morse theory for periodic solutions of hamiltonian systems and the maslov index in this paper we prove morse type inequalities for the contractible 1 periodic solutions of time dependent hamiltonian differential equations on those compact symplectic manifolds m for which the symplectic form and the first chern class of the tangent bundle vanish over q m the proof is based on a version of infinite dimensional morse theory which is due to floer the key point is an index theorem for the fredholm operator which plays a central role in floer homology the index formula involves the maslov index of nondegenerate contractible periodic solutions this maslov index plays the same role as the morse index of a nondegenerate critical point does in finite dimensional morse theory we shall use this connection between floer homology and maslov index to establish the existence of infinitely many periodic solutions having integer periods provided that every i periodic solution has at least one floquet multiplier which is not equal to 1

self dual instantons and holomorphic curves a gradient flow of a morse function on a compact riemannian manifold is said to be of morse smale type if the stable and unstable manifolds of any two critical points intersect transversally for such a morse smale gradient flow there is a chain complex generated by the critical points and graded by the morse index the boundary operator has as its x y entry the number of gradient flow lines running from x to y counted with appropriate signs whenever the difference of the morse indices is 1 the homology of this chain complex agrees with the homology of the underlying manifold m and this can be used to prove the morse inequalities cf 33 26 around 1986 floer generalized this idea to infinite dimensional variational problems in which every critical point has infinite morse index but the moduli spaces of connecting orbits form finite dimensional manifolds for every pair of critical points the dimensions of these spaces give rise to a relative morse index and the boundary operator is defined by counting connecting

fuzzy filter design for ito stochastic systems with application to sensor fault detection the paper deals with the robust fault detection problem for takagi sugeno t s fuzzy ito stochastic systems our aim is to develop a robust fault detection approach to the t s fuzzy systems with brownian motion by using a general observer based fault detection filter as a residual generator the robust fault detection is formulated as a filtering problem attention is focused on the design of both the fuzzy rule independent and the fuzzy rule dependent fault detection filters guaranteeing a prescribed noise attenuation level in an hinfin sense sufficient conditions are proposed to guarantee the mean square asymptotic stability with an hinfin performance for the fault detection system the corresponding solvability conditions for the desired fuzzy rule independent and fuzzy rule dependent fault detection filters are also established finally a numerical example is provided to illustrate the effectiveness of the proposed theory

monte carlo methods for estimating smoothing and filtering one and two factor stochastic volatility models one and two factor stochastic volatility models are assessed over three sets of stock returns data sp the problem is to get the shape of the conditional returns distribution right none of the models come close to matching the tails of this distribution including a second factor provides only a relatively small improvement over the single factor models fitting this aspect of the data is important for option pricing and risk management

bayesian hypothesis testing in latent variable models hypothesis testing using bayes factors bfs is known not to be well defined under the improper prior in the context of latent variable models an additional problem with bfs is that they are difficult to compute in this paper a new bayesian method based on the decision theory and the em algorithm is introduced to test a point hypothesis in latent variable models the new statistic is a by product of the bayesian mcmc output and hence easy to compute it is shown that the new statistic is appropriately defined under improper priors because the method employs a continuous loss function in addition it is easy to interpret the method is illustrated using a one factor asset pricing model and a stochastic volatility model with jumps

bayesian measures of model complexity and fit summary we consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined using an information theoretic argument we derive a measure pd for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest in general pd approximately corresponds to the trace of the product of fisher s information and the posterior covariance which in normal models is the trace of the hat matrix projecting observations onto fitted values its properties in exponential families are explored the posterior mean deviance is suggested as a bayesian measure of fit or adequacy and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages adding pd to the posterior mean deviance gives a deviance information criterion for comparing models which is related to other information criteria and has an approximate decision theoretic justification the procedure is illustrated in some examples and comparisons are drawn with alternative bayesian and classical proposals throughout it is emphasized that the quantities required are trivial to compute in a markov chain monte carlo analysis

radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems this paper presents a means to approximate the dynamic and static equations of stochastic nonlinear systems and to estimate state variables based on radial basis function neural network rbfnn after a nonparametric approximate model of the system is constructed from a priori experiments or simulations a suboptimal filter is designed based on the upper bound error in approximating the original unknown plant with nonlinear state and output equations the procedures for both training and state estimation are described along with discussions on approximation error nonlinear systems with linear output equations are considered as a special case of the general formulation finally applications of the proposed rbfnn to the state estimation of highly nonlinear systems are presented to demonstrate the performance and effectiveness of the method

improved robust h2 and h filtering for uncertain discrete time systems this paper is concerned with the robust h 2 and h filtering problems for linear discrete time systems with polytopic parameter uncertainty we aim to derive a less conservative design than existing linear matrix inequality lmi based sufficient conditions it is shown that a more efficient evaluation of robust h 2 or h performance can be obtained by a matrix inequality condition which contains additional free parameters as compared to existing characterizations when applying this new matrix inequality condition to the robust filter design these parameters provide extra degrees of freedom in optimizing the guaranteed h 2 or h performance and lead to a less conservative design

