The Experts below are selected from a list of 7893 Experts worldwide ranked by ideXlab platform

Li Yang - One of the best experts on this subject based on the ideXlab platform.

  • impact of truncation on model free implied Moment Estimator
    Social Science Research Network, 2015
    Co-Authors: Geul Lee, Li Yang
    Abstract:

    This study examines the impact of truncation, i.e., the unavailability of extremely deep-out-of-the-money option quotes, on the model-free implied Moment Estimators of Bakshi et al. (2003) and suggests how truncation should be controlled for implied higher Moment estimation. We show that truncation has a significantly larger impact on the implied skewness and kurtosis Estimators than on the implied volatility Estimator and that the impact is not completely removed by linear extrapolation (LE) suggested by Jiang and Tian (2005) or domain symmetrization (DSym) proposed by Dennis and Mayhew (2002). As an alternative method, we suggest domain stabilization (DStab) which makes the truncation error less volatile.

Geul Lee - One of the best experts on this subject based on the ideXlab platform.

  • impact of truncation on model free implied Moment Estimator
    Social Science Research Network, 2015
    Co-Authors: Geul Lee, Li Yang
    Abstract:

    This study examines the impact of truncation, i.e., the unavailability of extremely deep-out-of-the-money option quotes, on the model-free implied Moment Estimators of Bakshi et al. (2003) and suggests how truncation should be controlled for implied higher Moment estimation. We show that truncation has a significantly larger impact on the implied skewness and kurtosis Estimators than on the implied volatility Estimator and that the impact is not completely removed by linear extrapolation (LE) suggested by Jiang and Tian (2005) or domain symmetrization (DSym) proposed by Dennis and Mayhew (2002). As an alternative method, we suggest domain stabilization (DStab) which makes the truncation error less volatile.

Yang Lu - One of the best experts on this subject based on the ideXlab platform.

  • Negative Binomial Autoregressive Process with Stochastic Intensity
    Journal of Time Series Analysis, 2018
    Co-Authors: Christian Gouriéroux, Yang Lu
    Abstract:

    We introduce negative binomial‐60 autoregressive (NBAR) processes with stochastic intensity for (univariate and bivariate) count processes. The univariate NBAR process is defined jointly with an underlying intensity process, which is autoregressive gamma. The resulting count process is Markov, with negative binomial conditional and marginal distributions. The process is then extended to the bivariate case with a Wishart autoregressive matrix intensity process. The NBAR processes are compound autoregressive, which allows for simple stationarity condition and quasi‐closed form nonlinear forecasting formulae at any horizon, as well as a computationally tractable generalized method of Moment Estimator. The model is applied to a pairwise analysis of weekly occurrence counts of a contagious disease between the greater Paris region and other French regions.

Yongdai Kim - One of the best experts on this subject based on the ideXlab platform.

  • expected probability weighted Moment Estimator for censored flood data
    Advances in Water Resources, 2011
    Co-Authors: Jongjune Jeon, Youngoh Kim, Yongdai Kim
    Abstract:

    Abstract Two well-known methods for estimating statistical distributions in hydrology are the Method of Moments (MOMs) and the method of probability weighted Moments (PWM). This paper is concerned with the case where a part of the sample is censored. One situation where this might occur is when systematic data (e.g. from gauges) are combined with historical data, since the latter are often only reported if they exceed a high threshold. For this problem, three previously derived Estimators are the “B17B” Estimator, which is a direct modification of MOM to allow for partial censoring; the “partial PWM Estimator”, which similarly modifies PWM; and the “expected Moments algorithm” Estimator, which improves on B17B by replacing a sample adjustment of the censored-data Moments with a population adjustment. The present paper proposes a similar modification to the PWM Estimator, resulting in the “expected probability weighted Moments (EPWM)” Estimator. Simulation comparisons of these four Estimators and also the maximum likelihood Estimator show that the EPWM method is at least competitive with the other four and in many cases the best of the five Estimators.

Hongjuan Zhou - One of the best experts on this subject based on the ideXlab platform.

  • parameter estimation for an ornstein uhlenbeck process driven by a general gaussian noise
    Acta Mathematica Scientia, 2021
    Co-Authors: Yong Chen, Hongjuan Zhou
    Abstract:

    In this paper, we consider an inference problem for an Ornstein-Uhlenbeck process driven by a general one-dimensional centered Gaussian process (Gt)t≥0. The second order mixed partial derivative of the covariance function $$R(t,s) = \mathbb{E}\left[ {{G_t}{G_s}} \right]$$ can be decomposed into two parts, one of which coincides with that of fractional Brownian motion and the other of which is bounded by (ts)β−1 up to a constant factor. This condition is valid for a class of continuous Gaussian processes that fails to be self-similar or to have stationary increments; some examples of this include the subfractional Brownian motion and the bi-fractional Brownian motion. Under this assumption, we study the parameter estimation for a drift parameter in the Ornstein-Uhlenbeck process driven by the Gaussian noise (Gt)t≥0. For the least squares Estimator and the second Moment Estimator constructed from the continuous observations, we prove the strong consistency and the asympotic normality, and obtain the Berry-Esseen bounds. The proof is based on the inner product’s representation of the Hilbert space $$\mathfrak{h}$$ associated with the Gaussian noise (Gt)t≥0, and the estimation of the inner product based on the results of the Hilbert space associated with the fractional Brownian motion.

  • parameter estimation for an ornstein uhlenbeck process driven by a general gaussian noise
    arXiv: Probability, 2020
    Co-Authors: Yong Chen, Hongjuan Zhou
    Abstract:

    In this paper, we consider an inference problem for an Ornstein-Uhlenbeck process driven by a general one-dimensional centered Gaussian process $(G_t)_{t\ge 0}$. The second order mixed partial derivative of the covariance function $ R(t,\, s)=\mathbb{E}[G_t G_s]$ can be decomposed into two parts, one of which coincides with that of fractional Brownian motion and the other is bounded by $(ts)^{\beta-1}$ up to a constant factor. This condition is valid for a class of continuous Gaussian processes that fails to be self-similar or have stationary increments. Some examples include the subfractional Brownian motion and the bi-fractional Brownian motion. Under this assumption, we study the parameter estimation for drift parameter in the Ornstein-Uhlenbeck process driven by the Gaussian noise $(G_t)_{t\ge 0}$. For the least squares Estimator and the second Moment Estimator constructed from the continuous observations, we prove the strong consistency and the asympotic normality, and obtain the Berry-Esseen bounds. The proof is based on the inner product's representation of the Hilbert space $\mathfrak{H}$ associated with the Gaussian noise $(G_t)_{t\ge 0}$, and the estimation of the inner product based on the results of the Hilbert space associated with the fractional Brownian motion.