in this paper, we investigate variations of gold coin price and also probe to model the fluctuations and conditional variance of coin market returns. the data consist of daily market prices of gold coin over the 1380 – 1386 period. since volatility clustering is viewed in time series of returns, we employ arch (autoregressive conditional heteroskedasticity) methodology in order to model the var...

This paper gives necessary and sufficient conditions for stationarity and existence of second moments in mixtures of linear vector autoregressive models with autoregressive conditional heteroskedasticity. Sufficient conditions are also provided for a more general model in which the mixture components are permitted to exhibit limited forms of nonlinearity. When specialized to the corresponding n...

1.1. Point Processses and Intensities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1. Stochastic Integrals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2. The Autoregressive Conditional Duration Model . . . . . . . . . . . . . . . . . . . . . . . 2 1.3. The Autoregressive Conditional Intensity Model . . . . . . . . . . . . . . . ...

The variance–covariance matrix is a multi-dimensional array of numbers, containing information about the individual variabilities and pairwise linear dependence set variables. However, itself difficult to represent in concise way, particularly context multivariate autoregressive conditional heteroskedastic models. common practice report plots k(k−1)/2 time-varying covariances, where k number ma...

This paper demonstrates that metaheuristic algorithms can provide a useful general framework for estimating both linear and nonlinear econometric models. Two metaheuristic algorithms—firefly and accelerated particle swarm optimization—are employed in the context of several quantile regression models. The algorithms are stable and robust to the choice of starting values and the presence of vario...

Various nonparametric kernel regression estimators are presented, based on which we consider two nonparametric tests for neglected nonlinearity in time series regression models. One of them is the goodness-of-fit test of Cai, Fan, and Yao (2000) and another is the nonparametric conditional moment test by Li and Wang (1998) and Zheng (1996). Bootstrap procedures are used for these tests and thei...

This paper addresses a method to solve multi-period portfolio selection on the stock market. The problem seeks an investor trade stocks with finite budget and given integer number of hold in portfolio. must be performed through stockbroker that charges its respective transaction cost has minimum required amount. A mathematical model been proposed deal constrained problem. objective function is ...

The paper establishes the local asymptotic normality property for general conditionally heteroskedastic time series models of multiplicative form, $\epsilon _t=\sigma _t(\boldsymbol {\theta }_0)\eta _t$ , where volatility $\sigma }_0)$ is a parametric function $\{\epsilon _{s}, s< t\}$ and $(\eta _t)$ sequence i.i.d. random variables with common density $f_{\boldsymbol }_0}$ . In contrast ea...

This paper presents a parsimonious approach to estimating conditional skewness and kurtosis, as well as conditional variance, in financial log-returns time series. Using a GARCH formulation of the skew t-distribution (Jones and Faddy, 2003), autoregressive relationships are developed for the conditional skewness and conditional kurtosis. A numerical example indicates that allowing for estimates...