robust h filtering for discrete nonlinear stochastic systems with time varying delay this is the postprint version of the article the official published version can be accessed from the link below 2007 elsevier inc

supersymmetry and morse theory it is shown that the morse inequalities can be obtained by consideration of a certain supersymmetric quantum mechanics hamiltonian some of the implications of modern ideas in mathematics for supersymmetric theories are discussed

efficient method of moments estimation of a stochastic volatility model a monte carlo study abstract we perform an extensive monte carlo study of efficient method of moments emm estimation of a stochastic volatility model emm uses the expectation under the structural model of the score from an auxiliary model as moment conditions we examine the sensitivity to the choice of auxiliary model using arch garch and egarch models for the score as well as nonparametric extensions emm efficiency approaches that of maximum likelihood for larger sample sizes inference is sensitive to the choice of auxiliary model in small samples but robust in larger samples specification tests and t tests show little size distortion

small sample tests for stock return predictability with possibly non stationary regressors and garch type effects we develop a simulation based procedure to test for stock return predictability with multiple regressors the process governing the regressors is left completely free and the test procedure remains valid in small samples even in the presence of non normalities and garch type effects in the stock returns

brief filtering on nonlinear time delay stochastic systems in this paper we address the filtering problem for a general class of nonlinear time delay stochastic systems the purpose of this problem is to design a full order filter such that the dynamics of the estimation error is guaranteed to be stochastically exponentially ultimately bounded in the mean square both filter analysis and synthesis problems are considered sufficient conditions are proposed for the existence of desired exponential filters which are expressed in terms of the solutions to algebraic riccati inequalities involving scalar parameters the explicit characterization of the desired filters is also derived a simulation example is given to illustrate the design procedures and performances of the proposed method

generalized big picard theorem for pseudo holomorphic maps we show how an appropriate choice for affine connections in the target manifold allows the pseudo holomorphic curves to be realized as harmonic maps as an application we present a generalized big picard theorem for pseudo holomorphic maps between manifolds with almost complex structures

deviance information criterion for latent variable models and misspecified models abstract deviance information criterion dic has been widely used for bayesian model comparison especially after markov chain monte carlo mcmc is used to estimate candidate models this paper first studies the problem of using dic to compare latent variable models when dic is calculated from the conditional likelihood in particular it is shown that the conditional likelihood approach undermines theoretical underpinnings of dic a new version of dic namely dic l is proposed to compare latent variable models the large sample properties of dic l are studied a frequentist justification of dic l is provided like aic dic l provides an asymptotically unbiased estimator to the expected kullback leibler kl divergence between the dgp and a predictive distribution some popular algorithms such as the em kalman and particle filtering algorithms are introduced to compute dic l for latent variable models moreover this paper studies the problem of using dic to compare misspecified models a new version of dic namely dic m is proposed and it can be regarded as a bayesian version of tic a frequentist justification of dic m is provided under misspecification dic l and dic m are illustrated using asset pricing models and stochastic volatility models

stationary holomorphic discs and finite jet determination problems we construct a family of small analytic discs attached to levi non degenerate hypersurfaces in mathbb c n 1 which is globally biholomorphically invariant we then apply this technique to study unique determination problems along levi non degenerate hypersurfaces that are merely of class mathcal c 4 this method gives 2 jet determination results for germs of biholomorphisms cr diffeomorphisms as well as in the almost complex setting

the structure of dynamic correlations in multivariate stochastic volatility models this paper proposes two types of stochastic correlation structures for multivariate stochastic volatility msv models namely the constant correlation cc msv and dynamic correlation dc msv models from which the stochastic covariance structures can easily be obtained both structures can be used for purposes of determining optimal portfolio and risk management strategies through the use of correlation matrices and for calculating value at risk var forecasts and optimal capital charges under the basel accord through the use of covariance matrices a technique is developed to estimate the dc msv model using the markov chain monte carlo mcmc procedure and simulated data show that the estimation method works well various multivariate conditional volatility and msv models are compared via simulation including an evaluation of alternative var estimators the dc msv model is also estimated using three sets of empirical data namely nikkei 225 index hang seng index and straits times index returns and significant dynamic correlations are found the dynamic conditional correlation dcc model is also estimated and is found to be far less sensitive to the covariation in the shocks to the indexes the correlation process for the dcc model also appears to have a unit root and hence constant conditional correlations in the long run in contrast the estimates arising from the dc msv model indicate that the dynamic correlation process is stationary

morse theory for lagrangian intersections soit p une variete symplectique compacte et soit l p une sous variete lagrangienne avec 2 p l 0 pour un diffeomorphisme exact de p avec la propriete que l coupe l transversalement on demontre une inegalite de morse reliant l ensemble l l a la cohomologie de l

