scholarly journals Kernel Estimation of Cumulative Residual Tsallis Entropy and Its Dynamic Version under ρ-Mixing Dependent Data

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 9
Author(s):  
Muhammed Rasheed Irshad ◽  
Radhakumari Maya ◽  
Francesco Buono ◽  
Maria Longobardi
Keyword(s):  
Numerical Evaluation ◽  
Asymptotic Properties ◽  
Kernel Estimation ◽  
Tsallis Entropy ◽  
Dependent Data ◽  
Entropy Measure ◽  
Regularity Conditions ◽  
Monte Carlo Simulation Study ◽  
Dynamic Version ◽  
Cumulative Residual

Tsallis introduced a non-logarithmic generalization of Shannon entropy, namely Tsallis entropy, which is non-extensive. Sati and Gupta proposed cumulative residual information based on this non-extensive entropy measure, namely cumulative residual Tsallis entropy (CRTE), and its dynamic version, namely dynamic cumulative residual Tsallis entropy (DCRTE). In the present paper, we propose non-parametric kernel type estimators for CRTE and DCRTE where the considered observations exhibit an ρ-mixing dependence condition. Asymptotic properties of the estimators were established under suitable regularity conditions. A numerical evaluation of the proposed estimator is exhibited and a Monte Carlo simulation study was carried out.

scholarly journals Some Characterization Results on Dynamic Cumulative Residual Tsallis Entropy

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Madan Mohan Sati ◽  
Nitin Gupta
Keyword(s):  
Tsallis Entropy ◽  
Information Measure ◽  
Probability Models ◽  
New Information ◽  
Life Distributions ◽  
Equilibrium Probability ◽  
Dynamic Version ◽  
Cumulative Residual

We propose a generalized cumulative residual information measure based on Tsallis entropy and its dynamic version. We study the characterizations of the proposed information measure and define new classes of life distributions based on this measure. Some applications are provided in relation to weighted and equilibrium probability models. Finally the empirical cumulative Tsallis entropy is proposed to estimate the new information measure.

SIEVE ESTIMATION OF THE MINIMAL ENTROPY MARTINGALE MARGINAL DENSITY WITH APPLICATION TO PRICING KERNEL ESTIMATION

2017 ◽  
Vol 20 (06) ◽  
pp. 1750041 ◽  
Author(s):  
DENIS BELOMESTNY ◽  
WOLFGANG KARL HÄRDLE ◽  
EKATERINA KRYMOVA
Keyword(s):  
Asymptotic Properties ◽  
Kernel Estimation ◽  
Equity Returns ◽  
Statistical Problem ◽  
Entropy Measure ◽  
Marginal Density ◽  
Sieve Estimation ◽  
Risk Neutral ◽  
Empirical Performance ◽  
Ill Posed

We study the problem of nonparametric estimation of the risk-neutral densities from options data. The underlying statistical problem is known to be ill-posed and needs to be regularized. We propose a novel regularized empirical sieve approach for the estimation of the risk-neutral densities which relies on the notion of the minimal martingale entropy measure. The proposed approach can be used to estimate the so-called pricing kernels which play an important role in assessing the risk aversion over equity returns. The asymptotic properties of the resulting estimate are analyzed and its empirical performance is illustrated.

scholarly journals Generalized Entropies, Variance and Applications

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 709 ◽  
Author(s):  
Abdolsaeed Toomaj ◽  
Antonio Di Crescenzo
Keyword(s):  
Residual Entropy ◽  
Dispersion Measure ◽  
Homogeneous Poisson Process ◽  
Cumulative Residual Entropy ◽  
Excess Wealth Order ◽  
Dynamic Version ◽  
Cumulative Residual ◽  
Non Homogeneous Poisson Process ◽  
Generalized Entropies ◽  
Cumulative Entropy

The generalized cumulative residual entropy is a recently defined dispersion measure. In this paper, we obtain some further results for such a measure, in relation to the generalized cumulative residual entropy and the variance of random lifetimes. We show that it has an intimate connection with the non-homogeneous Poisson process. We also get new expressions, bounds and stochastic comparisons involving such measures. Moreover, the dynamic version of the mentioned notions is studied through the residual lifetimes and suitable aging notions. In this framework we achieve some findings of interest in reliability theory, such as a characterization for the exponential distribution, various results on k-out-of-n systems, and a connection to the excess wealth order. We also obtain similar results for the generalized cumulative entropy, which is a dual measure to the generalized cumulative residual entropy.

scholarly journals What They Did Not Tell You about Algebraic (Non-) Existence, Mathematical (IR-)Regularity, and (Non-) Asymptotic Properties of the Dynamic Conditional Correlation (DCC) Model

2019 ◽  
Vol 12 (2) ◽  
pp. 61 ◽  
Author(s):  
McAleer
Keyword(s):  
Financial Risk ◽  
Dynamic Models ◽  
Asymptotic Properties ◽  
Regularity Conditions ◽  
Conditional Correlation ◽  
Risky Assets ◽  
Dynamic Conditional Correlation ◽  
Volatility Models ◽  
Dynamic Time ◽  
Optimal Hedge

