can one make any crash prediction in finance using the local hurst exponent idea abstract we apply the hurst exponent idea for investigation of djia index time series data the behavior of the local hurst exponent prior to drastic changes in financial series signal is analyzed the optimal length of the time window over which this exponent can be calculated in order to make some meaningful predictions is discussed our prediction hypothesis is verified with examples of 1929 and 1987 crashes as well as with more recent phenomena in stock market from the period 1995 to 2003 some interesting agreements are found

dual h _ infty algorithms for signal processing application to speech enhancement this paper deals with the joint signal and parameter estimation for linear state space models an efficient solution to this problem can be obtained by using a recursive instrumental variable technique based on two dual kalman filters in that case the driving process and the observation noise in the state space representation for each filter must be white with known variances these conditions however are too strong to be always satisfied in real cases to relax them we propose a new approach based on two dual hinfin filters once a new observation of the disturbed signal is available the first hinfin algorithm uses the latest estimated parameters to estimate the signal while the second hinfin algorithm uses the estimated signal to update the parameters in addition as the hinfin filter behavior depends on the choice of various weights we present a way to recursively tune them this approach is then studied in the following cases 1 consistent estimation of the ar parameters from noisy observations and 2 speech enhancement where no a priori model of the additive noise is required for the proposed approach in each case a comparative study with existing methods is carried out to analyze the relevance of our solution

cross correlations between baltic dry index and crude oil prices this paper examines the cross correlation properties of baltic dry index bdi and crude oil prices using cross correlation statistics test and multifractal detrended cross correlation analysis mf dcca the empirical results show that the cross correlations between bdi and crude oil prices are significantly multifractal by introducing the concept of a crossover we find that the cross correlations are strongly persistent in the short term and weakly anti persistent in the long term moreover cross correlations of all kinds of fluctuations are persistent in the short time while cross correlations of small fluctuations are persistent and those of large fluctuations are anti persistent in the long term we have also verified that the multifractality of the cross correlations of bdi and crude oil prices is both attributable to the persistence of fluctuations of time series and fat tailed distributions

multifractal detrended cross correlation analysis for two nonstationary signals we propose a method called multifractal detrended cross correlation analysis to investigate the multifractal behaviors in the power law cross correlations between two time series or higher dimensional quantities recorded simultaneously which can be applied to diverse complex systems such as turbulence finance ecology physiology geophysics and so on the method is validated with cross correlated one and two dimensional binomial measures and multifractal random walks as an example we illustrate the method by analyzing two financial time series

kalman filtering with intermittent observations tail distribution and critical value in this paper we analyze the performance of kalman filtering for discrete time linear gaussian systems where packets containing observations are dropped according to a markov process modeling a gilbert elliot channel to address the challenges incurred by the loss of packets we give a new definition of non degeneracy which is essentially stronger than the classical definition of observability but much weaker than one step observability which is usually used in the study of kalman filtering with intermittent observations we show that the trace of the kalman estimation error covariance under intermittent observations follows a power decay law moreover we are able to compute the exact decay rate for non degenerate systems finally we derive the critical value for non degenerate systems based on the decay rate improving upon the state of the art

stationary filter for linear minimum mean square error estimator of discrete time markovian jump systems we derive in this paper a stationary filter for the linear minimum mean square error estimator lmmse of discrete time markovian jump linear systems mjlss we obtain the convergence of the error covariance matrix of the lmmse to a stationary value under the assumption of mean square stability of the mjls and ergodicity of the associated markov chain it is shown that there exists a unique solution for the stationary riccati filter equation and moreover this solution is the limit of the error covariance matrix of the lmmse the advantage of this scheme is that it is very easy to implement and all calculations can be performed off line leading to a linear time invariant filter

optimal h filtering in networked control systems with multiple packet dropouts abstract this paper studies the problem of h filtering in networked control systems ncss with multiple packet dropouts a new formulation enables us to assign separate dropout rates from the sensors to the controller and from the controller to the actuators by employing the new formulation random dropout rates are transformed into stochastic parameters in the system s representation a generalized h norm for systems with stochastic parameters and both stochastic and deterministic inputs is derived the stochastic h norm of the filtering error is used as a criterion for filter design in the ncs framework a set of linear matrix inequalities lmis is given to solve the corresponding filter design problem a simulation example supports the theory