robust h filtering for a class of discrete time nonlinear systems abstract in this paper a robust h filtering problem is considered for a class of discrete time nonlinear systems based on the lyapunov method a design criterion of the robust h filter guarantee not only the stability but also the prespecified upper bound of h norm from the disturbance inputs to error state outputs the design condition can be solved easily by efficient convex optimization algorithms numerical examples show the effectiveness of the proposed method

the birkhoff lewis fixed point theorem and a conjecture of v i arnold abstract the following conjecture of v i arnold is proved every measure preserving diffeomorphism of the torus t2 which is homologeous to the identity and which leaves the center of mass invariant possesses at least 3 fixed points the proof of this global fixed point theorem does not make use of the generating function technique the theorem is a consequence of the statement that a hamiltonian vector field on a torus t2n which depends periodically on time possesses at least 2n 1 forced oscillations these periodic solutions are are found using the classical variational principle by means of two qualitative statements for general flows a second conjecture of v i arnold proved concerns a birkhoff lewis type fixed point theorem for symplectic maps additional keywords periodic functions variational principles equations reprints author

stochastic input to state stability and h filtering for a class of stochastic nonlinear systems in this paper an h filtering problem is considered for a class of stochastic nonlinear systems with time delay and exogenous disturbances sufficient conditions for stochastic input to state stability siss of the stochastic nonlinear systems are developed via lyapunov function method and linear matrix inequalities lmis technique the purpose of this work is to design a nonlinear filter to ensure both the siss and a prescribed h attenuation level for the corresponding filtering error system by using the siss results a set of sufficient conditions for the existence of h filter is given in the form of lmi when the lmi is feasible an explicit expression of a desired filter is presented finally some numerical and practical examples are employed to demonstrate the effectiveness of the proposed approaches

technical communique absolute stability of time delay systems with sector bounded nonlinearity this paper deals with the problem of absolute stability of time delay systems with sector bounded nonlinearity some new delay dependent stability criteria are obtained and formulated in the form of linear matrix inequalities lmis neither model transformation nor bounding technique for cross terms is involved through derivation of the stability criteria numerical examples show that the results obtained in this paper improve the estimate of the stability limit over some existing result

on the arnold conjecture for weakly monotone symplectic manifolds we show the arnold conjecture concerning symplectic fixed points in the case that the symplectic manifold is weakly monotone and all the fixed points are non degenerate in particular the conjecture is true in dimension 2 4 6 if all the fixed points are non degenerate

witten s complex and infinite dimensional morse theory we investigate the relation between the trajectories of a finite dimensional gradient flow connecting two critical points and the cohomology of the surrounding space the results are applied to an infinite dimensional problem involving the symplect c action function

stochastic volatility with leverage fast and efficient likelihood inference this paper is concerned with the bayesian analysis of stochastic volatility sv models with leverage specifically the paper shows how the often used kim et al 1998 stochastic volatility likelihood inference and comparison with arch models review of economic studies 65 361 393 method that was developed for sv models without leverage can be extended to models with leverage the approach relies on the novel idea of approximating the joint distribution of the outcome and volatility innovations by a suitably constructed ten component mixture of bivariate normal distributions the resulting posterior distribution is summarized by mcmc methods and the small approximation error in working with the mixture approximation is corrected by a reweighting procedure the overall procedure is fast and highly efficient we illustrate the ideas on daily returns of the tokyo stock price index finally extensions of the method are described for superposition models where the log volatility is made up of a linear combination of heterogenous and independent autoregressions and heavy tailed error distributions student and log normal

markov chain monte carlo methods for stochastic volatility models this paper is concerned with simulation based inference in generalized models of stochastic volatility defined by heavy tailed student t distributions with unknown degrees of freedom and exogenous variables in the observation and volatility equations and a jump component in the observation equation by building on the work of kim shephard and chib rev econom stud 65 1998 361 we develop efficient markov chain monte carlo algorithms for estimating these models the paper also discusses how the likelihood function of these models can be computed by appropriate particle filter methods computation of the marginal likelihood by the method of chib j amer statist assoc 90 1995 1313 is also considered the methodology is extensively tested and validated on simulated data and then applied in detail to daily returns data on the s p 500 index where several stochastic volatility models are formally compared under different priors on the parameters

an alternative form used to calibrate the heston option pricing model this paper presents an alternative form of the heston model that preserves an essential advantage of the heston model its analytic tractability by imposing the necessary and sufficient conditions for the existence of a solution in affine form while it is in a different form so that it offers certain advantages in parameter determination to demonstrate this we conducted some empirical studies exploring if this new form does have certain advantages over the original version under certain market conditions

