an exact finite rotation shell theory its mixed variational formulation and its finite element implementation a non linear shell theory including transverse shear strains with exact description of the kinematical fields is developed the strain measures are derived via the polar decomposition theorem allowing for an explicit use of a three parametric rotation tensor thus in plane rotations also called drilling degrees of freedom are included in a natural way various alternatives of the theory are derived for a special version of the theory with altogether six kinematical fields different mixed variational principles are given a hybrid finite element formulation which does not exhibit locking phenomena is developed numerical examples of shell deformation at finite rotations with excellent element performance are presented comparison with results reported in the literature demonstrates the features of the theory as well as the proposed finite element formulation

estimating quadratic variation consistently in the presence of endogenous and diurnal measurement error abstract we propose an econometric model that captures the effects of market microstructure on a latent price process in particular we allow for correlation between the measurement error and the return process and we allow the measurement error process to have a diurnal heteroskedasticity we propose a modification of the tsrv estimator of quadratic variation we show that this estimator is consistent with a rate of convergence that depends on the size of the measurement error but is no worse than n 1 6 we investigate in simulation experiments the finite sample performance of various proposed implementations

stress resultant geometrically nonlinear shell theory with drilling rotations part ii computational aspects abstract in this work we discuss some details of the numerical implementation of the geometrically nonlinear shell theory presented in part i two possibilities to represent finite rotations with an orthogonal matrix and with a rotation vector are examined in detail along with their mutual relationship in both spatial and material description the issues pertinent to the consistent linearization procedure corresponding to these rotation parameterizations are also carefully considered the geometrically nonlinear method of incompatible modes is extended to the nonlinear shell theory under consideration and used to provide a 4 node shell element with enhanced performance a rather extensive set of numerical examples in nonlinear elastostatics is solved in order to corroborate the non locking performance of the incompatible mode based shell elements the examples include not only analyses of simple shell structures undergoing very large displacements and rotations but also cases of strong practical interest such as non smooth shell structures and shell structures with stiffeners

two stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity abstract under the two important modern financial market features of noise and non synchronicity for multiple assets for consistent estimators of the integrated covariations we adopt the two time scale average realized volatility matrix arvm which is a matrix extension of the two time scale realized volatilities of zhang et al 2005 an asymptotic normal theory is provided for the two time scale arvm and resulting realized covariations the asymptotic normality is not directly applicable in practice to construct statistical methods owning to nuisance parameters to bypass the nuisance parameter problem two stage stationary bootstrapping is proposed we establish consistencies of the bootstrap distributions and construct confidence intervals and hypothesis tests for the integrated covariance regression coefficient and correlation coefficient the validity of the stationary bootstrap for the high frequency heterogeneous returns is proved by showing that there exist parameters of the stationary bootstrap blocks so that the bootstrap consistencies hold the proposed bootstrap methods extend the i i d bootstrapping methods for realized covariations by dovonon et al 2013 that are confined to synchronous noise free sampling for high frequency noisy asynchronous samples a monte carlo experiment shows better finite sample performances of the proposed stationary bootstrap methods based on the two time scale arvm estimator than the wild blocks of blocks bootstrap methods of hounyo 2017 based on pre averaged truncated estimator

econometric analysis of multivariate realised qml estimation of the covariation of equity prices under asynchronous trading estimating the covariance between assets using high frequency data is challenging due to market microstructure effects and asynchronous trading in this paper we develop a multivariate realised quasi likelihood qml approach carrying out inference as if the observations arise from an asynchronously observed vector scaled brownian model observed with error under stochastic volatility the resulting realised qml estimator is positive semi definite uses all available data is consistent and asymptotically mixed normal the quasi likelihood is computed using a kalman filter and optimised using a relatively simple em algorithm which scales well with the number of assets we derive the theoretical properties of the estimator and prove that it achieves the efficient rate of convergence we show how to make itobtain the non parametric efficiency bound for this problem the estimator is also analysed usingmonte carlo methods and applied to equity data with varying levels of liquidity

