جستجوی مقالات مرتبط با کلیدواژه « stochastic volatility » در نشریات گروه « اقتصاد »

As Stern et al (2019) argued, energy is considered an important determinant of sustainable economic growth. Energy sources meet the needs of various sectors such as industry, modern agriculture, commerce, transportation, etc. Therefore,electricityconsumption (energy consumption) is vital for the growth of an economy.Electricity is the backbone of today's industrial and consumer economies. Its share in the energy mix is increasing due to increasing per capita income, electrification of transportation, use of electronic devices, and demand for consumer and industrialproducts. However, developed countries are moving towards energy efficiency technology to offset the increasing demand for electricity and its effects (Bildirici et al., 2012). Discussions about the relationship between economic growth, energy consumption and some macroeconomic variables have been high among researchers and policymakers in recent decades (Ehigiamusoe and Lean, 2019; Ehigiamusoe et al., 2020). The aim of our study is to examine the dynamics of the relationship between electricity consumption, ecological footprint and real GDP in Iran by dividing GDP into oil GDP and non-oil GDP. The logic behind this is that Iran's growth model is dependent on oil exports and public sector spending, with no diversification of oil revenues to ensure sustainable development. In fact, although Iran's successive development plans have emphasized the diversification and promotion of the non-oil private sector as a priority goal, today this goal can be achieved by reducing dependence on oil. Our aim is to provide a comprehensive review of energy consumption-environment-GDP dynamics with oil on one hand and energy-environment-GDP non-oil dynamics on the other hand. Therefore, we address the dichotomy between the oil and non-oil sectors and its consequences on the efficiency of energy policies and sustainable development.

This study uses the Vector Auto-Regressive model of time-varying parameters (TVP-VAR) to examine the inter-temporal dynamics between Iran's real GDP (oil, non-oil), electricity consumption and ecological footprint during 1967-2018. The results show that the TVP-VAR model is useful for examining the dynamics of the relationship between electricity consumption, real GDP and ecological footprint.

The results show that the reaction functions of GDP with oil to positive shocks of environmental effect and electricity consumption are significantly different over time. Similar results exist for the impulse responses of the environmental effect to the positive shock of electricity consumption and GDP. We find the positive response of GDP to electricity consumption before 1978, negative between 1979 and 1991 and after 2003. The reactions of domestic gross production to environmental impact shocks between 1979 and 1986 are negative in the 8th and 12th period horizons and positive in other periods.The shock response of energy consumption to GDP is positive in four periods during 1981 to 2006 and is negative in other years. It is negative in the 8-period horizon between 1976 and 2004, as well as in the 12-period horizon between 1971 and 1999 and positive in other years.In relation to the response functions of the environmental impact of GDP and energy consumption in the horizon of 4 periods, the effect is positive, but it is positive in the horizon of 8 periods except for the years 1994-2000 and in the horizon of 12 periods except for the years 1979 to 1999 positive effects are observed.

The results show that regimes with high and low volatility of real GDP (oil and non-oil), electricity consumption and environmental impact shocks have asymmetric effects (positive or negative) on these variables. In particular, the high fluctuations in electricity consumption during 1980s, 2000s, and 2010s likely affect real oil GDP and the environmental effect, negatively, But negatively, it leads to a decrease in real non-oil GDP growth. In the 1981s, 2001s, and 2011s, low volatility of electricity consumption had a negative impact on environmental impact, and low volatility of real oil and non-oil GDP had a positive impact on environmental impact.In addition, real oil GDP fluctuations in the 1980s and 1990s both have positive effects on electricity consumption. The low real non-oil GDP fluctuations likely have positive effects on environmental effect, and real non-oil GDP fluctuations have positive effects on electricity consumption, but high real non-oil GDP fluctuations have negative effects on environmental status.

Firm investment is one of the important financial decisions in the economy, which affects the value of companies and the wealth of shareholders, which can result in increasing welfare. Despite neglecting the effects of uncertainty in traditional investment theories, modern theories have introduced various mechanisms for the impact of uncertainty on investment expenditures. Using the daily data of oil prices and the data of 131 companies listed on the Tehran Stock Exchange market during the period of 2008-2020, the factors affecting the investment of the companies are identified by emphasizing the oil price uncertainty. For this purpose, in the first step, the stochastic volatility model in the framework of the space-state approach is the basis for estimating the oil price uncertainty, and in the next, according to the results of the Hausman endogeneity test, the instrumental variable method is used to estimate the coefficients of the variables affecting investment. The findings indicate that first, the volatility of oil prices has no significant effect on investment. Second, firm size, profitability, inflation, and Tobin’s Q affect investment positively and significantly. Third, the financial leverage, which is reflected in the capital structure polices, has a significant negative effect on investment, meaning that more focus on debt financing leads to less corporate investment expenditures.