simulation run length control in the presence of an initial transient this paper studies the estimation of the steady state mean of an output sequence from a discrete event simulation it considers the problem of the automatic generation of a confidence interval of prespecified width when there is an initial transient present it explores a procedure based on schruben s brownian bridge model for the detection of nonstationarity and a spectral method for estimating the variance of the sample mean the procedure is evaluated empirically for a variety of output sequences the performance measures considered are bias confidence interval coverage mean confidence interval width mean run length and mean amount of deleted data if the output sequence contains a strong transient then inclusion of a test for stationarity in the run length control procedure results in point estimates with lower bias narrower confidence intervals and shorter run lengths than when no check for stationarity is performed if the output sequence contains no initial transient then the performance measures of the procedure with a stationarity test are only slightly degraded from those of the procedure without such a test if the run length is short relative to the extent of the initial transient the stationarity tests may not be powerful enough to detect the transient resulting in a procedure with unreliable point and interval estimates

transversality in elliptic morse theory for the symplectic action our goal in this paper is to settle some transversality question for the perturbed nonlinear cauchy riemann equations on the cylinder these results play a central role in the denition of symplectic floer homology and hence in the proof of the arnold conjecture there is currently no other reference to these transversality results in the open literature our approach does not require aronszajn s theorem instead we derive the unique continuation theorem from a generalization of the carleman similarity principle

a semiparametric stochastic volatility model abstract in this paper the correlation structure in the classical leverage stochastic volatility sv model is generalized based on a linear spline in the new model the correlation between the return and volatility innovations is time varying and depends nonparametrically on the type of news arrived to the market theoretical properties of the proposed model are examined the model estimation and comparison are conducted by bayesian methods the performance of the estimates are examined in simulations the new model is fitted to daily and weekly us data and compared with the classical sv and garch models in terms of their in sample and out of sample performances empirical results suggest evidence in favor of the proposed model in particular the new model finds strong evidence of time varying leverage effect in individual stocks when the classical model fails to identify the leverage effect

robust energy to peak filter design for stochastic time delay systems abstract this paper considers the robust energy to peak filtering problem for uncertain stochastic time delay systems the stochastic uncertainties appear in both the dynamic and the measurement equations and the state delay is assumed to be time varying attention is focused on the design of full order and reduced order filters guaranteeing a prescribed energy to peak performance for the filtering error system sufficient conditions are formulated in terms of linear matrix inequalities lmis and the corresponding filter design is cast into a convex optimization problem which can be efficiently handled by using standard numerical algorithms in addition the results obtained are further extended to more general cases where the system matrices also contain uncertain parameters the most frequently used ways of dealing with parameter uncertainties including polytopic and norm bounded characterizations have been taken into consideration with convex optimization problems obtained for the design of desired robust energy to peak filters

a state space approach to analysis of almost periodic nonlinear systems with sector nonlinearities the nonlinear system considered in this paper consists of two components a linear system and a time varying nonlinear element in the feedback connection and is almost periodic it is shown using a state space approach or lyapunov approach that if the well known circle criterion is satisfied then there exists an almost periodic solution which is uniformly asymptotically stable in the large furthermore it is also shown that the spectra of the almost periodic solution can be characterized by those of the input and the time varying nonlinearity

robust kalman filtering for uncertain discrete time systems this paper is concerned with the problem of a kalman filter design for uncertain discrete time systems the system under consideration is subjected to time varying norm bounded parameter uncertainty in both the state and output matrices the problem addressed is the design of a linear filter such that the variance of the filtering error is guaranteed to be within a certain bound for all admissible uncertainties furthermore the guaranteed cost can be optimized by appropriately searching a scaling design parameter

approximate bayesian inference with the weighted likelihood bootstrap we introduce the weighted likelihood bootstrap wlb as a way to simulate approximately from a posterior distribution this method is often easy to implement requiring only an algorithm for calculating the maximum likelihood estimator such as iteratively reweighted least squares in the generic weighting scheme the wlb is first order correct under quite general conditions inaccuracies can be removed by using the wlb as a source of samples in the sampling importance resampling sir algorithm which also allows incorporation of particular prior information the sir adjusted wlb can be a competitive alternative to other integration methods in certain models asymptotic expansions elucidate the second order properties of the wlb which is a generalization of rubin s bayesian bootstrap the calculation of approximate bayes factors for model comparison is also considered we note that given a sample simulated from the posterior distribution the required marginal likelihood may be simulation consistently estimated by the harmonic mean of the associated likelihood values a modification of this estimator that avoids instability is also noted these methods provide simple ways of calculating approximate bayes factors and posterior model probabilities for a very wide class of models

maximum likelihood estimation of partially observed diffusion models this paper develops a maximum likelihood ml method to estimate partially observed diffusion models based on data sampled at discrete times the method combines two techniques recently proposed in the literature in two separate steps in the first step the closed form approach of ait sahalia 2008 is used to obtain a highly accurate approximation to the joint transition probability density of the latent and the observed states in the second step the efficient importance sampling technique of richard and zhang 2007 is used to integrate out the latent states thereby yielding the likelihood function using both simulated and real data we show that the proposed ml method works better than alternative methods the new method does not require the underlying diffusion to have an affine structure and does not involve infill simulations therefore the method has a wide range of applicability and its computational cost is moderate

