Dynamic Hedging Based on Markov Regime-Switching Dynamic Correlation Multivariate Stochastic Volatility Model

It is important to consider the changing states in hedging.The Markov regime-switching dynamic correlation multivariate stochastic volatility( MRS-DC-MSV) model was proposed to solve this issue. DC-MSV model and MRS-DC-MSV model were used to calculate the time-varying hedging ratios and compare the hedging performance. The Markov chain Monte Carlo( MCMC) method was used to estimate the parameters. The results showed that,there were obviously two economic states in Chinese financial market. Two models all did well in hedging,but the performance of MRS-DCMSV model was better. It could reduce risk by nearly 90%. Thus,in the hedging period,changing states is a factor that cannot be neglected.

Quantum correlation dynamics of three non-coupled two-level atoms in different reservoirs

Time evolution dynamics of three non-coupled two-level atoms independently interacting with their reservoirs is solved exactly by considering a damping Lorentzian spectral density.For three atoms initially prepared in Greenberger-Horne-Zeilinger-type state,quantum correlation dynamics in a Markovian reservoir is compared with that in a nonMarkovian reservoir.By increasing detuning quantity in the non-Markovian reservoir,three-atom correlation dynamics measured by negative eigenvalue presents a trapping phenomenon which provides long-time quantum entanglement.Then we compare the correlation dynamics of three atoms with that of two atoms,measured by quantum entanglement and quantum discord for an initial robuster-entangled type state.The result further confirms that quantum discord is indeed different from quantum entanglement in identifying quantum correlation of many bodies.

Fast speedometer identification in dynamic scene based on phase correlation

Speedometer identification has been researched for many years.The common approaches to that problem are usually based on image subtraction,which does not adapt to image offsets caused by camera vibration.To cope with the rapidity,robust and accurate requirements of this kind of work in dynamic scene,a fast speedometer identification algorithm is proposed,it utilizes phase correlation method based on regional entire template translation to estimate the offset between images.In order to effectively reduce unnecessary computation and false detection rate,an improved linear Hough transform method with two optimization strategies is presented for pointer line detection.Based on VC＋＋ 6.0 software platform with OpenCV library,the algorithm performance under experiments has shown that it celerity and precision.

Dynamical correlation functions of the quadratic coupling spin-Boson model

The spin-boson model with quadratic coupling is studied using the bosonic numerical renormalization group method.We focus on the dynamical auto-correlation functions CO（ω）, with the operator taken as σx, σz, and X, respectively. In the weak-coupling regime α 〈 αc, these functions show power law ω-dependence in the small frequency limit, with the powers 1 ＋ 2s, 1 ＋ 2s, and s, respectively. At the critical point α = αc of the boson-unstable quantum phase transition, the critical exponents yO of these correlation functions are obtained as yσx= yσz= 1-2s and yX=-s, respectively. Here s is the bath index and X is the boson displacement operator. Close to the spin flip point, the high frequency peak of Cσx（ω） is broadened significantly and the line shape changes qualitatively, showing enhanced dephasing at the spin flip point.

Non-monotonic temperature evolution of nonlocal structure–dynamics correlation in CuZr glass-forming liquids

The structure–dynamics correlations in a nonlocal manner were investigated in CuZr metallic glass-forming liquids via classical molecular dynamics simulations.A spatial coarse-graining approach was employed to incorporate the nonlocal structural information of given structural order parameters in the structure–dynamics relationship.It is found that the correlation between structure order parameters and dynamics increases with increasing coarse-graining length and has a characteristic length scale.Moreover,the characteristic correlation length exhibits a non-monotonic temperature evolution as temperature approaches glass transition temperature,which is not sensitive to the considered structure order parameters.Our results unveil a striking change in the structure–dynamics correlation,which involves no fitting theoretical interpretation.These findings provide new insight into the structure–dynamics correlation in glass transition.