estimation of integrated quadratic covariation with endogenous sampling times abstract when estimating high frequency covariance quadratic covariation of two arbitrary assets observed asynchronously simple assumptions such as independence are usually imposed on the relationship between the prices process and the observation times in this paper we introduce a general endogenous two dimensional nonparametric model because an observation is generated whenever an auxiliary process called observation time process hits one of the two boundary processes it is called the hitting boundary process with time process hbt model we establish a central limit theorem for the hayashi yoshida hy estimator under hbt in the case where the price process and the observation price process follow a continuous ito process we obtain an asymptotic bias we provide an estimator of the latter as well as a bias corrected hy estimator of the high frequency covariance in addition we give a consistent estimator of the associated standard error

bootstrapping integrated covariance matrix estimators in noisy jump diffusion models with non synchronous trading abstract we propose a bootstrap method for estimating the distribution and functionals of it such as the variance of various integrated covariance matrix estimators in particular we first adapt the wild blocks of blocks bootstrap method suggested for the pre averaged realized volatility estimator to a general class of estimators of integrated covolatility we then show the first order asymptotic validity of this method in the multivariate context with a potential presence of jumps dependent microstructure noise irregularly spaced and non synchronous data our results justify using the bootstrap to estimate the covariance matrix of a broad class of covolatility estimators the bootstrap variance estimator is positive semi definite by construction an appealing feature that is not always shared by existing variance estimators of the integrated covariance estimator as an application of our results we also consider the bootstrap for regression coefficients we show that the wild blocks of blocks bootstrap appropriately centered is able to mimic both the dependence and heterogeneity of the scores we provide a proof of construction of bootstrap percentile and percentile t intervals as well as variance estimates in this context this contrasts the traditional pairs bootstrap which is not able to mimic the score heterogeneity even in the simple case where no microstructure noise is present our monte carlo simulations show that the wild blocks of blocks bootstrap improve the finite sample properties of the alternative approach based on the gaussian approximation we illustrate its practical use on high frequency equity data

on the parametrization of finite rotations in computational mechanics a classification of concepts with application to smooth shells this paper concerns the computational treatment of large rotations with application to the finite element discretisation of smooth shells formulations based on various rotational parametrizations are reviewed and classified with respect to their update structure category ii is based on total rotational degrees of freedom leading to an additive update structure whereas category i relies on linearized rotational degrees of freedom leading to a multiplicative update structure based on this classification new formulations are developed among them are the rodrigues formula applied within category ii with additive update structure for the two components of the rotation vector and two successive elementary rotations in the sense of euler angles applied within category i yielding a singularity free formulation the numerical examples confirm that every rotational parametrization when applied within category ii leads to singularities whereas the application within category i enables the calculation of overall rotations unrestricted in size without any singularity

stress resultant geometrically exact form of classical shell model and vector like parameterization of constrained finite rotations summary in this work we consider the geometrically exact shell model subjected to 7nite rotations making use of rotation vector parameters for handling the corresponding constrained rotation for smooth shells a modi7cation of such a parameterization which is based on the incremental rotation vector and thus capable of avoiding the singularity problem is also discussed copyright 2001 john wiley sons ltd

increased correlation among asset classes are volatility or jumps to blame or both abstract we develop estimators and asymptotic theory to decompose the quadratic covariation between two assets into its continuous and jump components in a manner that is robust to the presence of market microstructure noise using high frequency data on different assets classes we find that the recent financial crisis led to an increase in both the quadratic variations of the assets and their correlations however we find little evidence to suggest a change between the relative contributions of the brownian and jump components as both comove co jumps stem from surprising news announcements that occur primarily before the opening of the us market and are also accompanied by an increase in brownian driven correlations

a four node corotational quadrilateral elastoplastic shell element using vectorial rotational variables four node corotational quadrilateral elastoplastic shell element summary a four node corotational quadrilateral elastoplastic shell element is presented the local coordinate system of the element is defined by the two bisectors of the diagonal vectors generated from the four corner nodes and their cross product this local coordinate system rotates rigidly with the element but does not deform with the element as a result the element rigid body rotations are excluded in calculating the local nodal variables from the global nodal variables the two smallest components of each nodal orientation vector are defined as rotational variables leading to the desired additive property for all nodal variables in a nonlinear incremental solution procedure different from other existing corotational finite element formulations the resulting element tangent stiffness matrix is symmetric owing to the commutativity of the local nodal variables in calculating the second derivative of strains with respect to these variables for elastoplastic analyses the maxwell huber hencky von mises yield criterion is employed together with the backward euler return mapping method for the evaluation of the elastoplastic stress state the consistent tangent modulus matrix is derived to eliminate locking problems we use the assumed strain method several elastic patch tests and elastoplastic plate shell problems undergoing large deformation are solved to demonstrate the computational efficiency and accuracy of the proposed formulation copyright 2013 john wiley sons ltd

