spacetime texture representation and recognition based on a spatiotemporal orientation analysis this paper is concerned with the representation and recognition of the observed dynamics i e excluding purely spatial appearance cues of spacetime texture based on a spatiotemporal orientation analysis the term spacetime texture is taken to refer to patterns in visual spacetime x y t that primarily are characterized by the aggregate dynamic properties of elements or local measurements accumulated over a region of spatiotemporal support rather than in terms of the dynamics of individual constituents examples include image sequences of natural processes that exhibit stochastic dynamics e g fire water and windblown vegetation as well as images of simpler dynamics when analyzed in terms of aggregate region properties e g uniform motion of elements in imagery such as pedestrians and vehicular traffic spacetime texture representation and recognition is important as it provides an early means of capturing the structure of an ensuing image stream in a meaningful fashion toward such ends a novel approach to spacetime texture representation and an associated recognition method are described based on distributions histograms of spacetime orientation structure empirical evaluation on both standard and original image data sets shows the promise of the approach including significant improvement over alternative state of the art approaches in recognizing the same pattern from different viewpoints

computing moments of discrete order statistics from non identical distributions abstract in life testing and reliability studies it is commonly assumed that the lifetimes come from a continuous distribution while this often makes derivations more tractable under certain circumstances it may be more realistic to assume a discrete distribution recent literature has heavily focused on the former and with this results have been aimed at deriving properties in the case of independent and identically distributed random variables and associated order statistics in this paper we consider the case of discrete order statistics from non identical distributions we derive expression for single and product moments and with this covariances we demonstrate the results for the geometric and binomial distributions

the sr approach a new estimation procedure for non linear and non gaussian dynamic term structure models abstract this paper suggests a new approach for estimating linear and non linear dynamic term structure models with latent factors we impose no distributional assumptions on the factors which therefore may be non gaussian the novelty of our approach is to use many observables yields or bond prices in the cross section dimension this implies that the latent factors can be determined quite accurately by a sequence of cross section regressions we also show how output from these regressions can be used to obtain model parameters by a two or three step moment based estimation procedure

a unified approach to validating univariate and multivariate conditional distribution models in time series abstract modeling conditional distributions in time series has attracted increasing attention in economics and finance we develop a new class of generalized cramer von mises gcm specification tests for time series conditional distribution models using a novel approach which embeds the empirical distribution function in a spectral framework our tests check a large number of lags and are therefore expected to be powerful against neglected dynamics at higher order lags which is particularly useful for non markovian processes despite using a large number of lags our tests do not suffer much from loss of a large number of degrees of freedom because our approach naturally downweights higher order lags which is consistent with the stylized fact that economic or financial markets are more affected by recent past events than by remote past events unlike the existing methods in the literature the proposed gcm tests cover both univariate and multivariate conditional distribution models in a unified framework they exploit the information in the joint conditional distribution of underlying economic processes moreover a class of easy to interpret diagnostic procedures are supplemented to gauge possible sources of model misspecifications distinct from conventional cm and kolmogorov smirnov ks tests which are also based on the empirical distribution function our gcm test statistics follow a convenient asymptotic n 0 1 distribution and enjoy the appealing nuisance parameter free property that parameter estimation uncertainty has no impact on the asymptotic distribution of the test statistics simulation studies show that the tests provide reliable inference for sample sizes often encountered in economics and finance

on the structure and estimation of hierarchical archimedean copulas in this paper we provide a method for estimating multivariate distributions defined through hierarchical archimedean copulas in general the true structure of the hierarchy is unknown but we develop a computationally efficient technique to determine it from the data for this purpose we introduce a hierarchical estimation procedure for the parameters and provide an asymptotic analysis we consider both parametric and nonparametric estimation of the marginal distributions a simulation study and an empirical application show the effectiveness of the grouping procedure in the sense of structure selection

a parametric study of the term structure dynamics abstract we present an analysis of the dynamics of the term structure of interest rates based on the study of the time evolution of the parameters of a variation of the nelson siegel model the results show that it is extremely difficult to find a relation between the evolution of the term structure and the behavior of macroeconomic variables different from the official interest rate

convolutional autoregressive models for functional time series abstract functional data analysis has became an increasingly popular class of problems in statistical research however functional data observed over time with serial dependence remains a less studied area motivated by bosq 2000 who first introduced the functional autoregressive models we propose a convolutional functional autoregressive model where the function at time t is a result of the sum of convolutions of the past functions and a set of convolution functions plus a noise process mimicking the vector autoregressive process it provides an intuitive and direct interpretation of the dynamics of a stochastic process instead of principal component analysis commonly used in functional data analysis we adopt a sieve estimation procedure based on b spline approximation of the convolution functions we establish convergence rate of the proposed estimator and investigate its theoretical properties the model building model validation and prediction procedures are also developed both simulated and real data examples are presented

modeling maxima with autoregressive conditional frechet model abstract this paper introduces a novel dynamic generalized extreme value gev framework for modeling the time varying behavior of maxima in financial time series specifically an autoregressive conditional frechet acf model is proposed in which the maxima are modeled by a frechet distribution with time varying scale parameter volatility and shape parameter tail index conditioned on past information the acf provides a direct and accurate modeling of the time varying behavior of maxima and furthermore offers a new angle to study the tail risk dynamics in financial markets probabilistic properties of acf are studied and a maximum likelihood estimator is used for model estimation with its statistical properties investigated simulations show the flexibility of acf and confirm the reliability of its estimators two real data examples on cross sectional stock returns and high frequency foreign exchange returns are used to demonstrate the acf modeling approach where significant improvement over the static gev has been observed for market tail risk monitoring and conditional var estimation empirical result of acf is consistent with the findings of the dynamic peak over threshold pot literature that the tail index of financial markets varies through time

