Final Dessert at Ion Report on Gold Prices 13.01

8/8/2019 Final Dessert at Ion Report on Gold Prices 13.01

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DISSERTATION RESARCH PROPOSAL

ON

FACTORS INFLUENCES GOLD SPOT PRICESAND RELATIONSHIP AMONG THEM

SUBMITTED TO:

NAWAZ AHMEDFACULTY OF MANAGEMENT SCIENCES

GREENWICH UNIVERSITY, KARACH

BY

AMIR ALI SALIM HUSSEIN (MS22-1672)

Factors Influences Gold Spot Prices And Relationship Among Them 1


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1. Introduction:

Generally speaking, an increasing oil price results in increasing inflation,

negatively impact the global economy, particularly oil-dependent economies such

as the US. Apart from increased transportation, heating and utility costs, higher

oil prices are eventually reflected in virtually every finished product, as well as

food and commodities in general. Furthermore, there is evidence that global oil

production is peaking and the flow will soon be in permanent decline.

The majority of oil reserves are located in politically unstable regions, with

tensions in the Middle East, Venezuela and Nigeria likely to intensify rather than

to abate. Because of frequent terrorist attacks, Iraqi oil production is subject to

disruption, while the risk of political problems in Saudi Arabia grows. The timing

for these risks is uncertain and hard to quantify, but the implications of Peak Oil

are predictable and quantifiable, and the effects will be more far-reaching than

simply a rising oil price.

Using analytic techniques based on Hubbert's work, oil and gas experts now

project that world oil production will peak sometime in the latter half of this

decade. We are now depleting global reserves at an annual rate of 6 percent,while demand is growing at an annual rate of 2 percent (and that growth rate is

expected to triple over the next 20 years). This means we must increase world

reserves by 8 percent per annum simply to maintain the status quo, and we are

nowhere near achieving that goal. In fact, we are so far from it that, according to

Dr. Colin Campbell, one of the world's leading geologists, the world consumes

four barrels of oil for every one it discovers.

Oil, gold and commodities have all been priced in US dollars since 1975 when

OPEC officially agreed to sell its oil exclusively for US dollars. From 1944 until

1971, US dollars were convertible into gold by central banks in order to adjust for

any trade imbalances between countries. Up to that point, the price of gold was

fixed at USUSD35 per ounce, and the price of oil was relatively stable at about



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2. Research Statement and Hypothesis:

Investigations will be aimed at gaining a better understanding regarding the

relationship between Gold Price, Oil Prices, exchange rate and interest rate. The

study is an attempt to develop a stochastic relationship between them. This

research will eventually help the investors to hedge against inflated prices.

2.1. Research Hypothesis and Objectives:

Research Work will conduct aim to prove following hypothesis:

HypothesisHo1 : There is an association between Oil Prices and Gold Prices

Ho2 : There is an association between Exchange Rate and Gold Prices

Ho3 : There is an association between Interest Rate and Gold Prices



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3. Literature Review

Wealth of literature in the form of books, journals and articles is available on the

research topic and the researcher will able to heavily draw from it. Going through

the literature will not only enable the researcher to develop the theoretical

framework surrounding the study of Relationship of Oil Prices, Gold Spot Prices,

Exchange rate, and interest rate but this study also will help to further refine the

research objectives.

According to Claire Lunieski, 2009, the significance of the residual as a measure

of monetary policy uncertainty suggests that on a broader scale the volatility of

gold prices responds to macroeconomic shifts. For instance, recessionary

periods typically experience higher rates of monetary policy uncertainty because

investors and traders cannot always predict the depth or length of the recessions.

Due to its role as a financial safe haven, investors are known to rush toward gold

during recessions, causing prices to move (Hammoudeh and Yuan 2008). Thus

increased monetary policy uncertainties would be expected to contribute to price

movements during recessions. Likewise, because the prediction error of the fed

funds futures rate has improved over the past two decades and the models

shows increased errors causing increased volatility, we can deduce thatmonetary policy could be expected to have contributed to a reduction in

fluctuations in gold futures market. Although this GARCH model does not find

monetary policy uncertainties to predict gold futures prices well, it does model the

prediction error as a determinant in the volatility of gold. Therefore, uncertainties

in monetary policy are an important element in understanding movements in gold

futures markets.