measuring correlations between non stationary series with dcca coefficient abstract in this short report we investigate the ability of the dcca coefficient to measure correlation level between non stationary series based on a wide monte carlo simulation study we show that the dcca coefficient can estimate the correlation coefficient accurately regardless the strength of non stationarity measured by the fractional differencing parameter d for a comparison we also report the results for the standard pearson correlation coefficient the dcca coefficient dominates the pearson coefficient for non stationary series

multifractal properties of price change and volume change of stock market indices abstract we study auto correlations and cross correlations of daily price changes and daily volume changes of thirteen global stock market indices using multifractal detrended fluctuation analysis mf dfa and multifractal detrended cross correlation analysis mf dxa we find rather distinct multifractal behavior of price and volume changes our results indicate that the time series of price changes are more complex than those of volume changes and that large fluctuations dominate multifractal behavior of price changes while small fluctuations dominate multifractal behavior of volume changes we also find that there is an absence of correlations in price changes there are anti persistent long term correlations in volume changes and there are anti persistent long term cross correlations between price and volume changes shuffling the series reveals that multifractality of both price changes and volume changes arises from a broad probability density function

multifractal detrended fluctuation analysis of nonstationary time series abstract we develop a method for the multifractal characterization of nonstationary time series which is based on a generalization of the detrended fluctuation analysis dfa we relate our multifractal dfa method to the standard partition function based multifractal formalism and prove that both approaches are equivalent for stationary signals with compact support by analyzing several examples we show that the new method can reliably determine the multifractal scaling behavior of time series by comparing the multifractal dfa results for original series with those for shuffled series we can distinguish multifractality due to long range correlations from multifractality due to a broad probability density function we also compare our results with the wavelet transform modulus maxima method and show that the results are equivalent

measuring multifractality of stock price fluctuation using multifractal detrended fluctuation analysis abstract analyzing the shanghai stock price index daily returns using mf dfa method it is found that there are two different types of sources for multifractality in time series namely fat tailed probability distributions and non linear temporal correlations based on that a sliding window of 240 frequency data in 5 trading days was used to study stock price index fluctuation it is found that when the stock price index fluctuates sharply a strong variability is clearly characterized by the generalized hurst exponents h q therefore two measures h and based on generalized hurst exponents were proposed to compare financial risks before and after price limits and reform of non tradable shares the empirical results verify the validity of the measures and this has led to a better understanding of complex stock markets

kalman filtering with intermittent observations convergence for semi markov chains and an intrinsic performance measure this technical note shows that the stationary distribution for the covariance of kalman filtering with intermittent observations exists under mild conditions for a very general class of packet dropping models semi markov chain these results are proved using the geometric properties of riccati recursions with respect to a particular riemannian distance moreover the riemannian mean induced by that distance is always bounded therefore it can be used for characterizing the performance of the system for regimes where the moments of the covariance do not exist other interesting properties of that mean include the symmetry between covariance and information matrices averaging covariances or their inverse gives the same result and its interpretation in information geometry as the natural mean for the manifold of gaussian distributions

mixed h sub 2 h sub spl infin filtering design in multirate transmultiplexer systems lmi approach a mixed h sub 2 h sub spl infin filter design is proposed for multirate transmultiplexer systems with dispersive channel and additive noise first a multirate state space representation is introduced for the transmultiplexer with the consideration of channel dispersion then the problem of signal reconstruction can be regarded as a state estimation problem in order to design an efficient separating filterbank for a transmultiplexer system with uncertain input signal and additive noise the h sub spl infin filter is employed for robust signal reconstruction the h sub 2 filter design is considered to be a suboptimal approach to achieve the optimal signal reconstruction in transmultiplexer system under unitary noise power finally a mixed h sub 2 h sub spl infin filter is proposed to achieve a better signal reconstruction performance in transmultiplexer systems these design problems can be transformed to solving the eigenvalue problems evp under some linear matrix inequality lmi constraint the lmi matlab toolbox can be applied to efficiently solve the evp by convex optimization technique