genuinely resultant shell finite elements accounting for geometric and material non linearity the underlying theory is statically and geometrically exact and it naturally includes small strain and finite strain problems of thin as well as thick shells this paper examines the development of computational procedures employing the newton kantorovich linearization process and the galerkin type discretization method the treatment of finite rotations through an arbitrary parametrization of the rotation group the interpolation procedure of so 3 valued functions underlying the construction of finite element basis

a bias corrected estimator of the covariation matrix of multiple security prices when both microstructure effects and sampling durations are persistent and endogenous i propose a bias corrected non parametric estimator of the covariation matrix of log security prices designed as a convex combination of two realized kernels the estimator is simple but possesses desirable statistical properties including consistency asymptotic normality and the parametric rate of convergence in the presence of persistent diurnally heteroskedastic and endogenous microstructure effects it is robust to the asynchronous trading of multiple securities with persistent and endogenous refresh time durations i also prove the consistency of a subsampling based estimator of the asymptotic covariance matrix of the proposed estimator in simulations the non linear functions of the proposed estimator exhibit smaller bias than those based on a realized kernel while only slightly increasing the variance thereby the proposed estimator reduces the mean squared error

robust high dimensional volatility matrix estimation for high frequency factor model high frequency financial data allow us to estimate large volatility matrices with relatively short time horizon many novel statistical methods have been introduced to address large volatility matr

computational aspects of vector like parametrization of three dimensional finite rotations theoretical and computational aspects of vector like parametrization of three dimensional finite rotations which uses only three rotation parameters are examined in detail in this work the relationship of the proposed parametrization with the intrinsic representation of finite rotations via an orthogonal matrix is clearly identified careful considerations of the consistent linearization procedure pertinent to the proposed parametrization of finite rotations are presented for the chosen model problem of reissner s non linear beam theory pertaining details of numerical implementation are discussed for the simplest choice of the finite element interpolations for a 2 node three dimensional beam element a number of numerical simulations in three dimensional finite rotation analysis are presented in order to illustrate the proposed approach

econometrics of co jumps in high frequency data with noise abstract we establish estimation methods to determine co jumps in multivariate high frequency data with non synchronous observations and market microstructure a rate optimal estimator of the entire quadratic covariation of an ito semimartingale is constructed by a locally adaptive spectral approach thresholding allows to disentangle the co jump from the continuous part we derive a feasible limit theorem for a truncated estimator of integrated covolatility which facilitates asymptotically efficient co volatility estimation in the presence of jumps a test for common jumps is presented simulations and an empirical application to intra day tick data from eurex futures demonstrate the practical value of the approach

large dimensional factor modeling based on high frequency observations abstract this paper develops a statistical theory to estimate an unknown factor structure based on financial high frequency data we derive an estimator for the number of factors and consistent and asymptotically mixed normal estimators of the loadings and factors under the assumption of a large number of cross sectional and high frequency observations the estimation approach can separate factors for continuous and rare jump risk the estimators for the loadings and factors are based on the principal component analysis of the quadratic covariation matrix the estimator for the number of factors uses a perturbed eigenvalue ratio statistic in an empirical analysis of the s p 500 firms we estimate four stable continuous systematic factors which can be approximated very well by a market and industry portfolios jump factors are different from the continuous factors

a nonparametric eigenvalue regularized integrated covariance matrix estimator for asset return data abstract in high frequency data analysis the extreme eigenvalues of a realized covariance matrix are biased when its dimension p is large relative to the sample size n furthermore with non synchronous trading and contamination of microstructure noise we propose a nonparametrically eigenvalue regularized integrated covariance matrix estimator nerive which does not assume specific structures for the underlying integrated covariance matrix we show that nerive is positive definite in probability with extreme eigenvalues shrunk nonlinearly under the high dimensional framework p n c 0 we also prove that in portfolio allocation the minimum variance optimal weight vector constructed using nerive has maximum exposure and actual risk upper bounds of order p 1 2 incidentally the same maximum exposure bound is also satisfied by the theoretical minimum variance portfolio weights all these results hold true also under a jump diffusion model for the log price processes with jumps removed using the wavelet method proposed in fan and wang 2007 they are further extended to accommodate the existence of pervasive factors such as a market factor under the setting p 3 2 n c 0 the practical performance of nerive is illustrated by comparing to the usual two scale realized covariance matrix as well as some other nonparametric alternatives using different simulation settings and a real data set