Likewise, oil shocks are positive at the ten percent level for the mean equation

and the one percent level in the variance equation. Thus, it can be deduced that

movements in oil prices create more volatile gold markets but may not have the

same strength in raising the current term price. Although oil shocks may pass-

through onto gold prices, they are a more compelling determinant of the variance

in gold prices. Oil shocks are closely related to rising headline inflation (Baffes



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2007). Given golds role as an inflation hedge, it is not surprising that greater

volatility in oil causes a growth in the variance of gold prices. From this model we

can deduce that a price shock on oil will cause investors to hedge inflation, thus

moving the price of gold. Additionally, we can logically conclude that oil shocks

may affect gold prices and, like monetary policy uncertainties, significantly

increase the volatility of gold futures prices.

Ugur Soytas, December 2009, examines the long- and short-run transmissions of

information between the world oil price, Turkish interest rate, Turkish liraUS

dollar exchange rate, and domestic spot gold and silver price. We find that the

world oil price has no predictive power of the precious metal prices, the interest

rate or the exchange rate market in Turkey. The results also show that theTurkish spot precious metals, exchange rate and bond markets do not also

provide information that would help improve the forecasts of world oil prices in the

long run. The findings suggest that domestic gold is also considered a safe haven

in Turkey during devaluation of the Turkish lira, as it is globally. It is interesting to

note that there does not seem to be any significant influence of developments in

the world oil markets on Turkish markets in the short run either. However,

transitory positive initial impacts of innovations in oil prices on gold and silver

markets are observed. The short-run price transmissions between the world oil

market and the Turkish precious metal markets have implications for policy

makers in emerging markets and both local and global investors in the precious

metals market and the oil market.

According to Ugur Soytas, June 2009, based his research on importance of oil

prices and the real exchange rate for Russia with its impact on Gold prices and

countrys fiscal policy by using vector autoregressive (VAR) modeling and co-

integration techniques. The results of the study imply that the Russian economy

and Gold prices are influenced significantly by fluctuations in oil prices, and the

real exchange rate through both long-run equilibrium conditions and short-run

direct impacts. According to author, although the underlying growth trend



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indicates the Russian economy has strengthened in recent years, we find no

evidence that the role of oil prices has diminished.

Siaastad, Larry A., Scacciavillani, Fabio, (1996), conducted a study on the

theoretical relationship between the major exchange rates and internationally-traded commodity prices. The case of gold was examined using forecast error

data. The results indicate that floating exchange rates among the major

currencies have been a key source of price instability in the world gold market

since the dissolution of the Bretton Woods International monetary system.

Furthermore, movements of European currency prices have significant effects on

the price of gold in other currencies.

H. W. Mui, C. W. Chu, (1993), examines the fluctuation of the gold price has

significant impact on the economic and social aspects of a society. In the

literature, most authors have employed fundamental analysis approach in

forecast model building. The basic principle underlying this approach is that it is

the supply and the demand which simultaneously determines the gold price.

However, due to the lack of data of quantity supplied and quantity demanded,

simultaneous econometric approach seems unsuccessful. In this paper,

combined and composite time series forecasting techniques are proposed. The

effects of various economic factors towards spot price of gold are also examined.

Among the combined forecasting models, it seems that the odds-matrix method

of assigning weights provides the most accurate forecasts of spot price of gold.

For the economic factors considered, the futures price of gold and the exchange

rate seem to be most informative in forecasting the spot price of gold.

One of the pioneer studies was due to Lipschitz, Otani (1977), who developed a

quarterly econometric model to predict the gold price. They contended that since

gold stocks are large relative to annual flows, gold prices will adjust when

investors absorb the existing excess supply. Also, they assumed a portfolio



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balance model which related the demand for gold to the rates of return on other

financial assets, the rate of inflation and the expected price of gold.

Abken, (1980), provided evidence that changes in gold price are consistent not

with 'psychology' but with economic rationale. He related the expected price

change in gold to the marginal cost of holding gold. He contended that the stocks

of gold substantially outweigh the flows of gold but maintained that both flow

supply and flow demand are relatively insensitive to changes in gold price.

Another significant research on the gold market is due to Sherman, (1983). In his

econometric single-equation model, Sherman selected six variables, namely,

tension index, real Eurodollar rate, US trade balance, weighted exchange rate,real GNPIGDP (world), liquidity and unanticipated inflation. However, results of

the model reflected that serious multi-co linearity exists between interest rate,

exchange rate, tension index and unanticipated inflation.



