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.
hassan heidari; solmaz sadeghpour; morteza dehghandorost
Abstract
Undoubtedly, the financing structure in each country's economy is considered to be the main element of the economic system of that country, because the life of an economy depends on its production and growth in its various fields, and its production and growth will not be realized without the required ...
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Undoubtedly, the financing structure in each country's economy is considered to be the main element of the economic system of that country, because the life of an economy depends on its production and growth in its various fields, and its production and growth will not be realized without the required financial resources. The task entrusted to the economic system of the country is within the framework of its financing structure.
According to some economic experts, in Iran, the state budget, private sector savings and external resources are three determinants of financing sources. This means that active enterprises in various fields of production can pay for these resources to cover their needs. The government budget as one of the sources of financing firms due to dependence on oil revenues, can not be a powerful stimulus to sustain economic growth in the country. With regard to foreign sources, because of the sanctions, there can be no special account on them to finance various production sectors. Thus, it can be concluded that in the current economic conditions, the only possible option for financing firms is private sector savings; which can be equipped with the capital market and the money market (i.e., banks). Therefore, given that turbulence or fluctuations in inflation is one of the challenges in the banking system, what is highlighted in this paper is the uncertainty associated with inflation, and with the amount of bank loan facilities, and the facilities that most small and medium-sized enterprises have need to survive in the current state of the economy. Specifically, cheap bank facilities are referred to as facilities that are used by low-cost bank's resources for applicants.
Considering the importance of the banking system in the financing structure of firms and also need of small and medium firms for cheap banking facilities, and the banks' actions in the face of fluctuations of economic variables such as inflation, in order to maintain bank's financial strength, it seems that the study of the effect of macroeconomic variables on the performance of the banking system of the country has great importance.
Since developing countries, including Iran, have a high degree of uncertainty in macroeconomic variables. And this uncertainty also affects the decisions of bank officials, this paper examines the relationship between the uncertainty of inflation and Gharz-al-hassane facilities paid by commercial banks, in the form of a two-variable GARCH model using monthly data for the period of 2005-2014.
There are several methods to assess uncertainty and volatility in variables, but the most commonly used method in most econometric studies is the use of GARCH patterns. This method, proposed by Bollerslev (1986) is a modeling based on variance of variables over time.
GARCH patterns are categorized in a general classification based on the number of variables in the pattern, into univariate patterns and multivariate patterns. Single-GARCH patterns have limitations that make them difficult to use; one assumes that the conditional variance of each series is independent of all other series. In addition, in this type of models, covariance between series is not considered as an important factor of volatility of variables. These limitations make these patterns in many cases unrecognizable. The multivariate GARCH patterns can potentially overcome the deficiencies and defects of single-variable patterns. Multivariate patterns are very similar to single-variable models, and hence their estimates are similar to simple GARCH-single-variable patterns. However, in addition to the previous equations, there are certain equations for expressing how covariance moves over time (Heidari & Bashiri, 2011).
The first type of GARCH multivariate patterns is the Vech (q, p) pattern introduced by Bollerslev, Engle and Woldrige (1988). In 1991, another class of Vech (q, p) was introduced by Baba, Engle, Kraft and Kroner (1991) which became known as BEKK. This pattern has an interesting feature that, by applying several constraints, the variance-covariance matrix is a positive and definite condition. The problem with previous GARCH multi-variable protocols, including DCCs, is that they are not compatible; therefore, in order to avoid inappropriate results for estimating the conditional mean, variance, and variance of variables of inflation and facilities of the borrower, we use the cDCC model of MGARCH (1,1).
The results from the cDCC model estimation show that the uncertainty of inflation on the amount of Gharz-al-hassane facilities had a positive effect; which was not statistically significant at 5% level. On this basis, it can be concluded that with the increase of inflation, which is a depreciation of the money value and consequently a decline in the purchasers' purchasing power, the amount of Gharz-al-hassane loans has also increased. This is while expected in inflationary conditions, people withdraw these deposits or convert into long-term deposits. In the case of banks, it is also expected that by increasing inflation and rising money prices, and applying incentive policies to attract more long-term deposits instead of Gharz-al-hassane deposits, as a result, the amount of Gharz-al-hassane funds will be reduced and the amount of bank facilities will be lowered from these sources. However, the results indicate inverse of this issue in the selected time frame. It is a result that can prevent the adoption of false policies by the banking authorities. Thus, the banks are aware of the positive correlation between the inflationary fluctuations, that are increasing in inflation in the Iranian economy, and the commercial loans granted by commercial banks, withdrew its previous policies and put new policies in place to keep their capital under inflationary conditions.
As a suggested strategy, banks can use this in the context of the inflationary period in which some firms and households suffer from a drop in supply and demand due to rising prices, in order to adjust the business cycles; Thus, during this period, the resources of its Gharz-al-hassane deposits, which have not been reduced due to inflationary fluctuations, will be provided to this sector of enterprises and households. In this way, firms can continue to produce, and households are also buying power, both of which are a step towards more production and prosperity. On the other hand, the banks themselves have received a fee from the facility, and have also made a contribution to the investments made and can partly offset the depreciation of their finances during the inflationary period.
Hassan Heidari; Bahram Sanginabadi; Saman Almasi; Farzaneh Nassirzadeh
Abstract
According to volatility feedback theory there are relationships between stock return and the risk of stock. However, the results of empirical research, in several countries and markets, are different. This study investigates the effect of anticipated stock return volatility on stock return in Automobile ...
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According to volatility feedback theory there are relationships between stock return and the risk of stock. However, the results of empirical research, in several countries and markets, are different. This study investigates the effect of anticipated stock return volatility on stock return in Automobile industry using GARCH in Mean (GARCH-M) models, and ARDL modeling and Bounds test approach to level relationship. We also investigate the effect of unanticipated stock return volatility on stock return using ARDL model and Bounds test approach in the period of 06/04/1998 - 06/07/2010, applying daily and weekly Automobile industry index in Tehran Stock Exchange. Estimation of the GARCH-M model results by applying FIML method of estimation show that anticipated stock return volatility affects the stock return positively. Moreover, Bounds test approach results from both models confirm existence of long-run relationship among variables under investigation at 1% significance level. The ARDL estimation results show that anticipated (unanticipated) volatility of Automobile industry stock return increases (decreases) the return in long-run. Results from Granger causality test confirms one-way long-run causation from anticipated volatility of Automobile industry stock return to the return.