WRIST FORCE SENSOR'S DYNAMIC PERFORMANCE CALIBRATION BASED ON NEGATIVE STEP RESPONSE

Negative step response experimental method is used in wrist force sensor＇s dynamic performance calibration. The exciting manner of negative step response method is the same as wrist force sensor＇s load in working. This experimental method needn＇t special experiment equipments. Experiment＇s dynamic repeatability is good. So wrist force sensor＇s dynamic performance is suitable to be calibrated by negative step response method. A new correlation wavelet transfer method is studied. By wavelet transfer method, the signal is decomposed into two dimensional spaces of time-frequency. So the problem of negative step exciting energy concentrating in the low frequency band is solved. Correlation wavelet transfer doesn＇t require that wavelet primary function be orthogonal and needn＇t wavelet reconstruction. So analyzing efficiency is high. An experimental bench is designed and manufactured to load the wrist force sensor orthogonal excitation force/moment. A piezoelectric force sensor is used to setup soft trigger and calculate the value of negative step excitation. A wrist force sensor is calibrated. The pulse response function is calculated after negative step excitation and step response have been transformed to positive step excitation and step response. The pulse response function is transferred to frequency response function. The wrist force sensor＇s dynamic characteristics are identified by the frequency response function.

Equilibrium dynamics of the sub-ohmic spin-boson model at finite temperature

We use the full-density matrix numerical renormalization group method to calculate the equilibrium dynamical correlation function C(ω) of the spin operator σ_(z) at finite temperature for the sub-ohmic spin-boson model.A peak is observed at the frequency ω_(T)~T in the curve of C(ω).The curve merges with the zero-temperature C(ω) in ω>>ω_(T) and deviates significantly from the zero-temperature curve in ω<<ω_(T).

Equilibrium dynamics of the sub-Ohmic spin-boson model under bias

Using the bosonic numerical renormalization group method, we studied the equilibrium dynamical correlation function C（ω） of the spin operator σz for the biased sub-Ohmic spin-boson model. The small-ω behavior C（ω） ∝ ω～s is found to be universal and independent of the bias ε and the coupling strength α（except at the quantum critical point α = αc and ε = 0）. Our NRG data also show C（ω） ∝ χ～2ω～s for a wide range of parameters, including the biased strong coupling regime（ε = 0 and α 〉 αc）, supporting the general validity of the Shiba relation. Close to the quantum critical point αc,the dependence of C（ω） on α and ε is understood in terms of the competition between ε and the crossover energy scale ω0^＊of the unbiased case. C（ω） is stable with respect to ε for ε《ε^＊. For ε 》ε^＊, it is suppressed by ε in the low frequency regime. We establish that ε^＊∝（ω0^＊）^1/θ holds for all sub-Ohmic regime 0≤s 〈 1, with θ = 2/（3s） for 0 〈 s≤1/2 and θ = 2/（1 ＋ s） for 1/2 〈 s 〈 1. The variation of C（ω） with α and ε is summarized into a crossover phase diagram on the α–ε plane.

Capital mobility in Latin American and Caribbean countries: new evidence from dynamic common correlated effects panel data modeling

This study investigates the degree of capital mobility in a panel of 16 Latin American and 4 Caribbean countries during 1960 to 2017 against the backdrop of the Feldstein-Horioka hypothesis by applying recent panel data techniques.This is the first study on capital mobility in Latin American and Caribbean countries to employ the recently developed panel data procedure of the dynamic common correlated effects modeling technique of Chudik and Pesaran(J Econ 188:393–420,2015)and the error-correction testing of Gengenbach,Urbain,and Westerlund(Panel error correction testing with global stochastic trends,2008,J Appl Econ 31:982–1004,2016).These approaches address the serious panel data econometric issues of crosssection dependence,slope heterogeneity,nonstationarity,and endogeneity in a multifactor error-structure framework.The empirical findings of this study reveal a low average(mean)savings–retention coefficient for the panel as a whole and for most individual countries,as well as indicating a cointegration relationship between saving and investment ratios.The results indicate that there is a relatively high degree of capital mobility in the Latin American and Caribbean countries in the short run,while the long-run solvency condition is maintained,which is due to reduced frictions in goods and services markets causing increase competition.Increased capital mobility in these countries can promote economic growth and hasten the process of globalization by creating a conducive economic environment for FDI in these countries.