a max plus based algorithm for a hamilton jacobi bellman equation of nonlinear filtering the hamilton jacobi bellman hjb equation associated with the robust hinfty filter as well as the mortensen filter is considered these filters employ a model where the disturbances have finite power the hjb equation for the filter information state is a first order equation with a term that is quadratic in the gradient yet the solution operator is linear in the max plus algebra this property is exploited by the development of a numerical algorithm where the effect of the solution operator on a set of basis functions is computed off line the precomputed solutions are stored as vectors of coefficients of the basis functions these coefficients are then used directly in the real time computations

estimating a semiparametric asymmetric stochastic volatility model with a dirichlet process mixture in this paper we extend the parametric asymmetric stochastic volatility model asv where returns are correlated with volatility by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically its novelty is in modeling the joint conditional return volatility distribution with an infinite mixture of bivariate normal distributions with mean zero vectors but having unknown mixture weights and covariance matrices this semiparametric asv model nests stochastic volatility models whose innovations are distributed as either normal or student t distributions plus the response in volatility to unexpected return shocks is more general than the fixed asymmetric response with the asv model the unknown mixture parameters are modeled with a dirichlet process prior this prior ensures a parsimonious finite posterior mixture that best represents the distribution of the innovations and a straightforward sampler of the conditional posteriors we develop a bayesian markov chain monte carlo sampler to fully characterize the parametric and distributional uncertainty nested model comparisons and out of sample predictions with the cumulative marginal likelihoods and one day ahead predictive log bayes factors between the semiparametric and parametric versions of the asv model shows the semiparametric model projecting more accurate empirical market returns a major reason is how volatility responds to an unexpected market movement when the market is tranquil expected volatility reacts to a negative positive price shock by rising initially declining but then rising when the positive shock is large however when the market is volatile the degree of asymmetry and the size of the response in expected volatility is muted in other words when times are good no news is good news but when times are bad neither good nor bad news matters with regards to volatility

brief paper a delay decomposition approach to l2 l filter design for stochastic systems with time varying delay this paper investigates the problem of l 2 l filter design for a class of stochastic systems with time varying delay the addressed problem is the design of a full order linear filter such that the error system is asymptotically mean square stable and a prescribed l 2 l performance is satisfied in order to develop a less conservative filter design a new lyapunov krasovskii functional lkf is constructed by decomposing the delay interval into multiple equidistant subintervals and a new integral inequality is established in the stochastic setting then based on the lkf and integral inequality the delay dependent conditions for the existence of l 2 l filters are obtained in terms of linear matrix inequalities lmis the resulting filters can ensure that the error system is asymptotically mean square stable and the peak value of the estimation error is bounded by a prescribed level for all possible bounded energy disturbances finally two examples are given to illustrate the effectiveness of the proposed method

robust h filters for markovian jump linear systems under sampled measurements abstract in this paper a new class of markovian jump linear system model with polytopic parameter uncertainty continuous disturbance and discrete disturbance is introduced the transition rate matrix of the markov process and the parameters of the system are either exactly known or unknown but belong to a given polytope the general criteria for the stochastic stability of this model and the method for designing a robust stable linear time invariant h filter under sampled measurements are derived in terms of certain linear matrix inequalities lmis finally a numerical example is given to show that the method is effective and feasible

sparse bayesian time varying covariance estimation in many dimensions dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality this renders parsimonious estimation methods essential for conducting reliable statistical inference in this paper the issue is addressed by modeling the underlying co volatility dynamics of a time series vector through a lower dimensional collection of latent time varying stochastic factors furthermore we apply a normal gamma prior to the elements of the factor loadings matrix this hierarchical shrinkage prior effectively pulls the factor loadings of unimportant factors towards zero thereby increasing parsimony even more we apply the model to simulated data as well as daily log returns of 300 sp it is implemented in the r package factorstochvol author s abstract

robust stability and controllability of stochastic differential delay equations with markovian switching in this paper we investigate the almost surely asymptotic stability for the nonlinear stochastic differential delay equations with markovian switching some sufficient criteria on the controllability and robust stability are also established for linear stochastic differential delay equations with markovian switching

h filtering for networked systems with partly known distribution transmission delays this paper studies the h filtering problem for a class of discrete time networked systems with partly known distribution transmission delays it is assumed that the random sensor filter delay satisfies markovian characteristics but the transition probabilities of the delay transferring from one value to others are partly known a novel measurement model is proposed to reflect the random delay of the transmission modes and the resulting filtering error system is modeled as a markovian jump system mjs with multiple modes and state delay sufficient conditions are derived for the filtering error system to be stochastically stable and ensure a prescribed h disturbance attenuation level bound by using mode dependent lyapunov functional approach based on the derived conditions a design procedure for the h filter is also presented a numerical example is provided to show the design process of the filter and the effectiveness of the proposed method

