Dynamic connectedness and network in the high moments of cryptocurrency,stock,and commodity markets

This study examines the connectedness in high-order moments between cryptocurrency,major stock(U.S.,U.K.,Eurozone,and Japan),and commodity(gold and oil)markets.Using intraday data from 2020 to 2022 and the time and frequency connectedness models of Diebold and Yilmaz(Int J Forecast 28(1):57–66,2012)and Barunik and Křehlik(J Financ Econom 16(2):271–296,2018),we investigate spillovers among the markets in realized volatility,the jump component of realized volatility,realized skewness,and realized kurtosis.These higher-order moments allow us to identify the unique characteristics of financial returns,such as asymmetry and fat tails,thereby capturing various market risks such as downside risk and tail risk.Our results show that the cryptocurrency,stock,and commodity markets are highly connected in terms of volatility and in the jump component of volatility,while their connectedness in skewness and kurtosis is smaller.Moreover,jump and volatility connectedness are more persistent than that of skewness and kurtosis connectedness.Our rolling-window analysis of the connectedness models shows that connectedness varies over time across all moments,and tends to increase during periods of high uncertainty.Finally,we show the potential of gold and oil as hedging and safe-haven investments for other markets given that they are the least connected to other markets across all moments and investment horizons.Our findings provide useful information for designing effective portfolio management and cryptocurrency regulations.

AUTOMATIC TARGET RECOGNITION OF RADAR HRRP BASED ON HIGH ORDER CENTRAL MOMENTS FEATURES

The paper addresses the problem of target recognition using High-resolution Radar Range Profiles(HRRP).A novel approach of feature extraction and dimension reduction based on extended high order central moments is proposed in order to reduce the dimension of range profiles.Features extracted from radar HRRPs are normalized and smoothed,and then comparative analysis of the similar approaches is done.The range profiles are obtained by step frequency technique using the two-dimensional backscatters distribution data of four different aircraft models.The template matching method by nearest neighbor rules,which is based on the theory of kernel methods for pattern analysis,is used to classify and identify the range profiles from four different aircrafts.Numerical simulation results show that the proposed approach can achieve good performance of stability,shift independence and higher recognition rate.It is helpful for real-time identification and the engineering implements of automatic target recognition using HRRP.The number of required templates could be reduced con-siderably while maintaining an equivalent recognition rate.

On the Spectral Moments of Wind Waves

In order to obtain high order spectral moments, the residual moment M(w(n))(i) = integral(0)(wn) w(i)S(w)dw, as proposed by Denis s, is presented for approximate estimation of spectral moment m(i) = integral(0)(infinity) w(i)S(w)dw. Glazman's partial averaging idea is discussed. It is pointed out that Glazman's method and definition of non-dimensional spectral moment can not be used to estimate spectral moments for engineering purposes and that method is not supported by theory and computation. The non-dimensional spectral moment of PM spectrum, which should be expressed as [GRAPHICS] is related to wind speed. The 0 - 8th moments of PM spectrum are estimated for wind speeds of 10, 20 and 30 m/s and some discussions are given.

NEW RANGE ALIGNMENT ALGORITHM FOR ISAR BASED ON HIGH ORDER MOMENT

A new technique is proposed for range alignment in Inverse Synthetic Aperture Radar （ISAR）. The basic idea is to perform range alignment using a maximum kurtosis （fourth-order central moment） criterion. After maximizing the kurtosis of the combined range profile of two adjacent echoes, the amount of range shift between them can be automatically tracked out. The combined range profile is constructed by a max operation, which only reserves the larger elements of the two echoes, and the echoes＇ amplitudes are limited before they are combined. This algorithm has bee~ used to process real ISAR data and the results demonstrate the effectiveness of the method. Compared with the correlation method and the minimum entropy method, the proposed algorithm obtains much better results in both examples in this paper. Its computation complexity has the same order of magnitude as the minimum entropy method.