statistical inference procedures for bivariate archimedean copulas abstract a bivariate distribution function h x y with marginals f x and g y is said to be generated by an archimedean copula if it can be expressed in the form h x y 1 f x g y for some convex decreasing function defined on 0 1 in such a way that 1 0 many well known systems of bivariate distributions belong to this class including those of gumbel ali mikhail haq thelot clayton frank and hougaard frailty models also fall under that general prescription this article examines the problem of selecting an archimedean copula providing a suitable representation of the dependence structure between two variates x and y in the light of a random sample x 1 y 1 x n y n the key to the estimation procedure is a one dimensional empirical distribution function that can be constructed whether the uniform representation of x and y is archimedean or not and independently of their marginals this semiparametric estimator based on a decomposition of kendall s tau statistic

a semiparametric estimation procedure of dependence parameters in multivariate families of distributions summary this paper investigates the properties of a semiparametric method for estimating the dependence parameters in a family of multivariate distributions the proposed estimator obtained as a solution of a pseudo likelihood equation is shown to be consistent asymptotically normal and fully efficient at independence a natural estimator of its asymptotic variance is proved to be consistent comparisons are made with alternative semiparametric estimators in the special case of clayton s model for association in bivariate data

a glivenko cantelli lemma and weak convergence for empirical processes of associated sequences with the help of an extension of hoeffding s equality we develop a way for estimating the covariance structures for empirical functions of associated sequences in terms of covariances of the original random variables based on these estimations a glivenko cantelli lemma for associated sequences and weak convergence for empirical processes of stationary associated sequences are obtained all under the conditions on covariances of the original random variables

global yield curve dynamics and interactions a dynamic nelson siegel approach the popular nelson siegel 1987 yield curve is routinely fit to cross sections of intra country bond yields and diebold and li 2006 have recently proposed a dynamized version in this paper we extend diebold li to a global context modeling a potentially large set of country yield curves in a framework that allows for both global and country specific factors in an empirical analysis of term structures of government bond yields for the germany japan the u k and the u s we find that global yield factors do indeed exist and are economically important generally explaining significant fractions of country yield curve dynamics with interesting differences across countries

maximum likelihood and the bootstrap for nonlinear dynamic models we provide a unified framework for analyzing bootstrapped extremum estimators of nonlinear dynamic models for heterogeneous dependent stochastic processes we apply our results to the moving blocks bootstrap of kunsch ann stat 17 1989 1217 and liu and singh in r lepage l billiard eds exploring the limits of the bootstrap wiley new york 1992 and prove the first order asymptotic validity of the bootstrap approximation to the true distribution of quasi maximum likelihood estimators we also consider bootstrap testing in particular we prove the first order asymptotic validity of the bootstrap distribution of suitable bootstrap analogs of wald and lagrange multiplier statistics for testing hypotheses

application of copulas to improve covariance estimation for partial least squares dimension reduction techniques such as partial least squares are useful for computing summary measures and examining relationships in complex settings partial least squares requires an estimate of the covariance matrix as a first step in the analysis making this estimate critical to the results in addition the covariance matrix also forms the basis for other techniques in multivariate analysis such as principal component analysis and independent component analysis this paper has been motivated by an example from an imaging study in alzheimer s disease where there is complete separation between alzheimer s and control subjects for one of the imaging modalities this separation occurs in one block of variables and does not occur with the second block of variables resulting in inaccurate estimates of the covariance we propose the use of a copula to obtain estimates of the covariance in this setting where one set of variables comes from a mixture distribution simulation studies show that the proposed estimator is an improvement over the standard estimators of covariance the methods are illustrated from the motivating example from a study in the area of alzheimer s disease

estimation and model selection of semiparametric copula based multivariate dynamic models under copula misspecification we introduce a new class of semiparametric copula based multivariate dynamic scomdy models which specify the conditional mean and the conditional variance of a multivariate time series parametrically but specify the multivariate distribution of the standardized innovation semiparametrically as a parametric copula evaluated at nonparametric marginal distributions we first study large sample properties of the estimators of scomdy model parameters under a misspecified parametric copula then propose pseudo likelihood ratio plr tests for model selection between two scomdy models with possibly misspecified copulas and finally develop plr tests for model selection between more than two scomdy models the limiting null distributions of the plr tests do not depend on the estimation of conditional mean and conditional variance parameters hence are very easy to simulate empirical applications to three and higher dimensional daily exchange rate series indicate that a scomdy model with a tail dependent copula is generally preferred

partial identification and inference in censored quantile regression abstract in this paper we study partial identification and inference in a linear quantile regression where the dependent variable is subject to possibly unknown dependent censoring characterized by an archimedean copula an outer set of the identified set for the regression coefficient is characterized via inequality constraints for one parameter ordered families of archimedean copulas we construct a simple confidence set by inverting an asymptotically pivotal statistic a bootstrap confidence set is also constructed sensitivity of the identified set to possible misspecification of the true copula and the finite sample performance of the boostrap confidence set are investigated numerically

exponential series estimator of multivariate densities abstract we present an exponential series estimator ese of multivariate densities which has an appealing information theoretic interpretation for a d dimensional random variable x with density p 0 the ese takes the form p x exp i 1 0 m 1 i d 0 m d i i x where i are some real valued linearly independent functions defined on the support of p 0 we derive the convergence rate of the ese in terms of the kullback leibler information criterion the integrated squared error and some other metrics we also derive its almost sure uniform convergence rate we then establish the asymptotic normality of p we undertake two sets of monte carlo experiments the first experiment examines the ese performance using mixtures of multivariate normal densities the second estimates copula density functions the results demonstrate the efficacy of the ese an empirical application on the joint distributions of stock returns is presented