robust h exponential filtering for uncertain stochastic time delay systems with markovian switching and nonlinearities in this paper we deal with the robust h exponential filtering problem for a class of uncertain stochastic time delay systems with markovian switching and nonlinear disturbances the system under consideration involves parameter uncertainties ito type stochastic disturbances markovian jump parameters unknown nonlinear disturbances as well as time varying delays which vary in a range and dependent on the mode of the operation the aim of this problem is to design a markovian jump exponential filter such that the filtering error system is robustly stochastically exponentially mean square stable and a prescribed h disturbance attenuation level is guaranteed novel delay range dependent sufficient conditions are proposed to guarantee the existence of the desired robust exponential filter which are derived in terms of linear matrix inequalities lmis then the explicit expression of the desired filter parameters is characterized an illustrative numerical example is provided to demonstrate the effectiveness of the proposed method

robust h sub spl infin filtering for nonlinear stochastic systems this paper describes the robust h sub spl infin filtering analysis and synthesis of nonlinear stochastic systems with state and exogenous disturbance dependent noise we assume that the state and measurement are corrupted by stochastic uncertain exogenous disturbance and that the system dynamic is modeled by ito spl circ type stochastic differential equations for general nonlinear stochastic systems the h sub spl infin filter can be obtained by solving second order nonlinear hamilton jacobi inequalities when the worst case disturbance is considered in the design procedure a mixed h sub 2 h sub spl infin filtering problem is also solved by minimizing the total estimation error energy it is found that for a class of special nonlinear stochastic systems the h sub spl infin filtering design can be given via solving several linear matrix inequalities instead of hamilton jacobi inequalities a few examples show that the proposed methods are effective

a bayesian robust chi squared test for testing simple hypotheses abstract in this paper we introduce a new bayesian chi squared test based on an adjusted quadratic loss function for testing a simple null hypothesis we show that the asymptotic null distribution of our suggested test is a central chi squared distribution under some assumptions required for the bayesian large sample theory we refer to our test as the bayesian robust chi squared test since it is robust to parametric misspecification in the alternative model that is the limiting null distribution of our test is a central chi squared distribution irrespective of parametric misspecification in the alternative model in addition to being robust to parametric misspecification our test also shares properties of the test suggested by li et al 2015 based on a quadratic loss function we provide four examples to illustrate the implementation of our suggested bayesian test statistic

reduced order h sub spl infin filtering for stochastic systems this paper deals with the reduced order h sub spl infin filtering problem for stochastic systems necessary and sufficient conditions are obtained for the existence of solutions to the continuous time and discrete time problems in terms of certain linear matrix inequalities lmis and a coupling nonconvex rank constraint condition furthermore when these conditions are feasible an explicit parametrization of all desired reduced order filters corresponding to a feasible solution is given in particular when the reduced order filter is restricted to be a static one then simple conditions expressed by lmis only without any rank constraints are derived and a parametrization of all solutions is also given finally an illustrative example is provided to show the effectiveness of the proposed approach

marginal likelihood from the gibbs output abstract in the context of bayes estimation via gibbs sampling with or without data augmentation a simple approach is developed for computing the marginal density of the sample data marginal likelihood given parameter draws from the posterior distribution consequently bayes factors for model comparisons can be routinely computed as a by product of the simulation hitherto this calculation has proved extremely challenging our approach exploits the fact that the marginal density can be expressed as the prior times the likelihood function over the posterior density this simple identity holds for any parameter value an estimate of the posterior density is shown to be available if all complete conditional densities used in the gibbs sampler have closed form expressions to improve accuracy the posterior density is estimated at a high density point and the numerical standard error of resulting estimate is derived the ideas are applied to probit regression and finite mixture models

time varying leverage effects abstract vast empirical evidence points to the existence of a negative correlation named leverage effect between shocks to variance and shocks to returns we provide a nonparametric theory of leverage estimation in the context of a continuous time stochastic volatility model with jumps in returns jumps in variance or both leverage is defined as a flexible function of the state of the firm as summarized by the spot variance level we show that its point wise functional estimates have asymptotic properties in terms of rates of convergence limiting biases and limiting variances which crucially depend on the likelihood of the individual jumps and co jumps as well as on the features of the jump size distributions empirically we find economically important time variation in leverage with more negative values associated with higher variance levels

filtering via simulation auxiliary particle filters this article analyses the recently suggested particle approach to filtering time series we suggest that the algorithm is not robust to outliers for two reasons the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution here we tackle the first of these problems

