Factors influencing the gold price in Iran

Mainly, assets are classified into current assets including cash, foreign exchange and gold coins, fixed assets including land, buildings, and housing, long time investments including bonds, stocks and long-term deposit, and finally intangible assets including patents and goodwill. The major assets that households keep in their portfolio are gold coins, various exchange, stocks and housing. In Iranian household culture, gold has always been considered a good financial backing. The reason for this can be attributed to the liquidity of gold and its intrinsic value. Thus, governments try to find solutions for managing price of the assets in the portfolio of household by planning and making use of certain policies.
Theoretical framework: Gold, along with oil, is one of the strategic products in international markets. In the meantime, gold, due to the intrinsic value, incorruption, enjoying popularity, hight liquidity and low maintenance costs, is very important. The price of gold is influenced by the forces of market supply and demand. However, since gold is among sensitive and strategic goods, many factors affect supply and demand and therefore its price. The most important influencing factors are changes in dollar and foreign exchange reserves, in interest rates, in the world price of oil, and global inflation.
In Engel-Granger method, the estimating result in samples with small volume is biased because we ignore the short-time dynamic interactions between variables. On the other hand, the distribution of least squares estimators is unnormal. Hence, doing the usual test hypotheses using statistics is invalid. Engel-Granger methodology is based on the assumption of a co-integration vector and when there is more than one co-integration vector, using this method will lead to inefficiencies. To resolve these problems, Johansson (1989) and Johansen-Juselius (1990) proposed maximum likelihood estimation method for co-integration test.
Johansen-Juselius cointegration method is not useful because stationary degree of model variables may not be the same. In this method, short and long time relationship between the dependent variable and the other explanatory variables of the model is simultaneously estimated. This method does not require the same degree of variable's co-integration. The Auto Regressive Distributed Lag (ARDL) methodology is applicable in the case of variables combinations of I (1) and I (0) cointegration.
In this method, in order to estimate long-term relationship a two-stage method can be used which is as follows. In the first stage Banerjee et al.'s (1993) test is used. The null hypothesis of this test shows that there is not any co-integration or long-time relationship between the variables and t statistics is used. In the second stage which is proposed by Pesaran and Shin (1996), long-term relationship between the variables under investigation is examined by calculating F statistic for significantly lagged levels of the variables in the form of error correction.
The results of long-term estimation are presented in the following Table.
significant level T statistic Standard deviation Coefficients variables
000/0 783/4- 205/0 98/0- LOIL
001/0 252/0- 993/0 25/0- LSR
009/0 456/0- 758/0 67/0- LER
039/0 442/0 330/0 14/0 LCPI
044/0 770/0 103/0 079/0 LPGW
177/0 36/1 827/2 85/3 C
Based on the results of the study,
*) The negative sign of LOCAL coefficient suggests that in long time, a change in the unit of the price of oil reduces gold price about 0.98.
*) The coeffitiont of interest rate is negative in long time. In fact, each unit change in this variable decreases the gold price about 0.25.
*) Exchange rate has a negative effect on gold price in long-term.
*) The coeffitiont of price index (inflation) has positive impact on gold price in long time.
*) The negative sign of World gold prices suggests that in long time, a unit change in the price of gold decreases the gold price about 0.079.
*) The Error correction coeffitiont is statisticaly significant; therefore, there is a co- integration relationship between the variables in the long time. The result of the Error correction estimation are reported in the following Table.
significant level T statistic Standard deviation Coefficients variables
000/0 906/5- 137/0 814/0- ECM (-1)
This study examines the factors affecting gold price in the quarterly time series data between 1991 and 2010, and Auto Regressive Distributed Lag (ARDL) method is used. The results of this study indicate that short-time and long-time variations in oil prices, interest rates, and exchange rates have negative and significant impact on gold prices variable. In addition, global gold price and inflation have positive and significant impact on gold prices.