forecasting the term structure of government bond yields despite powerful advances in yield curve modeling in the last 20 years comparatively little attention has been paid to the key practical problem of forecasting the yield curve in this paper we do so we use neither the no arbitrage approach nor the equilibrium approach instead we use variations on the nelson siegel exponential components framework to model the entire yield curve period by period as a three dimensional parameter evolving dynamically we show that the three time varying parameters may be interpreted as factors corresponding to level slope and curvature and that they may be estimated with high efficiency we propose and estimate autoregressive models for the factors and we show that our models are consistent with a variety of stylized facts regarding the yield curve we use our models to produce term structure forecasts at both short and long horizons with encouraging results in particular our forecasts appear much more accurate at long horizons than various standard benchmark forecasts r 2005 published by elsevier b v

copula based time series with filtered nonstationarity abstract economic and financial time series data can exhibit nonstationary and nonlinear patterns simultaneously this paper studies copula based time series models that capture both patterns we introduce a procedure where nonstationarity is removed via a filtration and then the nonlinear temporal dependence in the filtered data is captured via a flexible markov copula we propose two estimators of the copula dependence parameters the parametric two step copula estimator where the marginal distribution of the filtered series is estimated parametrically and the semiparametric two step copula estimator where the marginal distribution is estimated via a rescaled empirical distribution of the filtered series we show that the limiting distribution of the parametric copula estimator depends on the nonstationary filtration and the parametric marginal distribution estimation and may be non normal surprisingly the limiting distribution of the semiparametric copula estimator using the filtered data is shown to be the same as that without nonstationary filtration which is normal and free of marginal distribution specification the simple and robust properties of the semiparametric copula estimators extend to models with misspecified copulas and facilitate statistical inferences such as hypothesis testing and model selection tests on semiparametric copula based dynamic models in the presence of nonstationarity monte carlo studies and real data applications are presented

higher order correlations in common input shapes the output spiking activity of a neural population abstract recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non gaussian inputs across neurons the presence of beyond pairwise correlations in the neuronal inputs and the spiking outputs following a non gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns to this end we quantify the amount of higher order correlations in the common neuronal inputs and outputs of a population of neurons we provide a novel formalism of easy numerical implementation that can capture the subtle changes of the inputs heterogeneities within our approach correlations across neurons arise from q gaussian inputs into threshold neurons and higher order correlations in the spiking outputs activity are quantified by the parameter q we present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics and we show under which conditions higher order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs

selection of mixed copula model via penalized likelihood a fundamental issue of applying a copula method in applications is how to choose an appropriate copula function for a given problem in this article we address this issue by proposing a new copula selection approach via penalized likelihood plus a shrinkage operator the proposed method selects an appropriate copula function and estimates the related parameters simultaneously we establish the asymptotic properties of the proposed penalized likelihood estimator including the rate of convergence and asymptotic normality and abnormality particularly when the true coefficient parameters may be on the boundary of the parameter space and the dependence parameters are in an unidentified subset of the parameter space we show that the limiting distribution for boundary parameter estimator is half normal and the penalized likelihood estimator for unidentified parameter converges to an arbitrary value finally monte carlo simulation studies are carried out to illustrate the finite sample performance of the pro

a semiparametric maximum likelihood estimator this paper presents a procedure for analyzing a model in which the parameter vector has two parts a finite dimensional component 8 and a nonparametric component a the procedure does not require parametric modeling of but assumes that the true density of the data satisfies an index restriction the idea is to construct a parametric model passing through the true model and to estimate by setting the score for the parametric model to zero the score is estimated nonparametrically and the estimator is shown to be n consistent and asymptotically normal the estimator is then shown to attain the semiparametric efficiency bound characterized in begun et al 1983 for multivariate nonlinear regression simultaneous equations partially specified regression index regression censored regression switching regression and disequilibrium models in which the error densities are unknown

a noisy principal component analysis for forward rate curves abstract principal component analysis pca is the most common nonparametric method for estimating the volatility structure of gaussian interest rate models one major difficulty in the estimation of these models is the fact that forward rate curves are not directly observable from the market so that non trivial observational errors arise in any statistical analysis in this work we point out that the classical pca analysis is not suitable for estimating factors of forward rate curves due to the presence of measurement errors induced by market microstructure effects and numerical interpolation our analysis indicates that the pca based on the long run covariance matrix is capable to extract the true covariance structure of the forward rate curves in the presence of observational errors moreover it provides a significant reduction in the pricing errors due to noisy data typically found in forward rate curves

inference on co integration parameters in heteroskedastic vector autoregressions we consider estimation and hypothesis testing on the coefficients of the co integrating relations and the adjustment coefficients in vector autoregressions driven by shocks which display both conditional and unconditional heteroskedasticity of a quite general and unknown form we show that the conventional results in johansen 1996 for the maximum likelihood estimators and associated likelihood ratio tests derived under homoskedasticity do not in general hold under heteroskedasticity as a result standard confidence intervals and hypothesis tests on these coefficients are potentially unreliable solutions based on wald tests using a sandwich estimator of the variance matrix and on the use of the wild bootstrap are discussed these do not require the practitioner to specify a parametric model for volatility we establish the conditions under which these methods are asymptotically valid a monte carlo simulation study demonstrates that significant improvements in finite sample size can be obtained by the bootstrap over the corresponding asymptotic tests in both heteroskedastic and homoskedastic environments an application to the term structure of interest rates in the us illustrates the difference between standard and bootstrap inferences regarding hypotheses on the co integrating vectors and adjustment coefficients

