Seyed Ali Hoseini Ebrahimaba; khalil jahangiri; mahdi Ghaemi Asl; hasan heidari
Abstract
Introduction: Decision making in conditions of uncertainty is one of the important features of risk asset allocation optimization models. Interconnection in stock price fluctuations or other assets is introduced as a factor in transferring price fluctuations from one or more sectors to other sectors. ...
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Introduction: Decision making in conditions of uncertainty is one of the important features of risk asset allocation optimization models. Interconnection in stock price fluctuations or other assets is introduced as a factor in transferring price fluctuations from one or more sectors to other sectors. Since the main drawbacks of the Markowitz model are the need for a normal distribution of the return series and the impossibility of short-selling, the Bayesian DCC-GARCH model and the Huang & Litzenberger approach solve the problems of the Markowitz model, respectively. At the same time, the use of wavelet analysis makes it possible to present a suitable portfolio based on different frequency and scale domains during different sub-periods. Theoretical framework: According to Zhang, et al. (2018), the Markowitz mean-variance method is the most popular method for solving the optimal portfolio selection problem. But Trichilli, et al. (2020) point out that due to the high sensitivity of the Markowitz mean-variance process to small changes in inputs as well as the dependence of the process on past historical prices, it leads to a lack of application of the investor’s knowledge and experience in the capital market. Unfortunately, the Markowitz portfolio optimization model leads to the selection of a small number of superior assets. He suggests using the Bayesian approach to address the shortcomings of the Markowitz model. Another critique of previous models of modern portfolio theory is the assumption of a normal distribution for variance of portfolio. Hence, fat-tail asymmetric distributions such as the dynamic conditional variance heterogeneity (DCC-GARCH) approach are used in generalized Markowitz models that are closer to real-world data. But dynamic conditional heterogeneity models have limitations in asymmetric time series analysis. This led to the use of multivariate skew variance heterogeneity models such as Bayesian DCC-GARCH, which are more capable than MGARCH models in adopting the characteristics of financial time series in the process of estimating covariance and correlation matrices, used by Bala and Takimoto (2017), Fiorchi et al. (2014). Another problem with the Markowitz approach is that it assumes sales restrictions. This means that short-term sales are not possible. Therefore, Huang and Litzenberger (1988) introduced this generalized Markowitz model to remove this constraint in the model. In and Kim (2013) also consider the use of wavelet transform methods in Markowitz model to lead to more realistic results. Methodology: Rambaud, et al. (2009) argue that if an economy consists of a set of risky assets combined with a risk-free asset, then portfolios along the capital market line (CML) are superior than the efficient frontiers portfolios that contain only high-risk assets. Black (1972) imposed the possibility of short-selling (negative weight) to the basic Markowitz model by introducing mathematical relations. The period of this research is from 14/12/2008 to 16/06/2019 and according to the periods, before JCPOA, after JCPOA and the leave of the United States from JCPOA. The covariance matrix uses two different methods (unconditional and conditional derived from the Bayesian DCC-GARCH model) in the Huang & Litzenberger portfolio optimization model, in four different time scales based on the maximal overlap discrete wavelet transform (MODWT) approach. the results are compared at the end to select the best portfolio from the two covariance matrices. Results & Discussion: By comparing the performance of the portfolios obtained from the unconditional and conditional covariance-variance matrices of the Bayesian DCC model, it is observed that in all subsections and wavelets, the efficiency of the portfolio of the Bayesian DCC model is higher than the unconditional model and the degree of efficiency varies in different subsectors. In fact, when all time-series have an abnormal distribution, the efficiency of asset portfolio derived from the variance-covariance matrix of the Bayesian conditional model is much higher than the unconditional model. The difference between the performance of asset portfolios derived from Bayesian unconditional and conditional models is less when there is a combination of normal and abnormal time series, and this necessitates the application of Bayesian models in financial markets, especially when all series are abnormal. Conclusions & Suggestions: The important result of the present study is to realize the multi-resolution nature of Huang and Litzenberger portfolio optimization theory in the Iranian capital market. The Estimation results indicate that the performance of portfolios in the medium-term and long-term scales (wavelets D3 and D4) is higher than the performance of these portfolios in the short-term scales (D1 and D2). Also, the present study clearly showed that in all subsectors, asset portfolios obtained by Bayesian distribution and by means of variance-covariance matrix extracted by Monte Carlo Markov chain (MCMC) method have higher efficiency than other portfolios which are obtained from other statistical distributions. Also, since all asset portfolios obtained under the second part are more efficient than other sub-sectors, one of the important achievements of the present study is the positive effect of lifting economic sanctions on the Iranian capital market.
mahdi ghaemiasl; Mahmod Hossin Mahdvi Adeli; shhab matin; sayed mahdi mosavi barrodi
Abstract
Iran has more than a century of history in exploration and production; the first successful exploration well was Masjid Suleiman-1 on May 26, 1908. Since then, based on the latest oil and gas reports, 145 hydrocarbon fields and 297 oil and gas reservoirs have been discovered in Iran, with many fields ...