induced l sub 2 and generalized h sub 2 filtering for systems with repeated scalar nonlinearities this paper provides complete results on the filtering problem for a class of nonlinear systems described by a discrete time state equation containing a repeated scalar nonlinearity as in recurrent neural networks both induced l sub 2 and generalized h sub 2 indexes are introduced to evaluate the filtering performance for a given stable discrete time systems with repeated scalar nonlinearities our purpose is to design a stable full order or reduced order filter with the same repeated scalar nonlinearities such that the filtering error system is asymptotically stable and has a guaranteed induced l sub 2 or generalized h sub 2 performance sufficient conditions are obtained for the existence of admissible filters since these conditions involve matrix equalities the cone complementarity linearization procedure is employed to cast the nonconvex feasibility problem into a sequential minimization problem subject to linear matrix inequalities which can be readily solved by using standard numerical software if these conditions are feasible a desired filter can be easily constructed these filtering results are further extended to discrete time systems with both state delay and repeated scalar nonlinearities the techniques used in this paper are very different from those used for previous controller synthesis problems which enable us to circumvent the difficulty of dilating a positive diagonally dominant matrix a numerical example is provided to show the applicability of the proposed theories

a new approach to bayesian hypothesis testing abstract in this paper a new bayesian approach is proposed to test a point null hypothesis based on the deviance in a decision theoretical framework the proposed test statistic may be regarded as the bayesian version of the likelihood ratio test and appeals in practical applications with three desirable properties first it is immune to jeffreys concern about the use of improper priors second it avoids jeffreys lindley s paradox third it is easy to compute and its threshold value is easily derived facilitating the implementation in practice the method is illustrated using some real examples in economics and finance it is found that the leverage effect is insignificant in an exchange time series and that the fama french three factor model is rejected

symplectic fixed points and holomorphic spheres letp be a symplectic manifold whose symplectic form integrated over the spheres inp is proportional to its first chern class on the loop space ofp we consider the variational theory of the symplectic action function perturbed by a hamiltonian term in particular we associate to each isolated invariant set of its gradient flow an abelian group with a cyclic grading it is shown to have properties similar to the homology of the conley index in locally compact spaces as an application we show that if the fixed point set of an exact diffeomorphism onp is nondegenerate then it satisfies the morse inequalities onp we also discuss fixed point estimates for general exact diffeomorphisms

a multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries in this paper we propose a flexible model to describe nonlinearities and long range dependence in time series dynamics the new model is a multiple regime smooth transition extension of the heterogeneous autoregressive har model which is specifically designed to model the behavior of the volatility inherent in financial time series the model is able to simultaneously approximate long memory behavior as well as describe sign and size asymmetries a sequence of tests is developed to determine the number of regimes and an estimation and testing procedure is presented monte carlo simulations evaluate the finite sample properties of the proposed tests and estimation procedures we apply the model to several dow jones industrial average index stocks using transaction level data from the trades and quotes database that covers ten years of data we find strong support for long memory and both sign and size asymmetries furthermore the new model when combined with the linear har model is viable and flexible for purposes of forecasting volatility

on leverage in a stochastic volatility model this paper is concerned with the specification for modelling financial leverage effect in the context of stochastic volatility sv models two alternative specifications co exist in the literature one is the euler approximation to the well known continuous time sv model with leverage effect and the other is the discrete time sv model of jacquier et al j econometrics 122 2004 185 using a gaussian nonlinear state space form with uncorrelated measurement and transition errors i show that it is easy to interpret the leverage effect in the conventional model whereas it is not clear how to obtain and interpret the leverage effect in the model of jacquier et al empirical comparisons of these two models via bayesian markov chain monte carlo mcmc methods further reveal that the specification of jacquier et al is inferior simulation experiments are conducted to study the sampling properties of bayes mcmc for the conventional model

robust h filter design for neutral stochastic uncertain systems with time varying delay abstract in this paper the robust h filtering problem for a class of neutral stochastic systems is discussed the system under consideration contains parameter uncertainties ito type stochastic disturbances time varying delay the parameter uncertainties are assumed to be time varying norm bounded using the stochastic lyapunov stability theory and ito s differential rule a full order filter is designed for all admissible uncertainties and time varying delay which is expressed in the form of linear matrix inequality lmi the dynamics of the filtering error systems are guaranteed to be robust stochastically mean square asymptotically stable while achieving a prescribed stochastic robust h performance level at the end of this paper a numerical example is given to demonstrate the usefulness of the proposed method

moment stability of nonlinear discrete stochastic systems with time delays based on h representation technique in this paper mean square stability of nonlinear discrete stochastic systems with time delays is investigated we obtain the second order moment equations of discrete stochastic systems by virtue of the h representation technique by the comparison principle and the established moment equations some moment stability theorems for nonlinear discrete stochastic systems with time delays are established a numerical example is presented to illustrate the efficiency of our results at the end of the paper