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4. Methodology

The main components to this research are specification of a theory, aim based on

the theory, execution, and evaluation. This work addresses the important general

issue of movement Gold price in comparison with Oil prices, Exchange rate and

Interest rate and how these commodities relate to each other. The goal is to

develop a theory that describes the relation between the prices of the two

scarcest resources of the world i.e. Gold and Oil and relationship of Gold prices

with Exchange rate and Interest rate.

The proposed theory and implementation have many potential applications. It will

allow the investors that usually invest in commodities market, to get the better

picture about the movement of prices, and in Exchange rate, Interest

rate(KIBOR) and helps them to anticipate the further activity in the commodities.

So they can invest accordingly.

The approach that we will apply for conducting the research is deductive

approach. Past experiences are being evident of this fact that the prevailing

relationship among these variable is very strong. Since deductive research isquicker to complete, it facilitated to make the time schedule and follow it more

accurately. On the other hand, the inductive research approach can have been

much lengthy since the theory building often requires much longer period of data

collection and analysis and the ideas emerge only gradually. This approach was

also low-risk one, while with inductive research approach- fundamental

analysis , one has continuously to live with the fear that no useful data and theory

will emerge.

The strategy that we will use is based upon data collection and then applying

research on that, it means researchers focus will on technical analysis . We will

take the past historic data regarding gold prices, oil prices, Exchange rate and

Interest rate in term of PKR. We will take some semi-structured interview in order



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to gain in depth knowledge regarding the research subject. We will also take into

consideration some cases to support our findings. Case study as a research

strategy provides the researcher with a systematic way of looking at events,

collecting data, analyzing information and reporting the results. As a result the

researcher will get a sharpened understanding of why the instance happened as

it did, and what become to look at more extensively in future research.



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5. Data:

1.1. Secondary Data Collection

Historical data has also taken into calculations in order to find the relationship

among Gold Spot Prices, Oil Prices, and dollar value.Primary empirical data will support by the secondary comprising three

comprehensive reference cases that boosts the researchers insight over the

issue and help conduct a far-reaching analysis and debate.

2.2. Variables: Spot Gold Prices

Oil Prices

Exchange rate USD in term of Pak Rupee

KIBOR

5.3. Model:

1.1. Model: Multivariate Regression Analysis

Multivariate regression analysis is an extension of Bi-variate regression analysis,which allows for the simultaneous investigation of the effect of two or more

independent variables on a single interval-scaled(Metric Measurement scale)

dependent variable.

We will perform multivariate data analysis technique through dependence method

with metric measurement scale.



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1.1.1. Model Overview

= + 1X1+ 2X2+ 3X3

Gold Price = a + b 1(Oil Prices) + b 2(Exchange Rate) + b 3(KIBOR) + c

Y is the dependent variable and X is the independent variable, represent the Y

intercept and is the slope co-efficient. The slope is the change in Y due to

corresponding change of one unit in X. The slope may be thought of as rise

over run.

Multivariate statistical methods allow the effects of more that one variable to be

considered at one time.

For, example, suppose a forecaster wished to estimate oil consumption for the

next five years, while consumption might by predicted by past oil consumption

record alone, adding additional variables such as average number of miles driven

per year, coal production, and nuclear plants under construction is likely to give

greater insight into the determinants of oil consumption.

Another forecasting example is useful in illustrating multiple regression.

Assume a toy manufacturer wishes to forecast sales by sales territory. It isthought that competitors sales, the presence or absence of company sales

person in the territory(a binary variable), and grammar school enrollment are the

independent variables that might explain the variation in the sales of toy.

In multiple regression the coefficients 1, 2 and so are coefficients of partial

regression because the independent variables are usually correlated with the

other independent variables. The Correlation between Y and X 1, with the

correlation that X 1 and X 2 have in common with Y held constant, is the partial

correlation.

R 2 Coefficient of multiple determination indicates the percentage of variation in

Y explained by the variation in the independent variables.



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5.3.2. Model: Correlation

Co relational research involves collecting data in order to determine whether, and

to what degree, a relationship exists between two or more quantifiable variables.

This degree of relationship between two or more quantifiable variables is

expressed as a correlation coefficient.

If a relationship exists between two variables, it means that scores within a

certain range on one measure are associated with scores within a certain range

on another measure.

1.1. Model Understanding:

When two variables are correlated the result is a correlation coefficient. A

correlation coefficient is a decimal number, between .00 and +1.00, or between .

