Nafiseh Shahmoradi; Hasan Ghalibaf Asl
Abstract
A large number of investors have been attracted to the Iran Mercantile Exchange as a result of launching Bahar Azadi Coin future contracts, also known as gold coin future contracts, since 2007. The nature of gold price as a physical-commodity and financial asset, as well as other contributing factors ...
Read More
A large number of investors have been attracted to the Iran Mercantile Exchange as a result of launching Bahar Azadi Coin future contracts, also known as gold coin future contracts, since 2007. The nature of gold price as a physical-commodity and financial asset, as well as other contributing factors to the gold futures market, extremely complicates the analysis of the relationship between the underlying variables.One of the methods to forecast the price volatility is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. However, the high percentage of errors in such prediction has forced researchers to apply a variety of techniques in the hope of more accurate projections. Similarly, in this study, a hybrid model of the GARCH and Artificial Neural Network model (ANN) was used to predict the volatility of gold coin spot and future prices in the Iran Mercantile Exchange.In this study, variables such as global gold price, spot or future gold coin price (depending on which one is analyzed), US Dollar/IR Rial, world price of OPEC crude oil, and Tehran Stock Exchange Index were considered as factors affecting the price of gold coin. The results of the study indicate that the ANN-GARCH model provides a better prediction model compared to the Autoregressive models. Moreover, the ANN-GARCH model was utilized to compare the predictive power of spot and future gold coin prices, and it revealed that gold coin future price fluctuations predicted spot price of gold coin more accurately.
Alexey Zaytsev
Abstract
Modern research often requires the use of economic models with multiple agents that interact over time. In this paper we research overlapping generations models, hereinafter OLG. In these models, the phenomenon of the multiplicity of long-term equilibrium may arise. This fact proves to be important for ...
Read More
Modern research often requires the use of economic models with multiple agents that interact over time. In this paper we research overlapping generations models, hereinafter OLG. In these models, the phenomenon of the multiplicity of long-term equilibrium may arise. This fact proves to be important for the theoretical justification of some economic effects, such as the collapse of the market and others. However, there is little theoretical research on the possibility of multiple equilibria in these models. At the same time, the works that exist are devoted to models with only few periods. This is due to the fact that the complexity of algorithms that calculate all long-term equilibria grows too fast with realistically selected lifespan values. However, solutions of some OLG models after the introduction of additional variables can become polynomial systems. Thus it is possible to represent many long-term equilibria as an algebraic variety. In particular, the Gr¨obner basis method became popular. However, this approach can only be used effectively when there are few variables. In this paper we consider the task of finding long-term equilibrium in overlapping generations models with many periods. We offer an algorithm for finding the system’s solutions and use it to investigate the presence of multiple solutions in realistically calibrated models with long-lived agents. We also examine these models for multiple equilibria using the Monte Carlo method and replicate previously known results using a new algorithm.