assessment of uncertainty in high frequency data the observed asymptotic variance the availability of high frequency financial data has generated a series of estimators based on intra day data improving the quality of large areas of financial econometrics however estimating the standard error of these estimators is often challenging the root of the problem is that traditionally standard errors rely on estimating a theoretically derived asymptotic variance and often this asymptotic variance involves substantially more complex quantities than the original parameter to be estimated standard errors are important they are used to assess the precision of estimators in the form of confidence intervals to create feasible statistics for testing to build forecasting models based on say daily estimates and also to optimize the tuning parameters the contribution of this paper is to provide an alternative and general solution to this problem which we call observed asymptotic variance it is a general nonparametric method for assessing asymptotic variance avar it provides consistent estimators of avar for a broad class of integrated parameters t dt where the spot parameter process can be a general semimartingale with continuous and jump components the observed avar is implemented with the help of a two scales method its construction works well in the presence of microstructure noise and when the observation times are irregular or asynchronous in the multivariate case the methodology is valid for a wide variety of estimators including the standard ones for variance and covariance and also for more complex estimators such as of leverage effects high frequency betas and semivariance

estimating the quadratic covariation matrix from noisy observations local method of moments and efficiency an efficient estimator is constructed for the quadratic covariation or integrated covolatility matrix of a multivariate continuous martingale based on noisy and non synchronous observations under high frequency asymptotics our approach relies on an asymptotically equivalent continuous time observation model where a local generalised method of moments in the spectral domain turns out to be optimal asymptotic semiparametric efficiency is established in the cramer rao sense main findings are that non synchronicity of observation times has no impact on the asymptotics and that major efficiency gains are possible under correlation simulations illustrate the finite sample behaviour

factor garch ito models for high frequency data with application to large volatility matrix prediction abstract several novel large volatility matrix estimation methods have been developed based on the high frequency financial data they often employ the approximate factor model that leads to a low rank plus sparse structure for the integrated volatility matrix and facilitates estimation of large volatility matrices however for predicting future volatility matrices these nonparametric estimators do not have a dynamic structure to implement in this paper we introduce a novel ito diffusion process based on the approximate factor models and call it a factor garch ito model we then investigate its properties and propose a quasi maximum likelihood estimation method for the parameter of the factor garch ito model we also apply it to estimating conditional expected large volatility matrices and establish their asymptotic properties simulation studies are conducted to validate the finite sample performance of the proposed estimation methods the proposed method is also illustrated by using data from the constituents of the s p 500 index and an application to constructing the minimum variance portfolio with gross exposure constraints

positive semidefinite integrated covariance estimation factorizations and asynchronicity an estimator of the ex post covariation of log prices under asynchronicity and microstructure noise is proposed it uses the cholesky factorization of the covariance matrix in order to exploit the heterogeneity in trading intensities to estimate the different parameters sequentially with as many observations as possible the estimator is positive semidefinite by construction we derive asymptotic results and confirm their good finite sample properties by means of a monte carlo simulation in the application we forecast portfolio value at risk and sector risk exposures for a portfolio of 52 stocks we find that the dynamic models utilizing the proposed high frequency estimator provide statistically and economically superior forecasts

quasi maximum likelihood estimation of volatility with high frequency data this paper investigates the properties of the well known maximum likelihood estimator in the presence of stochastic volatility and market microstructure noise by extending the classic asymptotic results of quasi maximum likelihood estimation when trying to estimate the integrated volatility and the variance of noise this parametric approach remains consistent efficient and robust as a quasi estimator under misspecified assumptions moreover it shares the model free feature with nonparametric alternatives for instance realized kernels while being advantageous over them in terms of finite sample performance in light of quadratic representation this estimator behaves like an iterative exponential realized kernel asymptotically comparisons with a variety of implementations of the tukey hanning2 kernel are provided using monte carlo simulations and an empirical study with the euro us dollar future illustrates its application in practice

