part II. Manifold structures and symmetric spaces

We define symmetric spaces in arbitrary dimension and over arbitrary non-discrete topological fields \K, and we construct manifolds and symmetric spaces associated to topological continuous quasi-inverse Jordan pairs and -triple systems. This class of spaces, called smooth generalized projective geometries, generalizes the well-known (finite or infinite-dimensional) bounded symmetric domains as well as their ``compact-like'' duals. An interpretation of such geometries as models of Quantum Mechanics is proposed, and particular attention is paid to geometries that might be considered as "standard models" -- they are associated to associative continuous inverse algebras and to Jordan algebras of hermitian elements in such an algebra

Projective completions of Jordan pairs. Part I: The generalized projective geometry of a Lie algebra

A geometric realization of the projective completion of the Jordan pair corresponding to a three-graded Lie algebra is given which permits to develop a geometric structure theory of the projective completion. This will be used in Part II of this work to define a manifold structure on the projective completion (in arbitrary dimension and over quite general base fields and -rings).Comment: 33 pages, plain te

The dynamics of hourly electricity prices

The dynamics of hourly electricity prices in day-ahead markets is an important element of competitive power markets that were only established in the last decade. In electricity markets, the market microstructure does not allow for continuous trading, since operators require advance notice in order to verify that the schedule is feasible and lies within transmission constraints. Instead agents have to submit their bids and offers for delivery of electricity for all hours of the next day before a specified market closing time. We suggest the use of dynamic semiparametric factor models (DSFM) for the behavior of hourly electricity prices. We find that a model with three factors is able to explain already a high proportion of the variation in hourly electricity prices. Our analysis also provides insights into the characteristics of the market, in particular with respect to the driving factors of hourly prices and their dynamic behavior through time.Power Markets, Dynamic Semiparametric Factor Models, Day-ahead Electricity Prices

Dynamic Activity Analysis Model Based Win-Win Development Forecasting Under the Environmental Regulation in China

Porter Hypothesis states that environmental regulation may lead to win-win opportunities, that is, improve the productivity and reduce the undesirable output simultaneously. Based on directional distance function, this paper proposes a novel dynamic activity analysis model to forecast the possibilities of win-win development in Chinese Industry between 2009 and 2049. The evidence reveals that the appropriate energy-saving and emission-abating regulation will result in both the improvement in net growth of potential output and the steadily increasing growth of total factor productivity. This favors Porter Hypothesis.Dynamic Activity Analysis Model, Energy-Saving and Emission-Abating, Environmental Regulation, Win-Win Development

Adaptive Interest Rate Modelling

A good description of the dynamics of interest rates is crucial to price derivatives and to hedge corresponding risk. Interest rate modelling in an unstable macroeconomic context motivates one factor models with time varying parameters. In this paper, the local parameter approach is introduced to adaptively estimate interest rate models. This method can be generally used in time varying coefficient parametric models. It is used not only to detect the jumps and structural breaks, but also to choose the largest time homogeneous interval for each time point, such that in this interval, the coeffcients are statistically constant. We use this adaptive approach and apply it in simulations and real data. Using the three month treasure bill rate as a proxy of the short rate, we nd that our method can detect both structural changes and stable intervals for homogeneous modelling of the interest rate process. In more unstable macroeconomy periods, the time homogeneous interval can not last long. Furthermore, our approach performs well in long horizon forecasting.CIR model, Interest rate, Local parametric approach, Time homogeneous interval, Adaptive statistical techniques

Spatial Risk Premium on Weather Derivatives and Hedging Weather Exposure in Electricity

Due to dependency of energy demand on temperature, weather derivatives enable the effective hedging of temperature related fluctuations. However, temperature varies in space and time and therefore the contingent weather derivatives also vary. The spatial derivative price distribution involves a risk premium. We examine functional principal components of temperature variation for this spatial risk premium. We employ a pricing model for temperature derivatives based on dynamics modelled via a vectorial Ornstein-Uhlenbeck process with seasonal variation. We use an analytical expression for the risk premia depending on variation curves of temperature in the measurement period. The dependence is exploited by a functional principal component analysis of the curves. We compute risk premia on cumulative average temperature futures for locations traded on CME and fit to it a geographically weighted regression on functional principal component scores. It allows us to predict risk premia for nontraded locations and to adopt, on this basis, a hedging strategy, which we illustrate in the example of Leipzig.risk premium, weather derivatives, Ornstein-Uhlenbeck process, functional principal components, geographically weighted regression

Differential Calculus, Manifolds and Lie Groups over Arbitrary Infinite Fields

We present an axiomatic approach to finite- and infinite-dimensional differential calculus over arbitrary infinite fields (and, more generally, suitable rings). The corresponding basic theory of manifolds and Lie groups is developed. Special attention is paid to the case of mappings between topological vector spaces over non-discrete topological fields, in particular ultrametric fields or the fields of real and complex numbers. In the latter case, a theory of differentiable mappings between general, not necessarily locally convex spaces is obtained, which in the locally convex case is equivalent to Keller's C^k_c-theory.Comment: 70 page

Local Quantile Regression

Quantile regression is a technique to estimate conditional quantile curves. It provides a comprehensive picture of a response contingent on explanatory variables. In a flexible modeling framework, a specific form of the conditional quantile curve is not a priori fixed. % Indeed, the majority of applications do not per se require specific functional forms. This motivates a local parametric rather than a global fixed model fitting approach. A nonparametric smoothing estimator of the conditional quantile curve requires to balance between local curvature and stochastic variability. In this paper, we suggest a local model selection technique that provides an adaptive estimator of the conditional quantile regression curve at each design point. Theoretical results claim that the proposed adaptive procedure performs as good as an oracle which would minimize the local estimation risk for the problem at hand. We illustrate the performance of the procedure by an extensive simulation study and consider a couple of applications: to tail dependence analysis for the Hong Kong stock market and to analysis of the distributions of the risk factors of temperature dynamics