nonparametric tests for constant tail dependence with an application to energy and finance the present paper proposes new tests for detecting structural breaks in the tail dependence of multivariate time series using the concept of tail copulas to obtain asymptotic properties we derive a new limit result for the sequential empirical tail copula process moreover consistency of both the tests and a change point estimator are proven we analyze the finite sample behavior of the tests by monte carlo simulations finally and crucial from a risk management perspective we apply the new findings to datasets from energy and financial markets

on the ramanujan conjecture and finiteness of poles for certain l functions in this paper we prove a number of general results about automorphic l functions which appear in the constant terms of eisenstein series for an arbitrary quasi split connected reductive algebraic group over a number field these l functions were first introduced by langlands for chevalley groups 20 and through them he was led to make some of his deep conjectures 21 one significance of these l functions is that all the automorphic l functions studied so far are among them there are three general results first we obtain a uniform line of absolute convergence for all of these l functions theorem 5 1 a uniform estimate for the hecke eigenvalues of generic cusp forms on many absolutely simple quasi split connected reductive algebraic groups over number fields corollary 5 4 and an improvement on the best available estimate for the fourier coefficients of maass wave forms corollary 5 5 also follow next we establish a meromorphic continuation and functional equation for each of these l functions theorem 6 1 finally in theorem 6 2 we prove finiteness of poles on the whole complex plane for an important class of these l functions corollaries 6 6 through 6 10 more precisely let g be a quasi split connected reductive algebraic group over a number field f set g g a f where a f is the ring of adeles of f fix a borel subgroup b of g over f and let u be its unipotent radical let p be a maximal f parabolic subgroup of g with p d b in the context of the problems studied here nothing new will be obtained if one drops the maximality condition on p write p mn a levi decomposition n c u and let b u p m and n be the corresponding groups of adelic points for every place v of f let gv g fj similarly we have bv us pv me and nv let x xv be a generic character of u f u cf section 3 then each xv is generic let r ov 7tv be a cusp form on m we shall say 7t is

information criteria for selecting possibly misspecified parametric models abstract we consider penalized likelihood criteria for selecting models of dependent processes the models may be strictly nested overlapping or nonnested linear or nonlinear and correctly specified or misspecified we provide sufficient conditions on the penalty to guarantee the selection with probability one or with probability approaching one of the model attaining the lower average kullback leibler information criterion klic or when both have the same klic the more parsimonious model as special cases our results describe the akaike schwarz and hannan quinn information criteria as examples we consider selection of armax garch and star models

the unitary dual of p adic g_2 in this paper is completly solved the unitarizability problem for the p adic reductive group of type g 2 the unitary dual of p adic g 2 is described in terms of supercuspidal representations of its levi subgroups

a likelihood ratio test for spatial model selection abstract this paper develops a nondegenerate likelihood ratio test for model selection between two competitive spatial econometrics models it generalizes the test of vuong 1989 to models with spatial near epoch dependent ned data we do not make any structural assumption on the true model specification and allow for the cases where both or one of the two competing models are mis specified the test is valid whether two models are nested or non nested as a prerequisite of the test we first show that quasi maximum likelihood estimators qmle of spatial econometrics models are consistent estimators of their pseudo true values and are asymptotically normal under regularity conditions in particular we study spatial autoregressive models with spatial autoregressive errors sarar and matrix exponential spatial specification mess models we derive the limiting null distribution of the test statistic a spatial heteroskedastic and autoregressive consistent estimator of asymptotic variance of the test statistic under the null which is necessary to implement the test is constructed monte carlo experiments are designed to investigate finite sample performance of qmles for sarar and mess models as well as the size and power of the proposed test

semiparametric dynamic max copula model for multivariate time series summary the paper presents a novel non linear framework for the construction of flexible multivariate dependence structure i e copulas from existing copulas based on a straightforward pairwise max rule the newly constructed max copula has a closed form and has strong interpretability compared with the classical linear symmetric mixture copula the max copula can be viewed as a non linear asymmetric framework it is capable of modelling asymmetric dependence and joint tail behaviour while also offering good performance in non extremal behaviour modelling max copulas that are based on single factor and block factor models are developed to offer parsimonious modelling for structured dependence especially in high dimensional applications combined with semiparametric time series models the max copula can be used to develop flexible and accurate models for multivariate time series a new semiparametric composite maximum likelihood method is proposed for parameter estimation where the consistency and asymptotic normality of estimators are established the flexibility of the max copula and the accuracy of the proposed estimation procedure are illustrated through extensive numerical experiments real data applications in value at risk estimation and portfolio optimization for financial risk management demonstrate the max copula s promising ability to capture accurately joint movements of high dimensional multivariate stock returns under both normal and crisis regimes of the financial market

a simple analytics framework for evaluating mean escape time in different term structures with stochastic volatility abstract the aim of this paper is to propose a simple framework for analyzing the mean escape time of interest rate product in different term structures with stochastic volatility in the modeling perspective we utilize the one factor hull white model to design the dynamics of interest rate returns whose stochastic volatility term is assumed to follow the cox ingersoll ross process furthermore we apply the nelson siegel function to simulate various scenarios of term structure based on the us treasury bill and european bond then we analyze the mean escape time surfaces of different term structures for theoretical flat yield curves and two empirical s shaped yield curves whose term structures of short mid and long term maturities are shifted by the modified parameters of the nelson siegel function we observe that the survival probability begins with one and reduces to zero with different rate of decay for different interest rates and volatilities furthermore the results of empirical term structures imply that the adjustment in the yield of long term maturity changes the structure of met surface more significantly than that of short and long term maturities lastly we detect the noise enhanced stability phenomenon from the mean escape time for all cases of flat yield treasury bill and european bond