designing realised kernels to measure the ex post variation of equity prices in the presence of noise this paper shows how to use realised kernels to carry out efficient feasible inference on the expost variation of underlying equity prices in the presence of simple models of market frictions the issue is subtle with only estimators which have symmetric weights delivering consistent estimators with mixed gaussian limit theorems the weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic variance which is close to that of the maximum likelihood estimator in the parametric version of this problem realised kernels can also be selected to i be analysed using endogenously spaced data such as that in databases on transactions ii allow for market frictions which are endogenous iii allow for temporally dependent noise the finite sample performance of our estimators is studied using simulation while empirical work illustrates their use in practice

portfolio optimization with ambiguous correlation and stochastic volatilities in a continuous time economy we investigate the asset allocation problem among a risk free asset and two risky assets with an ambiguous correlation between the two risky assets the portfolio selection that is robust to the uncertain correlation is formulated as the utility maximization problem over the worst case scenario with respect to the possible choice of correlation thus it becomes a maximin problem we solve the problem under the black scholes model for risky assets with an ambiguous correlation using the theory of g brownian motions we then extend the problem to stochastic volatility models for risky assets with an ambiguous correlation between risky asset returns an asymptotic closed form solution is derived for a general class of utility functions including constant relative risk aversion and constant absolute risk aversion utilities when stochastic volatilities are fast mean reverting we propose a practical trading strategy that combines information from the option implied volatilit

estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error this paper studies the estimation problem of the covariance matrices of asset returns in the presence of microstructure noise and asynchronicity between the observations across different assets motivated by malliavin and mancino 2002 2009 we propose a new fourier domain based estimator of multivariate ex post volatility which we call the fourier realized kernel frk an advantage of this approach is that no explicit time alignment is required unlike the time domain based methods widely adopted in the existing literature we derive the large sample properties and establish asymptotic normality of our estimator under some general conditions that allow for both temporal and cross sectional correlations in the measurement error process our results can be viewed as frequency domain extension of the asymptotic theories for the multivariate realized kernel estimator of barndorff nielsen et al 2011 we show in extensive simulations that our method outperforms the time domain estimators when two assets with different liquidity are traded asynchronously

a frequency domain analysis of the error distribution from noisy high frequency data data observed at high sampling frequency are typically assumed to be an additive composite of a relatively slow varying continuous time component a latent stochastic process or a smooth random function and measurement error supposing that the latent component is an it o diffusion process we propose to estimate the measurement error density function by applying a deconvolution technique with appropriate localization our estimator which does not require equally spaced observed times is consistent and minimax rate optimal we also investigate estimators of the moments of the error distribution and their properties propose a frequency domain estimator for the integrated volatility of the underlying stochastic process and show that it achieves the optimal convergence rate simulations and a real data analysis validate our analysis

the helicoidal modeling in computational finite elasticity part i variational formulation the finite elasticity mechanics of continua capable of a polar description is formulated by an alternative modeling to keeping position and orientation as uncoupled fields the rototranslation between two material particles can be described by a single complex tensorial quantity which is recognized to be orthogonal its linearization gives the characteristic curvature and differential vectors underlying the helicoidal modeling in both the sense of the body geometric description and the evolution of a deforming body after due introduction to dual tensors and rototranslations the polar description of the continuum is addressed with particular care to mixed differentiations of the rototranslation field then a thorough variational framework is established for the most general polar continuum under hyperelasticity hypothesis and the three field two field and one field principles are drawn and linearized the proposed modeling is expected to be profitably exploited in non linear finite element analyses of solids undergoing finite displacements rotations and strains

a generalized harish chandra method of descent for reductive lie algebras abstract let g be a connected real reductive group and m a connected reductive subgroup of g with lie algebras g and m respectively we assume that g and m have the same rank we define a map from the space of orbital integrals of m into the space of orbital integrals of g which we call a transfer we then consider the transpose of the transfer this can be viewed as a map from the space of g invariant distributions of g to the space of m invariant distributions of m and can be considered as a restriction map from g to m we prove that this map extends harish chandra method of descent and we obtain a generalization of the radial component theorem we give an application