Volatility spillovers,structural breaks and uncertainty in technology sector markets

This study uses the dynamic conditional correlation to investigate how technology subsector stocks interact with financial assets in the face of economic and financial uncertainty.Our results suggest that structural breaks have diverse effects on financial asset connectedness and that the level of bond linkage increases when the trend breaks.We see a growing co-movement between the technology sector and major financial assets when uncertainty is considered.Overall,our findings indicate that the connectedness response varies depending on the type of uncertainty shock.

Plasma Model of Generation and Slip of Linear Defects in Crystalline Materials

In dielectrics and semiconductors, a plasma model of the generation and slip of dislocations is considered, where under shock loads in a generalized space of rectangular pulses an alternating field forms a distribution of pairs of photoelectrons and cations;these electrons with velocities Ve create δ-collisions with cold plasma from free electrons and holes with masses me and mh (mh ≫ me), they emit and absorb longitudinal electron plasma waves whose phase velocities wpw / kpw are close to or are equal to the velocities Ve, while the frequencies wpw and wave numbers kpw of the wave packet of plasma waves are complex, the short-wave components of this wave packet at kpw ⋅ ae ≫ 1 (ae -Debye screening radius) decay in the core linear defect, and its long-wavelength components propagate in the region of the medium surrounding the core of the defect at kpw ⋅ ae <≅ 1. When a defect is generated, the distribution of cations under the influence of the internal Coulomb field shifts to the region of the first peak (protrusion) of the electron plasma wave, thereby forming a vacancy valley. When sliding under the influence of an external electric field, a cationic plasma wave consisting of a vacancy valley and two cationic protrusions moves against the background of an additional potential relief created by an electron plasma wave near the core of the defect. It has been shown that δ-collisions create flows of dynamic large-scale correlations of plasma fluctuations in the form of asymptotics of different-time correlators of density and potential fluctuations as t → +∞.

Beyond-mean-field dynamical correlations for nuclear mass table in deformed relativistic Hartree-Bogoliubov theory in continuum

We extend the deformed relativistic Hartree-Bogoliubov theory in continuum(DRHBc)to go beyondmean-field framework by performing a two-dimensional collective Hamiltonian.The influences of dynamical correlations on the ground-state properties are examined in different mass regions,picking Se,Nd,and Th isotopic chains as representatives.It is found that the dynamical correlation energies(DCEs)and the rotational correction energies E_(rot) in the cranking approximation have an almost equivalent effect on the description of binding energies for most deformed nuclei,and the DCEs can provide a significant improvement for the(near)spherical nuclei close to the neutron shells and thus reduce the rms deviations of S_(2n) by≈17%.Furthermore,it is found that the DCEs are quite sensitive to the pairing correlations;taking ^(148)Nd as an example,a 10%enhancement of pairing strength can raise the DCE by≈37%.

Correlation of quantitative perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and subtypes of breast carcinoma

Objective To investigate whether correlation existsbetween quantitative perfusion parameters obtained from dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and different prognostic factors or immunohistochemical subtypes of breast cancers.Methods A retrospective analysis of DCE-MRI was performed in

Cholesky GAS models for large time-varying covariance matrices

This paper develops a new class of multivariate models for large-dimensional time-varying covariance matrices,called Cholesky generalized autoregressive score(GAS)models,which are based on the Cholesky decomposition of the covariance matrix and assume that the parameters are score-driven.Specifically,two types of score-driven updates are considered:one is closer to the GARCH family,and the other is inspired by the stochastic volatility model.We demonstrate that the models can be estimated equation-wise and are computationally feasible for high-dimensional cases.Moreover,we design an equationwise dynamic model averaging or selection algorithm which simultaneously extracts model and parameter uncertainties,equipped with dynamically estimated model parameters.The simulation results illustrate the superiority of the proposed models.Finally,using a sizeable daily return dataset that includes 124 sectors in the Chinese stock market,two empirical studies with a small sample and a full sample are conducted to verify the advantages of our models.The full sample analysis by a dynamic correlation network documents significant structural changes in the Chinese stock market before and after COVID-19.

Multi-dimensional scenario forecast for generation of multiple wind farms

A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.