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Iran has more than a century of history in exploration and production; the first successful exploration well was Masjid Suleiman-1 on May 26, 1908. Since then, based on the latest oil and gas reports, 145 hydrocarbon fields and 297 oil and gas reservoirs have been discovered in Iran, with many fields having multiple pay zones. Proved oil reserves in Iran, according to its government, rank fifth largest in the world at approximately 150 billion barrels as of 2014, although it ranks third if Canadian reserves of unconventional oil are excluded. This is roughly 10% of the world's total proven petroleum reserves.
Oil sector in most of oil exporting countries (such as Iran) is a state-run sector and oil revenues belong to government. Iran is an energy superpower and the Petroleum industry in Iran plays an important part in it. In 2004 Iran produced 5.1 percent of the world’s total crude oil (3.9 million barrels per day), which generated revenues of US$25 billion to US$30 billion and was the country’s primary source of foreign currency. At 2006 levels of production, oil proceeds represented about 18.7 percent of gross domestic product (GDP). However, the importance of the hydrocarbon sector to Iran’s economy has been far greater. The oil and gas industry has been the engine of economic growth, directly affecting public development projects, the government’s annual budget, and most foreign exchange sources. In 2009, the sector accounted for 60 percent of total government revenues and 80 percent of the total annual value of both exports and foreign currency earnings. Oil and gas revenues are affected by the value of crude oil on the international market. It has been estimated that at the Organization of the Petroleum Exporting Countries (OPEC) quota level (December 2004), a one-dollar change in the price of crude oil on the international market would alter Iran’s oil revenues by US$1 billion.
The main hypothesis of this study is that the government's dependence on oil revenues has been caused policy passivity in Iran's economy. Fiscal policy and monetary policy are the two tools used by the state to achieve its macroeconomic objectives. While for many countries the main objective of fiscal policy is to increase the aggregate output of the economy, the main objective of the monetary policies is to control the interest and inflation rates. Traditionally, both the policy instruments were under the control of the national governments. Thus traditional analyses were made with respect to the two policy instruments to obtain the optimum policy mix of the two to achieve macroeconomic goals, lest the two policy tools be aimed at mutually inconsistent targets. In case of an active fiscal policy and a passive monetary policy, when the economy faces an expansionary fiscal shock that raises the price level, money growth passively increases as well because the monetary authority is forced to accommodate these shocks. But in case both the authorities are active, then the expansionary pressures created by the fiscal authority are contained to some extent by the monetary policies.
In other word the central bank's monetary policy and fiscal policy of the government have a heavy reliance on oil revenues and budgeting and monetary changes, instead of being active and effective, have an affective and passive nature and are subject to oil shocks.
In this study in order to investigate this hypothesis, seasonal data of 1369:1 to 1389:4 of oil revenues, government expenditures (as a representative of fiscal policy), monetary base (as a representative of monetary policy), GDP, exchange rate and GDP deflator (as a representative of price index) in a Factor-Augmented Bayesian Vector Autoregressive model have been used. If a small number of estimated factors effectively summarize large amounts of information about the economy, then a natural solution to the degrees-of-freedom problem in VAR analyses is to augment standard VARs with estimated factors. In this paper we consider the estimation and properties of factor-augmented vector autoregressive models (FAVARs).
Results of impulse response function and variance decomposition clearly confirm the passive monetary and fiscal policy in the Iranian economy. In other words, among the variables of model, the most affected variables respectively are the monetary base and government expenditures. According to the authors, there are two basic ways to deal with policy passivity, which are sterilization and stabilization of oil revenues through the correct management of stabilization funds and diversification of exports. Sterilization is, not to bring all the revenues into the country all at once, and to save some of the revenues abroad in special funds and bring them in slowly. In developing countries, this can be politically difficult as there is often pressure to spend the boom revenues immediately to alleviate poverty, but this ignores broader macroeconomic implications. Sterilisation will reduce the spending effect, alleviating some of the effects of inflation. Another benefit of letting the revenues into the country slowly is that it can give a country a stable revenue stream, giving more certainty to revenues from year to year. Also, by saving the boom revenues, a country is saving some of the revenues for future generations. In addition Oil stabilization funds are usually designed to address the problems created by the volatility and unpredictability of oil revenues, the need to save part of the oil revenues for future generations or both.