principal component analysis of high frequency data we develop the necessary methodology to conduct principal component analysis at high frequency we construct estimators of realized eigenvalues eigenvectors and principal components and provide

statistical properties of microstructure noise we study the estimation of joint moments of microstructure noise based on high frequency data the estimation is conducted under a nonparametric setting which allows the underlying price process to have jumps the observation times to be irregularly spaced and the noise to be dependent on the price process and to have diurnal features estimators of arbitrary orders of joint moments are provided for which we establish consistency as well as central limit theorems in particular we provide estimators of autocovariances and autocorrelations of the noise simulation studies demonstrate excellent performance of our estimators in the presence of jumps irregular observation times and even rounding empirical studies reveal moderate positive autocorrelations of microstructure noise for the stocks tested

robust jump regressions abstractwe develop robust inference methods for studying linear dependence between the jumps of discretely observed processes at high frequency unlike classical linear regressions jump regressions are determined by a small number of jumps occurring over a fixed time interval and the rest of the components of the processes around the jump times the latter are the continuous martingale parts of the processes as well as observation noise by sampling more frequently the role of these components which are hidden in the observed price shrinks asymptotically the robustness of our inference procedure is with respect to outliers which are of particular importance in the current setting of relatively small number of jump observations this is achieved by using nonsmooth loss functions like l1 in the estimation unlike classical robust methods the limit of the objective function here remains nonsmooth the proposed method is also robust to measurement error in the observed processes which is achieved by

copula structured m4 processes with application to high frequency financial data abstract statistical applications of classical parametric max stable processes are still sparse mostly due to lack of 1 efficiency of statistical estimation of many parameters in the processes 2 flexibility of concurrently modeling asymptotic independence and asymptotic dependence among variables and 3 capability of fitting real data directly this paper studies a more flexible model i e a class of copula structured m4 multivariate maxima and moving maxima processes and hence csm4 for short csm4 processes are constructed by incorporating sparse random coefficients and structured extreme value copulas in asymptotically in dependent m4 aim4 processes it is shown that the new model overcomes all of the aforementioned three constraints the paper illustrates new features and advantages of the csm4 model using simulated examples and real data of intra daily maxima of high frequency financial time series the paper also studies probabilistic properties of the proposed model and its statistical inference

optimal sparse volatility matrix estimation for high dimensional ito processes with measurement errors stochastic processes are often used to model complex scientiflc problems in flelds ranging from biology and flnance to engineering and physical science this paper investigates rate optimal estimation of the volatility matrix of a high dimensional it o process observed with measurement errors at discrete time points the minimax rate of convergence is established for estimating sparse volatility matrices by combining the multi scale and threshold approaches we construct a volatility matrix estimator to achieve the optimal convergence rate the minimax lower bound is derived by considering a subclass of it o processes for which the minimax lower bound is obtained through a novel equivalent model of covariance matrix estimation for independent but non identically distributed observations and through a delicate construction of the least favorable parameters in addition a simulation study was conducted to test the flnite sample performance of the optimal estimator and the simulation results were found to support the established asymptotic theory

using principal component analysis to estimate a high dimensional factor model with high frequency data this paper constructs an estimator for the number of common factors in a setting where both the sampling frequency and the number of variables increase empirically we document that the covariance matrix of a large portfolio of us equities is well represented by a low rank common structure with sparse residual matrix when employed for out of sample portfolio allocation the proposed estimator largely outperforms the sample covariance estimator

knowing factors or factor loadings or neither evaluating estimators of large covariance matrices with noisy and asynchronous data abstract we investigate estimators of factor model based large covariance and precision matrices using high frequency data which are asynchronous and potentially contaminated by the market microstructure noise our estimation strategies rely on the pre averaging method with refresh time to solve the microstructure problems while using three different specifications of factor models with a variety of thresholding methods respectively to battle the curse of dimensionality to estimate a factor model we either adopt the time series regression tsr to recover loadings if factors are known or use the cross sectional regression csr to recover factors from known loadings or use the principal component analysis pca if neither factors nor their loadings are assumed known we compare the convergence rates in these scenarios using the joint in fill and increasing dimensionality asymptotics to evaluate the empirical trade off between robustness to model misspecification and statistical efficiency among all 30 combinations of estimation strategies we run a horse race on the out of sample portfolio allocation with dow jones 30 s p 100 and s p 500 index constituents respectively and find the pre averaging based strategy using tsr or pca with location thresholding dominates especially over the subsampling based alternatives

