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Purpose-In this paper, we apply the Grey Cobb-Douglas production model to predict the GDP, examine the effects of the variation rate of capital, and labor inputs to economic growth. Many factors contribute to economic growth, such that technological progress, labor force, capital accumulation, the optimal using of sources, energy, institutional innovation ext. In reality, a variate of economic factors often intertwine with each other. Methodology-The capital and labor are main elements of economic growth. Improving the capital and labor performance plays important role in increase the wealth of a country. Traditionally, Cobb-Douglas (C-D) production model use only capital stock and labor to describe the economic growth. In this study, firstly C-D production function is established and confirmed that the capital and labor has a positive impact on economic growth (GDP). Then GM(1,1) prediction model is used to predict the future values of capital stock and labor force inputs. Findings-The future GDP values are predicted by the estimated capital and labor values putting into the Cobb-Douglas model. We also obtained the production elasticities of capital and labor inputs. Findings suggest that the contribution rate of capital is 0.403 and labor is 1.094 to economic growth. The sum of the contributions of factors is 1.497 and greater than one. Conclusion-Findings of this empirical studies shows that percentage of the increase in GDP is greater than that of the increase in capital stock and labor.

In this paper we present a novel model to analyze the behavior of random asset price process under the assumption that the stock price process is governed by time-changed generalized mixed fractional Brownian motion with an inverse gamma subordinator. This model is constructed by introducing random time changes into generalized mixed fractional Brownian motion process. In practice it has been observed that many different time series have long-range dependence property and constant time periods. Fractional Brownian motion provides a very general model for long-term dependent and anomalous diffusion regimes. Motivated by this facts in this paper we investigated the long-range dependence structure and trapping events (periods of prices stay motionless) of CSCO stock price return series. The constant time periods phenomena are modeled using an inverse gamma process as a subordinator. Proposed model include the jump behavior of price process because the gamma process is a pure jump Levy process and hence the subordinated process also has jumps so our model can be capture the random variations in volatility. To show the effectiveness of proposed model, we applied the model to calculate the price of an average arithmetic Asian call option that is written on Cisco stock. In this empirical study first the statistical properties of real financial time series is investigated and then the estimated model parameters from an observed data. The results of empirical study which is performed based on the real data indicated that the results of our model are more accuracy than the results based on traditional models.

In this study, we review the connections between Lévy processes with jumps and self-decomposable laws. Self-decomposable laws constitute a subclass of infinitely divisible laws. Lévy processes additive processes and independent increments can be related using self-similarity property. Sato (1991) defined additive processes as a generalization of Lévy processes. In this way, additive processes are those processes with inhomogeneous (in general) and independent increments and Lévy processes correspond with the particular case in which the increments are time homogeneous. Hence Lévy processes are considerable as a particular type. Self-decomposable distributions occur as limit law an Ornstein-Uhlenbeck type process associated with a background driving Lévy process. Finally as an application, asset returns are representing by a normal inverse Gaussian process. Then to test applicability of this representation, we use the nonparametric threshold estimator of the quadratic variation, proposed by Cont and Mancini (2007).

Purpose-In this paper, we investigate the Grey Cobb-Douglas production model applicable to estimation of economical indicators. Methodology-In the multi regression model, explanatory variables for estimation of future value of indicatiors is estimated by using Grey Cobb-Douglas model. Findings-GDP is an indicator for economic growth. We are used the annual data of United State of American economy for 1951 to 2008 and estimated the 2009-2018 years. The sum of the contributions of factors is 1.497 and greater than one, so it shows increasing return to scale. Conclusion-The percentage of the increase in GDP is greater than that of the increase in capital stock and labor.