New findings show that volatility models with jump component are more successful than without jumping models in modeling stylized facts about the stock market. This study focuses on the role of jump in the return of the total index of Tehran Stock Exchange from the beginning of the trading day 1396 to the last trading day 1399. The empirical approach used for this purpose is different from the usual procedure for estimating parameters and performing diagnostic tests. In this approach, jump times, jump size and volatility are estimated. These estimates provide a dynamic picture of the role of these factors and are useful for analyzing periods of market pressure. The results of the jump existence test using Bayesian factor show the superiority of the stochastic volatility model with jump component and leverage effect (SVLJ) over other models. The results of estimating the SVLJ model show that only two jumps in the total index yield occurred with a probability between 0.015 and 0.02 in the time interval between the data of this study and six jumps with a probability greater than 0.01 occurred in the return. However, the jump component accounts for up to 15.75% of the efficiency changes, which, if not included in the modeling, will lead to inaccurate results in volatility and risk measurements. This is important because it is important to determine the share of jumps in periods of market stress, as the risk of a jump in returns usually cannot be covered, and investors may need to risk premium to bear these risks.

Due to the strategic role of volatility and instability of crude oil prices and its effects on all countries of the world, different methods of modeling and forecasting are necessary. Over the past two decades, extensive literature has emerged on various approaches to empirically modeling volatility in the crude oil market. In this research, WTI crude oil price volatility modeling, which is one of the most important types of crude oil in the market of this strategic commodity, is examined with six flexible stochastic volatility (SV) models. Then the experimental performance of these models is compared with each other using Bayesian methods. The findings of this study show that adding jump in efficiency and leverage effect to the stochastic volatility (SVLJ) model greatly improves its performance compared to other models. According to the findings of this model, the stability of volatility in the WTI market is very high and on average one jump occurs in this market every year. However, this model shows that in 2020, two jumps in WTI returns occurred in April and May, which is a unique event. In addition, the correlation between the return jump component and the volatility jump (Merton correlation jump) is not confirmed in the WTI data. Also, due to the negative leverage effect, negative shocks have stronger volatility effects than positive shocks in the crude oil market.

In the macroeconomics and international economics literature, the rate of change in domestic prices as a result of exchange rate changes is known as the degree of exchange rate pass-through. This is important because the shocks to the economy are transmitted from the exchange rate channel to the relative prices of the economy. In addition, the degree of exchange rate pass-through is affected by microeconomic and macroeconomic variables, so that the degree of exchange rate pass-through will change along with their changes. Therefore, in the present study, the impact of exchange rate on domestic prices is estimated by using the Time-varying Parameter Factor Augmented Structural VAR with Stochastic Volatility (TVP-SFAVAR-SV) and applying seasonal data from 1990 to 2018. First, the latent variable of the amount of speculative activities in the Iranian economy is modeled and estimated. The results show that the highest speculation belongs to the periods 1994 -1996, 1998-1999 and 2011- 2012. Also, the shock to the speculative activities variable in the period under study has led to an increase in inflation. The estimated exchange rate pass-through coefficient has not been constant. Historical variance decomposition analysis of exchange rate pass-through in the presence of the effective factors also shows that the almost all exchange rate fluctuations can be explained by inflation and exchange rate fluctuations, and production gaps.

Volatility is a measure of uncertainty that plays a central role in financial theory, risk management, and pricing authority. Turbulence is the conditional variance of changes in asset prices that is not directly observable and is considered a hidden variable that is indirectly calculated using some approximations. To do this, two general approaches are presented in the literature of financial economics for modeling and calculating volatility. In the first approach, conditional variance is modeled as a function of the square of the past shocks of return on assets. Models of the GARCH type fall into this category. In the alternative approach, volatility is assumed to be a random variable, which evolves using nonlinear patterns of Gaussian state space. This type of model is known as Stochastic Volatility (SV).  Because, SV models include two kinds of noise processes, one for observations and another for hidden, volatility, thus, they are more realistic and more flexible in calculating volatility than GARCH type.  This study attempts to analyze the volatility in stock returns of 50 companies, which are active in Tehran Stock Market using symmetric and asymmetric methods of Stochastic Volatility, which is different in the presence of leverage effect. The empirical comparison of these two models by calculating the posterior probability of accuracy of each model using the MCMC Bayesian method represents a significant advantage of the ASV model. The results in both symmetric and asymmetric methods represent the very high stability of the volatility generated by the shocks on stock returns; therefore, the Tehran Stock market changes in returns due to this high sustainability will be predictable.

In this study measuring of uncertainty related to macroeconomic variables has been considered. Given that uncertainty is not directly observable and measurable, researchers suggest different proxies for its measuring. One of the popular approaches in the related literature is based on time series models. In this approach, the proper measurement of uncertainty requires correct specification of regression equations. Accordingly, we estimate the uncertainty estimates of the baseline model for several key series in Iran’s macro dataset and compare it to the corresponding estimates of the alternative models. Our results show that uncertainty estimates of macro variables are affected by the specification of the forecasting regression equations. The difference over time between the estimates for these variables is quite pronounced in some periods, suggesting that much of the variation in these series is predictable and should not be attributed to uncertainty. Finally, evaluation of the uncertainty forecasting accuracy of the models shows that the SV and asymmetric GARCH models have better performance for the in-sample and out-of-sample periods, respectively.