time series modelling with semiparametric factor dynamics high dimensional regression problems which reveal dynamic behavior are typically analyzed by time propagation of a few number of factors the inference on the whole system is then based on the low dimensional time series analysis such high dimensional problems occur frequently in many different fields of science in this article we address the problem of inference when the factors and factor loadings are estimated by semiparametric methods this more flexible modeling approach poses an important question is it justified from an inferential point of view to base statistical inference on the estimated times series factors we show that the difference of the inference based on the estimated time series and true unobserved time series is asymptotically negligible our results justify fitting vector autoregressive processes to the estimated factors which allows one to study the dynamics of the whole high dimensional system with a low dimensional representation we illustrate the theory with a simulati

the dynamics of economic functions modeling and forecasting the yield curve the class of functional signal plus noise fsn models is introduced that provides a new general method for modelling and forecasting time series of economic functions the underlying continuous economic function or signal is a natural cubic spline whose dynamic evolution is driven by a cointegrated vector autoregression for the ordinates or y values at the knots of the spline the natural cubic spline provides flexible cross sectional fit and results in a linear state space model this fsn model achieves dimension reduction provides a coherent description of the observed yield curve and its dynamics as the cross sectional dimension n becomes large and can feasibly be estimated and used for forecasting when n is large the integration and cointegration properties of the model are derived the fsn models are then applied to forecasting 36 dimensional yield curves for us treasury bonds at the one month ahead horizon the method consistently outperforms the diebold and li 2006 and random walk forecasts on the basis of both mean square forecast error criteria and economically relevant loss functions derived from the realised profits of pairs trading algorithms the analysis also highlights in a concrete setting the dangers of attempts to infer the relative economic value of model forecasts on the basis of their associated mean square forecast errors

a proof of langland s conjecture on plancherel measures complementary series of p adic groups analysis of p adic reductive groups our first result theorem 7 9 proves a conjecture of langlands on normalization of intertwining operators by means of local langlands root numbers and l functions at least when the group is quasi split and the inducing representation is generic assuming two natural conjectures in harmonic analysis of p adic groups we also prove the validity of the conjecture in general theorem 9 5 as our second result we obtain all the complementary series and special representations of quasi split p adic groups coming from rank one parabolic subgroups and generic supercuspidal represen

simulated method of moments estimation for copula based multivariate models this article considers the estimation of the parameters of a copula via a simulated method of moments mm type approach this approach is attractive when the likelihood of the copula model is not known in closed form or when the researcher has a set of dependence measures or other functionals of the copula that are of particular interest the proposed approach naturally also nests mm and generalized method of moments estimators drawing on results for simulation based estimation and on recent work in empirical copula process theory we show the consistency and asymptotic normality of the proposed estimator and obtain a simple test of overidentifying restrictions as a specification test the results apply to both iid and time series data we analyze the finite sample behavior of these estimators in an extensive simulation study we apply the model to a group of seven financial stock returns and find evidence of statistically significant tail dependence and mild evidence that the dependence between thes

estimation and model selection of semiparametric multivariate survival functions under general censorship we study estimation and model selection of semiparametric models of multivariate survival functions for censored data which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals we obtain the consistency and root n asymptotic normality of a two step copula estimator to the pseudo true copula parameter value according to klic and provide a simple consistent estimator of its asymptotic variance allowing for a first step nonparametric estimation of the marginal survivals we establish the asymptotic distribution of the penalized pseudo likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship an empirical application is provided

a bayesian multivariate functional dynamic linear model abstractwe present a bayesian approach for modeling multivariate dependent functional data to account for the three dominant structural features in the data functional time dependent and multivariate components we extend hierarchical dynamic linear models for multivariate time series to the functional data setting we also develop bayesian spline theory in a more general constrained optimization framework the proposed methods identify a time invariant functional basis for the functional observations which is smooth and interpretable and can be made common across multivariate observations for additional information sharing the bayesian framework permits joint estimation of the model parameters provides exact inference up to mcmc error on specific parameters and allows generalized dependence structures sampling from the posterior distribution is accomplished with an efficient gibbs sampling algorithm we illustrate the proposed framework with two applications 1 multi economy yield curve data

forecasting the yield curve in a data rich environment a no arbitrage factor augmented var approach this paper suggests a term structure model which parsimoniously exploits a broad macroeconomic information set the model uses the short rate and the common components of a large number of macroeconomic variables as factors precisely the dynamics of the short rate are modeled with a factor augmented vector autoregression and the term structure is derived using parameter restrictions implied by no arbitrage the model has economic appeal and provides better out of sample yield forecasts at intermediate and long horizons than a number of previously suggested approaches the forecast improvement is highly significant and particularly pronounced for short and medium term maturities

generalized runs tests for the iid hypothesis we provide a family of tests for the iid hypothesis based on generalized runs powerful against unspecified alternatives providing a useful complement to tests designed for specific alternatives such as serial correlation garch or structural breaks our tests have appealing computational simplicity in that they do not require kernel density estimation with the associated challenge of bandwidth selection simulations show levels close to nominal asymptotic levels our tests have power against both dependent and heterogeneous alternatives as both theory and simulations demonstrate