In order to hedge efficiently, persistently high negative covariances or, equivalently, correlations, between risky assets and the hedging instruments are intended to mitigate against financial risk and subsequent losses. If there is more than one hedging instrument, multivariate covariances and correlations have to be calculated. As optimal hedge ratios are unlikely to remain constant using high frequency data, it is essential to specify dynamic time-varying models of covariances and correlations. These values can either be determined analytically or numerically on the basis of highly advanced computer simulations. Analytical developments are occasionally promulgated for multivariate conditional volatility models. The primary purpose of this paper is to analyze purported analytical developments for the only multivariate dynamic conditional correlation model to have been developed to date, namely the widely used Dynamic Conditional Correlation (DCC) model. Dynamic models are not straightforward (or even possible) to translate in terms of the algebraic existence, underlying stochastic processes, specification, mathematical regularity conditions, and asymptotic properties of consistency and asymptotic normality, or the lack thereof. This paper presents a critical analysis, discussion, evaluation, and presentation of caveats relating to the DCC model, with an emphasis on the numerous dos and don’ts in implementing the DCC model, as well as a related model, in practice.

Optimal estimation for semimartingales

1986 ◽  
Vol 23 (02) ◽  
pp. 409-417 ◽  
Author(s):  
A. Thavaneswaran ◽  
M. E. Thompson
Keyword(s):  
Continuous Time ◽  
Asymptotic Properties ◽  
Optimal Estimation ◽  
Parametric Estimation ◽  
Local Martingale ◽  
Regularity Conditions ◽  
Probability Measures ◽  
Simple Process ◽  
Special Cases ◽  
Least Squares Estimates

This paper extends a result of Godambe's theory of parametric estimation for discrete-time stochastic processes to the continuous-time case. LetP={P} be a family of probability measures such that (Ω,F, P) is complete, (Ft, t≧0) is a standard filtration, andX = (XtFt, t ≧ 0)is a semimartingale for everyP ∈ P. For a parameterθ(Ρ), supposeXt=Vt,θ+Ht,θwhere theVθprocess is predictable and locally of bounded variation and theHθprocess is a local martingale. Consider estimating equations forθof the formprocess is predictable. Under regularity conditions, an optimal form forαθin the sense of Godambe (1960) is determined. WhenVt,θis linear inθthe optimal, corresponds to certain maximum likelihood or least squares estimates derived previously in special cases. Asymptotic properties of, are discussed.

scholarly journals What They Did Not Tell You about Algebraic (Non-) Existence, Mathematical (IR-)Regularity and (Non-) Asymptotic Properties of the Full BEKK Dynamic Conditional Covariance Model

2019 ◽  
Vol 12 (2) ◽  
pp. 66 ◽  
Author(s):  
Michael McAleer
Keyword(s):  
Dynamic Models ◽  
Asymptotic Properties ◽  
Regularity Conditions ◽  
Covariance Model ◽  
Conditional Covariance ◽  
Risky Assets ◽  
Volatility Models ◽  
Hedge Ratios ◽  
Covariance Models ◽  
Optimal Hedge

Persistently high negative covariances between risky assets and hedging instruments are intended to mitigate against risk and subsequent financial losses. In the event of having more than one hedging instrument, multivariate covariances need to be calculated. Optimal hedge ratios are unlikely to remain constant using high frequency data, so it is essential to specify dynamic covariance models. These values can either be determined analytically or numerically on the basis of highly advanced computer simulations. Analytical developments are occasionally promulgated for multivariate conditional volatility models. The primary purpose of the paper is to analyze purported analytical developments for the most widely-used multivariate dynamic conditional covariance model to have been developed to date, namely the Full BEKK model, named for Baba, Engle, Kraft and Kroner. Dynamic models are not straightforward (or even possible) to translate in terms of the algebraic existence, underlying stochastic processes, specification, mathematical regularity conditions, and asymptotic properties of consistency and asymptotic normality, or the lack thereof. The paper presents a critical analysis, discussion, evaluation and presentation of caveats relating to the Full BEKK model, and an emphasis on the numerous dos and don’ts in implementing the Full BEKK and related non-Diagonal BEKK models, such as Triangular BEKK and Hadamard BEKK, in practice.

scholarly journals A Berry-Esseen Type Bound in Kernel Density Estimation for Negatively Associated Censored Data

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Qunying Wu ◽  
Pingyan Chen
Keyword(s):  
Fixed Point ◽  
Censored Data ◽  
Kernel Estimation ◽  
Kernel Density ◽  
Kernel Density Estimator ◽  
Regularity Conditions ◽  
Density Estimator ◽  
Negatively Associated ◽  
Kaplan Meier ◽  
Na Sequences

We discuss the kernel estimation of a density function based on censored data when the survival and the censoring times form the stationary negatively associated (NA) sequences. Under certain regularity conditions, the Berry-Esseen type bounds are derived for the kernel density estimator and the Kaplan-Meier kernel density estimator at a fixed pointx.

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