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.

Spatial relation between fluctuating wall pressure and near-wall streamwise vortices in wall bounded turbulent flow

A new view of the spatial relation between fluctuating wall pressure and near-wall streamwise vortices （NWSV） is proposed for wall bounded turbulent flow by use of the direct numerical simulation （DNS） database. The results show that the wall region with low pressure forms just below the strong NWSV, which is mostly associated with the overhead NWSV. The wall region with high pressure forms downstream of the NWSV, which has a good correspondence with the downwash of the fluids induced by the upstream NWSV. The results provide a significant basis for the detection of NWSV.

3D prestack reverse time migration of ground penetrating radar data based on the normalized correlation imaging condition

The reverse time migration(RTM)of ground penetrating radar(GPR)is usually implemented in its two-dimensional(2D)form,due to huge computational cost.However,2D RTM algorithm is difficult to focus the scattering signal and produce a high precision subsurface image when the object is buried in a complicated subsurface environment.To better handle the multi-off set GPR data,we propose a three-dimensional(3D)prestack RTM algorithm.The high-order fi nite diff erence time domian(FDTD)method,with the accuracy of eighth-order in space and second-order in time,is applied to simulate the forward and backward extrapolation electromagnetic fi elds.In addition,we use the normalized correlation imaging condition to obtain pre-stack RTM result and the Laplace fi lter to suppress the low frequency noise generated during the correlation process.The numerical test of 3D simulated GPR data demonstrated that 3D RTM image shows excellent coincidence with the true model.Compared with 2D RTM image,the 3D RTM image can more clearly and accurately refl ect the 3D spatial distribution of the target,and the resolution of the imaging results is far better.Furthermore,the application of observed GPR data further validates the eff ectiveness of the proposed 3D GPR RTM algorithm,and its fi nal image can more reliably guide the subsequent interpretation.

On the optimal dynamic hedging with nonferrous metals

This paper employs multivariate GARCH to model conditional correlations and to examine volatility spillovers and hedging possibilities with nonferrous metals traded on the London Metal Exchange(LME)market.Three different multivariate GARCH models(diagonal,CCC and DCC)are employed and contrasted.The nonferrous metals studied are copper,aluminum,tin,lead,zinc and nickel and span the period from January 6,2000 to February 29,2016.The multivariate DCC GARCH framework is found to fit the data in an appropriate design and provides results showing the strongest evidence of long-term persistence volatility spillovers between lead and aluminum.We also find that the Hurst exponents given by the R/S method are on average 0.94,indicating the existence of a strong degree of long-range dependence in conditional volatilities.On average,the cheapest hedge is a long position in lead and a short position in nickel.The most expensive hedge is long nickel and short copper.

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.

Conditional Kernel Covariance and Correlation

The conditional kernel correlation is proposed to measure the relationship between two random variables under covariates for multivariate data.Relying on the framework of reproducing kernel Hilbert spaces,we give the definitions of the conditional kernel covariance and conditional kernel correlation.We also provide their respective sample estimators and give the asymptotic properties,which help us construct a conditional independence test.According to the numerical results,the proposed test is more effective compared to the existing one under the considered scenarios.A real data is further analyzed to illustrate the efficacy of the proposed method.

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.

A scalar dynamic conditional correlation model:Structure and estimation

The dynamic conditional correlation(DCC) model has been widely used for modeling the conditional correlation of multivariate time series by Engle(2002). However, the stationarity conditions have been established only recently and the asymptotic theory of parameter estimation for the DCC model has not yet to be fully discussed. In this paper, we propose an alternative model, namely the scalar dynamic conditional correlation(SDCC) model. Sufficient and easily-checked conditions for stationarity, geometric ergodicity, andβ-mixing with exponential-decay rates are provided. We then show the strong consistency and asymptotic normality of the quasi-maximum-likelihood estimator(QMLE) of the model parameters under regular conditions.The asymptotic results are illustrated by Monte Carlo experiments. As a real-data example, the proposed SDCC model is applied to analyzing the daily returns of the FSTE(financial times and stock exchange) 100 index and FSTE 100 futures. Our model improves the performance of the DCC model in the sense that the Li-Mc Leod statistic of the SDCC model is much smaller and the hedging efficiency is higher.