an excursion into large rotations the present discourse develops an enlarged exploration of the matrix formulation of finite rotations in space initiated in 1 it is shown how a consistent but subtle matrix calculus inevitably leads to a number of elegant expressions for the transformation or rotation matrix t appertaining to a rotation about an arbitrary axis also analysed is the case of multiple rotations about fixed or follower axes particular attention is paid to an explicit derivation of a single compound rotation vector equivalent to two consecutive arbitrary rotations this theme is discussed in some detail for a number of cases semitangential rotations for which commutativity holds first proposed in 2 3 are also considered furthermore an elementary geometrical analysis of large rotations is also given finally we deduce in an appendix using a judicious reformulation of quarternions the compound pseudovector representing the combined effect of n rotations in the author s opinion the present approach appears preferable to a pure vectorial scheme and even more so to an indicial formulation and is computationally more convenient

a four node plate bending element based on mindlin reissner plate theory and a mixed interpolation this communication discusses a 4 node plate bending element for linear elastic analysis which is obtained as a special case from a general nonlinear continuum mechanics based 4 node shell element formulation the formulation of the plate element is presented and the results of various example solutions are given that yield insight into the predictive capability of the plate and shell element

a quasi maximum likelihood approach for integrated covariance matrix estimation with high frequency data estimating the integrated covariance matrix icm from high frequency financial trading data is crucial to reflect the volatilities and covariations of the underlying trading instruments such an objective is difficult due to contaminated data with microstructure noises asynchronous trading records and increasing data dimensionality in this paper we study a quasi maximum likelihood qml approach for estimating an icm from high frequency financial data we explore a novel multivariate moving average time series device that is convenient for evaluating the estimator both theoretically for its asymptotic properties and numerically for its practical implementations we demonstrate that the qml estimator is consistent to the icm and is asymptotically normally distributed efficiency gain of the qml approach is theoretically quantified and numerically demonstrated via extensive simulation studies an application of the qml approach is illustrated through analyzing a high frequency financial trading data set

high frequency covariance estimates with noisy and asynchronous financial data this article proposes a consistent and efficient estimator of the high frequency covariance quadratic covariation of two arbitrary assets observed asynchronously with market microstructure noise this estimator is built on the marriage of the quasi maximum likelihood estimator of the quadratic variation and the proposed generalized synchronization scheme and thus is not influenced by the epps effect moreover the estimation procedure is free of tuning parameters or bandwidths and is readily implementable monte carlo simulations show the advantage of this estimator by comparing it with a variety of estimators with specific synchronization methods the empirical studies of six foreign exchange future contracts illustrate the time varying correlations of the currencies during the 2008 global financial crisis demonstrating the similarities and differences in their roles as key currencies in the global market

on the choice of finite rotation parameters in this work we discuss some aspects of the three dimensional finite rotations pertinent to the formulation and computational treatment of the geometrically exact structural theories among various possibilities to parameterize the finite rotations special attention is dedicated to a choice featuring an incremental rotation vector some computational aspects pertinent to the implementation of the newton iterative scheme and the newmark time stepping algorithm applied to solving these problems are examined representative numerical simulations are presented in order to illustrate the performance of the proposed formulation

sparse kalman filtering approaches to realized covariance estimation from high frequency financial data estimation of the covariance matrix of asset returns from high frequency data is complicated by asynchronous returns market microstructure noise and jumps one technique for addressing both asynchronous returns and market microstructure is the kalman expectation maximization kem algorithm however the kem approach assumes log normal prices and does not address jumps in the return process which can corrupt estimation of the covariance matrix in this paper we extend the kem algorithm to price models that include jumps we propose a sparse kalman filtering approach to this problem in particular we develop a kalman expectation conditional maximization algorithm to determine the unknown covariance as well as detecting the jumps in order to promote a sparse estimate of the jumps we consider both laplace and the spike and slab jump priors numerical results using simulated data show that each of these approaches provide for improved covariance estimation relative to the kem method in a variety of settings where jumps occur