copula based multivariate garch model with uncorrelated dependent errors abstract multivariate garch mgarch models are usually estimated under multivariate normality in this paper for non elliptically distributed financial returns we propose copula based multivariate garch c mgarch model with uncorrelated dependent errors which are generated through a linear combination of dependent random variables the dependence structure is controlled by a copula function our new c mgarch model nests a conventional mgarch model as a special case the aim of this paper is to model mgarch for non normal multivariate distributions using copulas we model the conditional correlation by mgarch and the remaining dependence by a copula separately and simultaneously we apply this idea to three mgarch models namely the dynamic conditional correlation dcc model of engle engle r f 2002 dynamic conditional correlation a simple class of multivariate generalized autoregressive conditional heteroskedasticity models journal of business and economic statistics 20 339 350 the varying correlation vc model of tse and tsui tse y k tsui a k 2002 a multivariate generalized autoregressive conditional heteroscedasticity model with time varying correlations journal of business and economic statistics 20 351 362 and the bekk model of engle and kroner engle r f kroner k f 1995 multivariate simultaneous generalized arch econometric theory 11 122 150 empirical analysis with three foreign exchange rates indicates that the c mgarch models outperform dcc vc and bekk in terms of in sample model selection and out of sample multivariate density forecast and in terms of these criteria the choice of copula functions is more important than the choice of the volatility models

identification in a generalization of bivariate probit models with dummy endogenous regressors abstract this paper provides identification results for a class of models specified by a triangular system of two equations with binary endogenous variables the joint distribution of the latent error terms is specified through a parametric copula structure that satisfies a particular dependence ordering while the marginal distributions are allowed to be arbitrary but known this class of models is broad and includes bivariate probit models as a special case the paper demonstrates that having an exclusion restriction is necessary and sufficient for global identification in a model without common exogenous covariates where the excluded variable is allowed to be binary having an exclusion restriction is sufficient in models with common exogenous covariates that are present in both equations the paper then extends the identification analyses to a model where the marginal distributions of the error terms are unknown

on regular square integrable representations of p adic groups we study the problem of construction of noncuspidal irreducible square integrable representations of classical p adic groups and questions related to it starting from cuspidal reducibilities in the generalized rank one case we give a construction of regular irreducible square integrable representations of p adic gsp n sp n and so 2 n 1 under an assumption expected to hold in general we show that all such representations should come from this construction further we obtain a number of general results about places where square integrable representations can appear in parabolically induced representation among others we show that in irreducible cuspidal representations of general linear groups only the self contragredient ones play a role in the construction of square integrable representations of the above groups at the end we use our regular square integrable representations to study some basic properties of whittaker models we also show that there exists nondegenerate standard modules of classical groups which contain degenerate irreducible subrepresentations

efficient estimation of multivariate semi nonparametric garch filtered copula models abstract this paper considers estimation of semi nonparametric garch filtered copula models in which the individual time series are modeled by semi nonparametric garch and the joint distributions of the multivariate standardized innovations are characterized by parametric copulas with nonparametric marginal distributions the models extend those of chen and fan 2006 to allow for semi nonparametric conditional means and volatilities which are estimated via the method of sieves the fitted residuals are then used to estimate the copula parameters and the marginal densities of the standardized innovations jointly via the sieve maximum likelihood sml we show that even using nonparametric filtered data the copula parameters estimated via our sml and the two step procedure of chen and fan 2006 are still root n consistent and asymptotically normal and the asymptotic variances of both estimators do not depend on the nonparametric filtering errors even more surprisingly our sml copula estimator using the filtered data achieves the full semiparametric efficiency bound as if the standardized innovations were directly observed these nice properties lead to simple and more accurate estimation of value at risk var for multivariate financial data with flexible dynamics contemporaneous tail dependence and asymmetric distributions of innovations monte carlo studies demonstrate that our sml estimators of the copula parameters and the marginal distributions of the standardized innovations have smaller variances and smaller mean squared errors compared to those of the two step estimators in finite samples a real data application is presented

wavelet domain multifractal analysis for static and dynamic texture classification in this paper we propose a new texture descriptor for both static and dynamic textures the new descriptor is built on the wavelet based spatial frequency analysis of two complementary wavelet pyramids standard multiscale and wavelet leader these wavelet pyramids essentially capture the local texture responses in multiple high pass channels in a multiscale and multiorientation fashion in which there exists a strong power law relationship for natural images such a power law relationship is characterized by the so called multifractal analysis in addition two more techniques scale normalization and multiorientation image averaging are introduced to further improve the robustness of the proposed descriptor combining these techniques the proposed descriptor enjoys both high discriminative power and robustness against many environmental changes we apply the descriptor for classifying both static and dynamic textures our method has demonstrated excellent performance in comparison with the state of the art approaches in several public benchmark datasets

reduction to real infinitesimal character in affine hecke algebras the main result of this paper is to show that the problem of the determination of the unramified unitary dual of a split p adic group is equivalent to the problem of determining the unitary dual of the corresponding graded hecke algebra in bm the authors established this equivalence in the case of iwahori spherical representations under a certain restriction namely it was essential for the infinitesimal character to be real in the terminology of bmj in terms of the langlands deligne lusztig parameters s u p kul the restriction is that s e lg be a purely hyperbolic element the technique used in bm was to combine the notion of the signature of a k character in v with some facts which follow from kl namely that the 2w characters of tempered representations are linearly independent this is essentially true precisely when the infinitesimal character is real for if not s has an elliptic part se such that

