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...

confirmation of convergence hypothesis among provinces, its role in removing divergence among regions and achieving regional convergence is one of the important issues in macroeconomics. the aim of the present study is to examine absolute and conditional income convergence hypotheses among iranian provinces during the period 2000-2010 using panel data. the hypotheses are tested for the whole pr...

We show how to extend any finite probability space into another one which satisfies the conditional construal of for original propositions, given some maximal allowed degree nesting conditional. This mitigates force well-known triviality results.

F orecasting the volatility of a financial asset has wide implications in finance. Conditional variance extracted from the GARCH framework could be a suitable proxy of financial asset volatility. Option pricing, portfolio optimization, and risk management are examples of implications of conditional variance forecasting. One of the most recent methods of volatility forecasting is Real...

Some properties of conditionally independent random variables are studied. Conditional versions of generalized Borel-Cantelli lemma, generalized Kolmogorov’s inequality and generalized Hájek-Rényi inequality are proved. As applications, a conditional version of the strong law of large numbers for conditionally independent random variables and a conditional version of the Kolmogorov’s strong law...

Mixed models are widely used to analyze longitudinal data. In their conventional formulation as linear mixed models (LMMs) and generalized LMMs (GLMMs), a commonly indispensable assumption in settings involving longitudinal non-Gaussian data is that the longitudinal observations from subjects are conditionally independent, given subject-specific random effects. Although conventional Gaussian...