multiscale multifractal dcca and complexity behaviors of return intervals for potts price model abstract to investigate the characteristics of extreme events in financial markets and the corresponding return intervals among these events we use a potts dynamic system to construct a random financial time series model of the attitudes of market traders we use multiscale multifractal detrended cross correlation analysis mm dcca and lempel ziv complexity lzc perform numerical research of the return intervals for two significant china s stock market indices and for the proposed model the new mm dcca method is based on the hurst surface and provides more interpretable cross correlations of the dynamic mechanism between different return interval series we scale the lzc method with different exponents to illustrate the complexity of return intervals in different scales empirical studies indicate that the proposed return intervals from the potts system and the real stock market indices hold similar statistical properties

sad and stock returns revisited nonlinear analysis based on mf dcca and granger test abstract using both multifractal detrended fluctuation analysis mf dfa and multifractal detrended cross correlation analysis mf dcca methods this paper examines the effects of seasonal affective disorder sad on stock returns in the chinese stock market we first separate stocks into small and large cap stocks according to market value and find that sad and these two stock return series are multifractal based on mf dfa moreover we employ mf dcca and show that the cross correlation between sad and stock returns is anti persistent and the cross correlation between sad and return of small cap stock is stronger than that between sad and return of large cap stock furthermore we find that long range correlation is the main source of multifractality in the cross correlations between sad and small cap stock while both long range correlation and fat tails contribute to the multifractality between sad and large cap stock finally utilizing nonlinear granger causality test we find that sad is the granger cause of stock returns not vice versa

robust h sub spl infin filtering for stochastic time delay systems with missing measurements in this paper the robust hinfin filtering problem is studied for stochastic uncertain discrete time delay systems with missing measurements the missing measurements are described by a binary switching sequence satisfying a conditional probability distribution we aim to design filters such that for all possible missing observations and all admissible parameter uncertainties the filtering error system is exponentially mean square stable and the prescribed hinfin performance constraint is met in terms of certain linear matrix inequalities lmis sufficient conditions for the solvability of the addressed problem are obtained when these lmis are feasible an explicit expression of a desired robust hinfin filter is also given an optimization problem is subsequently formulated by optimizing the hinfin filtering performances finally a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach

kalman filtering with intermittent observations motivated by navigation and tracking applications within sensor networks we consider the problem of performing kalman filtering with intermittent observations when data travel along unreliable communication channels in a large wireless multihop sensor network the effect of communication delays and loss of information in the control loop cannot be neglected we address this problem starting from the discrete kalman filtering formulation and modeling the arrival of the observation as a random process we study the statistical convergence properties of the estimation error covariance showing the existence of a critical value for the arrival rate of the observations beyond which a transition to an unbounded state error covariance occurs we also give upper and lower bounds on this expected state error covariance

price volume multifractal analysis and its application in chinese stock markets abstract an empirical research on chinese stock markets is conducted using statistical tools first the multifractality of stock price return series r i r i ln p t 1 ln p t and trading volume variation series v i v i ln v t 1 ln v t is confirmed using multifractal detrended fluctuation analysis furthermore a multifractal detrended cross correlation analysis between stock price return and trading volume variation in chinese stock markets is also conducted it is shown that the cross relationship between them is also found to be multifractal second the cross correlation between stock price p i and trading volume v i is empirically studied using cross correlation function and detrended cross correlation analysis it is found that both shanghai stock market and shenzhen stock market show pronounced long range cross correlations between stock price and trading volume third a composite index r based on price and trading volume is introduced compared with stock price return series r i and trading volume variation series v i r variation series not only remain the characteristics of original series but also demonstrate the relative correlation between stock price and trading volume finally we analyze the multifractal characteristics of r variation series before and after three financial events in china namely price limits reform of non tradable shares and financial crisis in 2008 in the whole period of sample to study the changes of stock market fluctuation and financial risk it is found that the empirical results verified the validity of r

stability of kalman filtering with markovian packet losses we consider kalman filtering in a network with packet losses and use a two state markov chain to describe the normal operating condition of packet delivery and transmission failure based on the sojourn time of each visit to the failure or successful packet reception state we analyze the behavior of the estimation error covariance matrix and introduce the notion of peak covariance as an estimate of filtering deterioration caused by packet losses which describes the upper envelope of the sequence of error covariance matrices p t t 1 for the case of an unstable scalar model we give sufficient conditions for the stability of the peak covariance process in the general vector case and obtain a sufficient and necessary condition for the scalar case finally the relationship between two different types of stability notions is discussed