viewpoint invariant texture description using fractal analysis image texture provides a rich visual description of the surfaces in the scene many texture signatures based on various statistical descriptions and various local measurements have been developed existing signatures in general are not invariant to 3d geometric transformations which is a serious limitation for many applications in this paper we introduce a new texture signature called the multifractal spectrum mfs the mfs is invariant under the bi lipschitz map which includes view point changes and non rigid deformations of the texture surface as well as local affine illumination changes it provides an efficient framework combining global spatial invariance and local robust measurements intuitively the mfs could be viewed as a better histogram with greater robustness to various environmental changes and the advantage of capturing some geometrical distribution information encoded in the texture experiments demonstrate that the mfs codes the essential structure of textures with very low dimension and thus represents an useful tool for texture classification

the encompassing principle and its application to testing nonnested hypotheses the encompassing principle is used to develop a testing framework which unifies the literature on non nested testing allowing analysis of the relationship between alternative tests and in particular enabling asymptotic and finite sample equivalences and identities to be established easily when they exist as well as embracing nested hypothesis testing the concept of the implicit null hypothesis of a test is introduced to show that the effective acceptance region for some tests extends beyond the acceptance region corresponding to the null of interest and so such tests can be inconsistent against fixed alternatives closely related to the nominal null and alternative the analysis is illustrated by an application to two non nested linear regression models and it is shown that the conventional f test as well as all the one degree of freedom non nested tests has an encompassing interpretation and that the f test is a complete parametric encompassing test

goodness of fit test for specification of semiparametric copula dependence models this paper concerns goodness of fit tests for semiparametric copula models our contribution is two fold we first propose a new test constructed via the comparison between in sample and out of sample pseudo likelihoods under the null hypothesis that the copula model is correctly specified we show that the proposed test statistic converges in probability to a constant equal to the dimension of the parameter space we establish the asymptotic normality and investigate the local power of the test we also extend the proposed test to the specification test of a class of multivariate time series models and propose a new bootstrap procedure to establish the finite sample null distribution which is shown to have better control of type i error than the commonly used bootstrap secondly we introduce a bonferroni based hybrid mechanism to combine several test statistics which yields a useful test this hybrid method is particularly appealing when there exists no single dominant optimal test we conduct comprehensive simulation experiments to compare the proposed new test and hybrid approach with two of the best blanket tests in the literature for illustration we apply the proposed tests to analyze two real datasets

on the formulation of empirical models in dynamic econometrics abstract available information is considered as partitioned into the sets past present and future observations other data of competing models and theory knowledge in each case specific concepts are relevant to empirical model formulation e g innovations for past data exogeneity for present encompassing for contending models etc and various properties of such concepts are established viz encompassing is transitive and asymmetric relationships between concepts are developed e g encompassing entails variance dominance in linear models and related to the notion of a progressive research strategy an empirical model illustrates the various criteria model selection by dynamic simulation tracking performance is critically evaluated

encompassing tests when no model is encompassing this paper considers regression based tests for encompassing when none of the models under consideration encompasses all the other models for both in and out of sample applications i derive asymptotic distributions and propose feasible procedures to construct confidence intervals and test statistics procedures that are asymptotically valid under the null of encompassing e g davidson and mackinnon econometrica 49 1981 781 can have large asymptotic and finite sample distortions simulations indicate that the proposed procedures can work well in samples of size typically available though the divergence between actual and nominal confidence interval coverage sometimes is large

linearized nelson siegel and svensson models for the estimation of spot interest rates abstract linearized versions of the nelson siegel 1987 and svensson 1994 models for the cross sectional estimation of spot yield curves from samples of coupon bonds are developed and analyzed it is shown how these models can be made linear in the level slope and curvature parameters and how prior information about these parameters can be incorporated in the estimation procedure the performance of the linearized models are assessed in a monte carlo setting and with a sample of us government bonds the results reveal that the linearized models compare favorably to the original models in terms of parameter estimates stability computing effort and prevalence of local optima

common factors in conditional distributions for bivariate time series a definition for a common factor for bivariate time series is suggested by considering the decomposition of the conditional density into the product of the marginals and the copula with the conditioning variable being a common factor if it does not directly enter the copula we show the links between this definition and the idea of a common factor as a dominant feature in standard linear representations an application using a business cycle indicator as the common factor in the relationship between u s income and consumption found that both series held the factor in their marginals but not in the copula

classifying dynamic textures via spatiotemporal fractal analysis the large scale images and videos are one kind of the main source of big data dynamic texture dt is essential for understanding the video sequences with spatio temporal similarities this paper presents a powerful tool called dynamic fractal analysis to dt description and classification which integrates rich description of dt with strong robustness to environmental changes the proposed dynamic fractal spectrum dfs for dt sequences is composed of two components the first one is a volumetric dynamic fractal spectrum component v dfs that captures the stochastic self similarities of dt sequences by treating them as 3d volumes the second one is a multi slice dynamic fractal spectrum component s dfs that encodes fractal structures of repetitive dt patterns on 2d slices along different views of the 3d volume to fully exploit various types of dynamic patterns in dt five measurements of dt pixels are collected for the analysis on dt sequences from different perspectives we evaluated our method on four publicly available benchmark datasets all the experimental results have demonstrated the excellent performance of our method in comparison with state of the art approaches highlightsthe dynamic multi fractal analysis is developed for dt description our method is very discriminative and robust to environmental changes a computational acceleration scheme is provided for the proposed descriptor our method exhibits excellent performance on four benchmark datasets

a multivariate extension of hoeffding s lemma abstract hoeffding s lemma gives an integral representation of the covariance of two random variables in terms of difference between their joint and marginal probability functions this identity has been found to be useful tool in studying the dependence structure of various random vectors a generalization of this result for more than 2 random variables is given this involves an integral representation of the multivariate joint cumulant applications of this result include characterizations of independence relationships with various types of dependence are also given author