monte carlo estimation for nonlinear non gaussian state space models we develop a proposal or importance density for state space models with a nonlinear non gaussian observation vector y p y and an unobserved linear gaussian signal vector p the proposal density is obtained from the laplace approximation of the smoothing density p y we present efficient algorithms to calculate the mode of p y and to sample from the proposal density the samples can be used for importance sampling and markov chain monte carlo methods the new results allow the application of these methods to state space models where the observation density p y is not log concave additional results are presented that lead to computationally efficient implementations we illustrate the methods for the stochastic volatility model with leverage

variance constrained filtering for uncertain stochastic systems with missing measurements in this note we consider a new filtering problem for linear uncertain discrete time stochastic systems with missing measurements the parameter uncertainties are allowed to be norm bounded and enter into the state matrix the system measurements may be unavailable i e missing data at any sample time and the probability of the occurrence of missing data is assumed to be known the purpose of this problem is to design a linear filter such that for all admissible parameter uncertainties and all possible incomplete observations the error state of the filtering process is mean square bounded and the steady state variance of the estimation error of each state is not more than the individual prescribed upper bound it is shown that the addressed filtering problem can effectively be solved in terms of the solutions of a couple of algebraic riccati like inequalities or linear matrix inequalities the explicit expression of the desired robust filters is parameterized and an illustrative numerical example is provided to demonstrate the usefulness and flexibility of the proposed design approach

bayesian estimation of an extended local scale stochastic volatility model a new version of the local scale model of shephard 1994 is presented its features are identically distributed evolution equation disturbances the incorporation of in the mean effects and the incorporation of variance regressors a bayesian posterior simulator and a new simulation smoother are presented the model is applied to publicly available daily exchange rate and asset return series and is compared with t garch and lognormal stochastic volatility formulations using bayes factors

robust h sub spl infin filtering of stationary continuous time linear systems with stochastic uncertainties the problem of applying h sub spl infin filters on stationary continuous time linear systems with stochastic uncertainties in the state space signal model is addressed these uncertainties are modeled via white noise processes the relevant cost function is the expected value of the standard h sub spl infin performance index with respect to the uncertain parameters the solution is obtained via a stochastic bounded real lemma that results in a modified riccati inequality this inequality is expressed in the form of a linear matrix inequality whose solution provides the filter parameters the method proposed is also applied to the case where in addition to the stochastic uncertainty other deterministic parameters of the system are not perfectly known and are assumed to lie in a given polytope the problem of mixed h sub 2 h sub spl infin filtering for the above system is also treated the theory developed is demonstrated by a practical example

robust filtering design of piecewise discrete time linear systems this work presents two filter design methods for piecewise discrete time linear systems based on a piecewise lyapunov function it is shown that the resulting filtering error system is globally stable with guaranteed h sub spl infin or generalized h sub 2 performance and the filter gains can be obtained by solving a set of linear matrix inequalities that is numerically feasible with commercially available software two simulation examples are also given to illustrate the performance of the proposed approaches

robust filtering for a class of stochastic uncertain nonlinear time delay systems via exponential state estimation we investigate the robust filter design problem for a class of nonlinear time delay stochastic systems the system under study involves stochastics unknown state time delay parameter uncertainties and unknown nonlinear disturbances which are all often encountered in practice and the sources of instability the aim of this problem is to design a linear delayless uncertainty independent state estimator such that for all admissible uncertainties as well as nonlinear disturbances the dynamics of the estimation error is stochastically exponentially stable in the mean square independent of the time delay sufficient conditions are proposed to guarantee the existence of desired robust exponential filters which are derived in terms of the solutions to algebraic riccati inequalities the developed theory is illustrated by numerical simulation

bayesian analysis of stochastic volatility models with fat tails and correlated errors abstract the basic univariate stochastic volatility model specifies that conditional volatility follows a log normal auto regressive model with innovations assumed to be independent of the innovations in the conditional mean equation since the introduction of practical methods for inference in the basic volatility model j business econom statist 12 4 371 it has been observed that the basic model is too restrictive for many financial series we extend the basic svol to allow for a so called leverage effect via correlation between the volatility and mean innovations and for fat tails in the mean equation innovation a bayesian markov chain monte carlo algorithm is developed for the extended volatility model thus far likelihood based inference for the correlated svol model has not appeared in the literature we develop bayes factors to assess the importance of the leverage and fat tail extensions sampling experiments reveal little loss in precision from adding the model extensions but a large loss from using the basic model in the presence of mis specification there is overwhelming evidence of a leverage effect for weekly and daily equity indices the evidence in favor of fat tails is very strong for daily exchange rate and equity indices but less so for weekly data we also find that volatility estimates from the extended model are markedly different from those produced by the basic svol

brief nonlinear h filtering of sampled data systems this paper considers the problem of h filtering for nonlinear sampled data systems sufficient conditions are obtained for the existence of filters that guarantee the l 2 gain from an exogenous input to an estimation error is less than or equal to a prescribed value these conditions are expressed in terms of both continuous and discrete hamilton jacobi equations