filtering and smoothing in an h sup infinity setting the problems of filtering and smoothing are considered for linear systems in an h sup infinity setting i e the plant and measurement noises have bounded energies are in l sub 2 but are otherwise arbitrary two distinct situations for the initial condition of the system are considered the initial condition is assumed known in one case while in the other the initial condition is not known but the initial condition the plant and measurement noise are in some weighted ball of r sup n xl sub 2 finite horizon and infinite horizon cases are considered necessary and sufficient conditions are presented for the existence of estimators both filters and smoothers that achieve a prescribed performance bound and algorithms that result in performance within the bounds are developed in case of smoothers the optimal smoother is also presented the approach uses basic quadratic optimization theory in a time domain setting as a consequence of which both linear time varying and time invariant systems can be considered with equal ease in the smoothing problem for linear time varying systems one considers only the finite horizon case

upscaling from discrete to continuous mathematical models of two interacting populations populations interact in a wide variety of ways through cooperation competition or predation in this work we consider two interacting populations individuals and a stimulus individuals move in response to the stimulus population while the stimulus only diffuses both populations grow while the stimulus population is being depleted by the individuals in order to account for the random nature of the system an individual based model ibm is first developed and then upscaled into a continuous partial differential equation pde model by considering transition probabilities of the individuals at each site finally a set of numerical experiments is presented showing very good agreement between the ibm and the pde model

minimax estimation with intermittent observations this paper considers a problem of minimax or h state estimation with intermittent observations in this setting the disturbance in the dynamical system and the sensor noise are controlled by adversaries and the estimator receives the sensor measurements only sporadically with the availability governed by an independent and identically distributed bernoulli process we cast this problem within the framework of stochastic zero sum dynamic games we first obtain a corresponding stochastic minimax state estimator smse and an associated generalized stochastic riccati equation gsre whose evolutions depend on two parameters one that governs the random measurement arrivals and another one that quantifies the level of h disturbance attenuation we then analyze the asymptotic behavior of the sequence generated by the gsre in the expectation sense and its weak convergence specifically we obtain threshold type conditions above which the sequence generated by the gsre can be bounded both below and above in the expectation sense moreover we show that under some conditions the norm of the sequence generated by the gsre converges weakly to a unique stationary distribution finally we prove that when the disturbance attenuation parameter goes to infinity our asymptotic results are equivalent to the corresponding results from the literature on kalman filtering with intermittent observations we provide simulations to illustrate the results

dynamic relationship between chinese rmb exchange rate index and market anxiety a new perspective based on mf dcca abstract this paper employs multifractal detrended cross correlation analysis mf dcca to study the cross correlations between the chinese rmb exchange rate index and market anxiety using data from june 21 2010 to december 28 2018 cross correlation statistics and coefficients verify the existence of cross correlations and the mf dcca method quantitatively confirms the presence of multifractality between the chinese rmb index and market anxiety for both the long and short term the results of the rolling window analysis reveal that cross correlation scaling exponents of the chinese rmb index and market anxiety are sensitive to external shocks

on kalman filtering for detectable systems with intermittent observations we consider the problem of kalman filtering when observations are available according to a bernoulli process it is known that there exists a critical probability pc such that if measurements are available with probability greater than pc then the expected prediction covariance is bounded for all initial conditions otherwise it is unbounded for some initial conditions we show that when the system observation matrix restricted to the observable subspace is invertible the known lower bound on pc is in fact the exact critical probability this result is based on a novel decomposition of positive semidefinite matrices

cross correlations between price and volume in chinese gold markets abstract we apply the multifractal detrended cross correlation analysis mf dcca method to investigate the cross correlation behaviors between price and volume in chinese gold spot and futures markets qualitatively we find that the price and volume series are significantly cross correlated using the cross correlation test statistics q c c m and the d c c a coefficients quantitatively by employing the mf dcca analysis we find that there is a power law cross correlation and significant multifractal features between price and volume in gold spot and futures markets furthermore by comparing the multifractality of the original series to the shuffled and surrogated series we find that for the gold spot market the main contribution of multifractality is fat tail distribution for the gold futures market both long range correlations and fat tail distributions play important roles in the contribution of multifractality finally by employing the method of rolling windows we undertake further investigation into the time varying features of the cross correlations between price and volume we find that for both spot and futures markets the cross correlations are anti persistent in general in the short term the cross correlation shows obvious fluctuations due to exogenous shocks while in the long term the relationship tends to be at a metastable level due to the dynamic mechanism