00 and -1.00. If the variables are positively related, the coefficient is between .

00 and +1.00.

For example, there is a relationship between intelligence and academic

achievement; persons who do well on intelligence tests tend to have higher GPA,and persons who do poorly on intelligence tests tend to have lower GPA. This

does not mean, of course, that all persons with high intelligence get good grades;

it means that overall there is a relationship between the two measures.

This means that a person with a high score on one variable is likely to have a

high score on the other variable, and a person with a low score on one is likely to

have a low score on the other an increase in one variable is associated with an

increase in the other variable and vice versa (relationship between aptitude test

score and performance rating).

If the variables are negatively related, the coefficient is between .00 and -1.00.



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This means that a person with a high score on one variable is likely to have a low

score on the other an increase in one variable is associated with a decrease in

the other variable, and vice versa (relationship between aptitude test score and

errors in accounting test). And, if two variables are not related, a coefficient

near .00 will be obtained.

This means that a persons score on one variable is no indication of what the

persons score is on the other variable (relationship between aptitude test score

and weight). The further a coefficient moves from .00, in either direction (toward

+1.00 or 1.00), the stronger the relationship will be. Correlation coefficient with

value + 1.00 (or 1.00) will be the strongest ones.

An important concept often misunderstood by beginning researchers, namely that

a high negative relationship is just as strong as a high positive relationship; -1.00

and +1.00 indicate equally perfect relationships.

High positive and high negative relationships are equally useful for making

predictions; knowing that a student has a low aptitude test score would enable

you to predict both a low performance rating and a high number of errors.

Correlation study is about a relationship only, not a cause-effect relationship. A

significant correlation coefficient may suggest a cause-effect relationship but

does not establish it. Rather, the only way to establish a cause-effect relationship

is by conducting an experiment.

According to Dr Cheema, June 2008, though it is often very tempting to conclude

that one causes the other when one finds a high relationship between two

variables, care must be taken in interpreting and using correlational studies to

avoid suggesting that cause-effect relationships exist or do not exist based upon

correlational results. Rather, correlational research must be treated as a type of

descriptive research primarily because it describes an existing condition.



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Data gathered through inferences drawn from the secondary data and the

calculations performed on the historical data will be integrated with the help

SPSS Software .

The study will also extend its scope through technical analysis with the use of

graphs of Time-series, Pie chart, Bar chart, Moving average, Means Plots, P-plot,

and with the help of Metatex software to extend the research horizon and to

prove research hypothesis.



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Bibliography

1. http://www.wtrg.com/prices.htm

2. Claire Lunieski, (2009), Commodity Price volatility and MonetaryPolicy Uncertainty : A GARCH Estimation. Issues in Political Economy,Vol 19, 2009, pp. 108-1243. Ugur Soytas, 2009, Turkish interest rate, Turkish liraUS dollar exchange rate, and domestic spot gold and silver price. Energy policyJournal, Vol. 37(12), December 2009, pp. 5557-5566.4. Ugur Soytas, 2009, A Co-integration approach. ComparativeEconomics Journal, Vol. 32(2), June 2009, pp. 315-327.5. Sjaastad, Larry A.m Scacciavillani, Fabio, (1996), InternationalMoney and Finance Journal, Butterworth-Heinemann Ltd. Publisher 6. H.W. Mui, C.W.Chu., 1993, Forecasting the Spot Price of Gold:Combined Forecast Approach Versus a Composite Forecast Approach,Applied Statistics Journal, Vol 20(1), pp. 13-237. Lispschitsz L, Otani I., (1977), A Simple Model of Private GoldMarket 1968-1974: An Exploratory Econometric Exercise, InternationalMonetary Fund Staff Papers, 24 March 1977, pp. 36-63.8. Shermane. J. (1983), A Gold Pricing Model., Journal of journal of Portfolio Management, 9 (Spring), pp. 67-80.9. Abken , P. A. (1980) The economics of gold price movements,

Economic Review , Vol. .66, April, 1980, pp. 313.10. Dr. Farooq-e-Azam Cheema, June 2008, Correlational Studiespresentation Slides.11. The Federal Reserve Board12. URL: http://www.federalreserve.gov/fomc/fundsrate.htm13. Bloomberg Software

http://www.wtrg.com/prices.htmhttp://www.wtrg.com/prices.htm

Final Dessert at Ion Report on Gold Prices 13.01

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