vines a new graphical model for dependent random variables a new graphical model called a vine for dependent random variables is introduced vines generalize the markov trees often used in modelling high dimensional distributions they differ from markov trees and bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence

a goodness of fit test for copulas based on martingale transformation abstract this paper proposes an asymptotically distribution free test for copulas with dynamic marginal distributions such as garch and arma processes the test is based on the empirical copula process with parametrically estimated marginal distributions by applying the khmaladze 1982 1988 1993 martingale transformation method the transformed empirical process converges to a standard gaussian process so the resulting test statistics are asymptotically distribution free monte carlo simulations show that the test performs well in finite samples an empirical application to test copulas between eur usd and gbp usd exchange rates is provided

approximately normal tests for equal predictive accuracy in nested models abstract forecast evaluation often compares a parsimonious null model to a larger model that nests the null model under the null that the parsimonious model generates the data the larger model introduces noise into its forecasts by estimating parameters whose population values are zero we observe that the mean squared prediction error mspe from the parsimonious model is therefore expected to be smaller than that of the larger model we describe how to adjust mspes to account for this noise we propose applying standard methods west k d 1996 asymptotic inference about predictive ability econometrica 64 1067 1084 to test whether the adjusted mean squared error difference is zero we refer to nonstandard limiting distributions derived in clark and mccracken 2001 tests of equal forecast accuracy and encompassing for nested models journal of econometrics 105 85 110 2005a evaluating direct multistep forecasts econometric reviews 24 369 404 to argue that use of standard normal critical values will yield actual sizes close to but a little less than nominal size simulation evidence supports our recommended procedure

on minimal representations of chevalley groups of type dn en and g2 let g be a simply connected chevalley group of type dn en or g2 in this paper we show that the minimal representation of g is unique for types dn and en and it does not exist for the type g2

the stochastic string model as a unifying theory of the term structure of interest rates we present the stochastic string model of santa clara and sornette 2001 as reformulated by bueno guerrero et al 2015 as a unifying theory of the continuous time modeling of the term structure of interest rates we provide several new results such as a an orthogonality condition for the volatilities in the heath jarrow and morton 1992 hjm model b the interpretation of multi factor hjm models as approximations to a full infinite dimensional model c a result of consistency based on hilbert spaces and d a theorem for option valuation

bond risk premia and gaussian term structure models existing results show that i lagged forward rates help predict bond returns and ii modern markovian dynamic term structure models dtsms cannot match the evidence cochrane jh piazzesi m 2005 bond risk premia amer econom rev 95 1 138 160 we develop the family of conditional mean dtsms where the dynamics depend on current yields and their history through a moving average component our preferred conditional mean model combines one moving average with the usual three gaussian risk factors closely matches the bond risk premium measured from predictive regressions and provides better forecasts of bond returns our framework nests duffee s models with a small hidden factor duffee g 2011 information in and not in the term structure rev financial stud 24 9 2895 2934 and our results compare favorably with his five factor model conditional mean models are easier to estimate than state space term structure models based on kalman estimates of latent factors the online appendix is avai

sequential conditional correlations inference and evaluation this paper presents a new approach to the modeling of conditional correlation matrices within the multivariate garch framework the procedure which consists of breaking the matrix into the product of a sequence of matrices with desirable characteristics in effect converts a highly dimensional and intractable optimization problem into a series of simple and feasible estimations this in turn allows for richer parameterizations and complex functional forms for the single components an empirical application involving the conditional second moments of 69 selected stocks from the nasdaq100 shows how the new procedure results in strikingly accurate measures of the conditional correlations

regime switching dynamic correlations for asymmetric and fat tailed conditional returns abstract a non gaussian multivariate regime switching dynamic correlation model for financial asset returns is proposed it incorporates the multivariate generalized hyperbolic law for the conditional distribution of returns all model parameters are estimated consistently using a new two stage expectation maximization algorithm that also allows for incorporation of shrinkage estimation via quasi bayesian priors it is shown that use of markov switching correlation dynamics not only leads to highly accurate risk forecasts but also potentially reduces the regulatory capital requirements during periods of distress in terms of portfolio performance the new regime switching model delivers consistently higher sharpe ratios and smaller losses than the equally weighted portfolio and all competing models finally the regime forecasts are employed in a new dynamic risk control strategy that avoids most losses during the financial crisis and vastly improves risk adjusted returns

efficient estimation of semiparametric multivariate copula models we propose a sieve maximum likelihood estimation procedure for a broad class of semiparametric multivariate distributions a joint distribution in this class is characterized by a parametric copula function evaluated at nonparametric marginal distributions this class of distributions has gained popularity in diverse fields due to its flexibility in separately modeling the dependence structure and the marginal behaviors of a multivariate random variable and its circumvention of the curse of dimensionality associated with purely nonparametric multivariate distributions we show that the plug in sieve maximum likelihood estimators mles of all smooth functionals including the finite dimensional copula parameters and the unknown marginal distributions are semiparametrically efficient and that their asymptotic variances can be estimated consistently moreover prior restrictions on the marginal distributions can be easily incorporated into the sieve maximum likelihood estimation procedure to achieve fu

the affine arbitrage free class of nelson siegel term structure models we derive the class of arbitrage free affine dynamic term structure models that approximate the widely used nelson siegel yield curve specification our theoretical analysis relates this new class of models to the canonical representation of the three factor arbitrage free affine model our empirical analysis shows that imposing the nelson siegel structure on this canonical representation greatly improves its empirical tractability furthermore we find that improvements in predictive performance are achieved from the imposition of absence of arbitrage