درخت حوزه‌های تخصصی

China has become the world’s high-growth and second largest economy since its socialist- orientation opening-up reforms in the early 1980s. Due also to its geographical, cultural and historical attractions, the country has become the world’s fourth largest tourism destination and tenth largest tourism earner in 2018 according to the World Tourist Organization. The important role played by tourism in the Chinese economy has been officially recognized by China’s National Development and Reform Commission as a strategic pillar industry development priority. In spite of these, rigorous policy-related studies on China’s tourism have been limited. The paper is a serious econometric study to investigate the determination of China’s tourism and its contribution to the country during the period 1996-2018 for credible data-based policy analysis.  Significantly, the study is carried out appropriately from an economic integration (globalization) growth framework, which is also the expenditure (as opposed to production or income) perspective of the United Nations System of National Accounts 1998/2003. Specifically, a multi-simultaneous equation model of endogenous growth and China’s tourism determination is developed. The model innovatively incorporates gravity theory and classical consumer demand contributors, Ironmonger-Lancaster new commodity attributes and Johansen policy impact add- and sub-factors (i.e., reforms and crises) explicitly in its economic integration structure. The model is then estimated by system methods with official economic and tourism 1996-2018 data from the World Tourism Organization and international databases. The research will contribute to advances in the literature and the findings provide useful insights and appropriate and much needed evidence-based inputs on the determination and contributors of tourism to China’s growth. Recommendations will be provided to key stake-holders such as tourism policy-makers, academic researchers, business analysts and tourism operators for national strategic policy analysis and practical implementation. JEL Classification: C54, F15, F62, Z32, Z38

This paper aims to estimate the Value-at-Risk (VaR) using GARCH type models with improved return distribution. Value at Risk (VaR) is an essential benchmark for measuring the risk of financial markets quantitatively. The parametric method, historical simulation, and Monte Carlo simulation have been proposed in several financial mathematics and engineering studies to calculate VaR, that each of them has some limitations. Therefore, these methods are not recommended in the case of complications in financial modeling since they require considering a series of assumptions, such as symmetric distributions in return on assets. Because the stock exchange data in the present study are skewed, asymmetric distributions along with symmetric distributions have been used for estimating VaR in this study. In this paper, the performance of fifteen VaR models with a compound of three conditional volatility characteristics including GARCH, APARCH and GJR and five distributional assumptions (normal, Student’s t, skewed Student’s t and two different Lévy distributions, include normal-inverse Gaussian (NIG) and generalized hyperbolic (GHyp)) for return innovations are investigated in the chemical, base metals, automobile, and cement industries. To do so, daily data from of Tehran Stock Exchange are used from 2013 to 2020. The results show that the GJR model with NIG distribution is more accurate than other models. According to the industry index loss function, the highest and lowest risks are related to the automotive and cement industries.

Identification of persistence component of inflation and prediction about future inflation structure is a crucial ingredient of an inflation targeting strategies or other related monetary policies. Monetary policy makers perform this task by extracting the measure known as “core inflation”. This measure is a good indicator for tracking trend of inflation and forecasting future inflation. Measures of core inflation was developed by using wavelet methods in our research. The maximum overlap discrete wavelet transformation (MOWT) and multi resolution analysis (MRA) methods are performed for extracting core inflation in period 1381(1)-1400(3) in Iran by monthly data. Using this methods is relatively new in the literature and are ideally appropriated for this task. The best wavelet in each wavelet families choose in accordance with Shannon entropy and logarithm of energy. On the other hand the properties of wavelet-based core inflation measure was evaluated by some tests. The results reveal that our new measure is more superior to the traditional approaches that used in previous studies in Iran.