389,715 research outputs found
A Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics
A primary goal in modelling the implied volatility surface (IVS) for pricing and hedging aims at reducing complexity. For this purpose one fits the IVS each day and applies a principal component analysis using a functional norm. This approach, however, neglects the degenerated string structure of the implied volatility data and may result in a modelling bias. We propose a dynamic semiparametric factor model (DSFM), which approximates the IVS in a finite dimensional function space. The key feature is that we only fit in the local neighborhood of the design points. Our approach is a combination of methods from functional principal component analysis and backfitting techniques for additive models. The model is found to have an approximate 10% better performance than a sticky moneyness model. Finally, based on the DSFM, we devise a generalized vega-hedging strategy for exotic options that are priced in the local volatility framework. The generalized vega-hedging extends the usual approaches employed in the local volatility framework.Smile, local volatility, generalized additive model, backfitting, functional principal component analysis
Spatial pattern formation induced by Gaussian white noise
The ability of Gaussian noise to induce ordered states in dynamical systems
is here presented in an overview of the main stochastic mechanisms able to
generate spatial patterns. These mechanisms involve: (i) a deterministic local
dynamics term, accounting for the local rate of variation of the field
variable, (ii) a noise component (additive or multiplicative) accounting for
the unavoidable environmental disturbances, and (iii) a linear spatial coupling
component, which provides spatial coherence and takes into account diffusion
mechanisms. We investigate these dynamics using analytical tools, such as
mean-field theory, linear stability analysis and structure function analysis,
and use numerical simulations to confirm these analytical results.Comment: 11 pages, 8 figure
Weighted Majorization Algorithms for Weighted Least Squares Decomposition Models
For many least-squares decomposition models efficient algorithms are well known. A more difficult problem arises in decomposition models where each residual is weighted by a nonnegative value. A special case is principal components analysis with missing data. Kiers (1997) discusses an algorithm for minimizing weighteddecomposition models by iterative majorization. In this paper, we for computing a solution. We will show that the algorithm by Kiers is a special case of our algorithm. Here, we will apply weighted majorization to weighted principal components analysis, robust Procrustes analysis, and logistic bi-additive models of which the two parameter logistic model in item response theory is a specialcase. Simulation studies show that weighted majorization is generally faster than the method by Kiers by a factor one to four and obtains the same or better quality solutions. For logistic bi-additive models, we propose a new iterative majorization algorithm called logistic majorization.iterative majorization;IRT;logistic bi-additive model;robust Procrustes analysis;weighted principal component analysis;two parameter logistic model
A Constrained EM Algorithm for Independent Component Analysis
We introduce a novel way of performing independent component analysis using a constrained version of the expectation-maximization (EM) algorithm. The source distributions are modeled as D one-dimensional mixtures of gaussians. The observed data are modeled as linear mixtures of the sources with additive, isotropic noise. This generative model is fit to the data using constrained EM. The simpler âsoft-switchingâ approach is introduced, which uses only one parameter to decide on the sub- or supergaussian nature of the sources. We explain how our approach relates to independent factor analysis
The Use of Qualitative Business TendencySurveys for Forecasting Business Investmentin Germany
Investment in equipment and machinery is a very important component of GDP. In this paper we examine whether data from business tendency surveys are useful for a timely assessment of current investment behavior. In addition we investigate whether the survey results are helpful for forecastinginvestment growth in the short run. The first question is addressed with thehelp of spectral analysis. To study the forecast ability we estimate linearautoregressive and additive autoregressive models. The forecasting performance is assessed through filtered residuals. The analyses show that the business survey is indeed a useful tool for assessing investment in equipment and machinery.Business tendency surveys, forecasting, investment, linear autoregression, additive autoregression
Component Selection in the Additive Regression Model
Similar to variable selection in the linear regression model, selecting
significant components in the popular additive regression model is of great
interest. However, such components are unknown smooth functions of independent
variables, which are unobservable. As such, some approximation is needed. In
this paper, we suggest a combination of penalized regression spline
approximation and group variable selection, called the lasso-type spline method
(LSM), to handle this component selection problem with a diverging number of
strongly correlated variables in each group. It is shown that the proposed
method can select significant components and estimate nonparametric additive
function components simultaneously with an optimal convergence rate
simultaneously. To make the LSM stable in computation and able to adapt its
estimators to the level of smoothness of the component functions, weighted
power spline bases and projected weighted power spline bases are proposed.
Their performance is examined by simulation studies across two set-ups with
independent predictors and correlated predictors, respectively, and appears
superior to the performance of competing methods. The proposed method is
extended to a partial linear regression model analysis with real data, and
gives reliable results
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