To date, nobody has formulated a comprehensive theorem to determine gold valuation or precious metal prices. Until fairly recently, Eugene Fama’s Efficient Market Hypothesis was the predominant paradigm explaining asset markets but today it is widely acknowledged that markets can be irrational and investors are prone to act irrationally. When trying to explain gold market anomalies, behavioural science approaches can be useful. Phenomena such as herding (‘group think’), ‘safe value bias’ and investors’ ‘excessive extrapolation’ can help explain positive price performance over a certain time.
In this dissertation, the author investigates the applicability of a multivariate ARIMA (auto-regressive, integrated, moving average) model to help explain gold price movements from 1973 to 2011. This model uses the gold price and independent variables such as inflation, real interest rates, silver prices, the US dollar money supply (M2), oil prices, the MSCI World index and the S&P 500 as these are linked to gold and/or highly correlated with the gold price. The evaluation criteria were defined as R-squared, mean absolute percentage error (MAPE) and BIC. The model was calculated over so-called ‘normal times’ and times of crises (one political, one financial). The researcher used SPSS’ Expert Modeler to find the best-fitting ARIMA model and to identify the independent variables significantly contributing to the fit of the model. Remarkably, a multivariate ARIMA model using independent variables explained almost twice as much of the variability of the gold price as a univariate ARIMA model using only the gold price. Also, throughout the complete period and during normal times the model explained a much higher percentage of the variability of the gold price than during crises and comparably more of the independent variables contributed significantly to the fit of the model (5 vs. 2). This can be explained by investors’ tendencies to buy gold to preserve their assets (“safe value”), to follow the crowd (“herding”) and to extrapolate past price chart developments.
The results show that in an attempt to discern the cause of gold price movements, a multivariate ARIMA model outperforms a univariate ARIMA model significantly. The results of the study furthermore indicate researchers evaluating different methods to fit a time series should consider a multivariate ARIMA model, especially if the independent variables are highly correlated with the dependent variable.
Table of Contents
Chapter 1 – Introduction
1.1 Background
1.2 Gold is different
1.3 The gold price since the end of Bretton Woods
1.4 Research questions
Chapter 2 – Literature Review and Theory
2.1 Theoretical framework: Explaining the movements of the gold price
2.2 Empirical findings: Independent variables correlating with the gold price
2.3 Conclusion
Chapter 3 – Data and Methods
3.1 The ARIMA model
3.2 Assumptions of an ARIMA model
3.3 Data collection and sources
3.4 Defining an ARIMA model to fit the gold price
3.5 Evaluation of the ARIMA model
3.6 Conclusion
Chapter 4 – Analysis and Results
4.1 Data description
4.2 The best fitting ARIMA model
4.3 ARIMA model fit during normal times and crises
4.4 Explaining divergences of the model fit during normal times and crises
4.5 Conclusion
Chapter 5 – Discussion and Conclusions
5.1 Summary
5.2 Implications
5.3 Limitations
5.4 Direction for Future Research
5.5 Reflections
Objectives and Research Themes
This dissertation investigates the applicability of a multivariate ARIMA model to explain gold price movements from 1973 to 2011, testing whether the inclusion of various macroeconomic independent variables enhances the explanatory power of the model compared to univariate approaches during both normal periods and crises.
- Application of multivariate ARIMA modeling for gold price forecasting.
- Analysis of correlations between gold and macroeconomic variables (inflation, interest rates, oil, stocks, M2).
- Comparative performance evaluation of the model during "normal" times versus periods of financial or political crisis.
- Examination of behavioural finance theories, such as herding and "safe value" bias, to explain model divergences.
Excerpt from the Book
1.2 Gold is different
Gold is not like other metals because its industrial use is negligible, which makes it different to other commodities such as zinc, copper or silver. This explains why the price of gold often moves differently than the price of other commodities during a recession or a depression and especially during periods of high inflation (World Gold Council 2011, p. 8). The gold supply is primarily absorbed in the production of jewellery, by central banks, investors and more recently by financial institutions offering gold ETFs (Shafiee and Topal 2010, p.178). Gold is also special because of its distinctive place in economic history and its use as a financial asset, in particular as a hedge against inflation and geopolitical and/or economic risk. Many individuals add gold to their portfolios as a risk diversifier (Dempster 2008, p. 5).
Chapter Summary
Chapter 1 – Introduction: Outlines the significance of gold as a unique asset class and introduces the study's objective to develop a multivariate ARIMA model for analyzing gold price movements since 1973.
Chapter 2 – Literature Review and Theory: Reviews existing theories on gold valuation, market efficiency, and behavioral finance, while identifying key independent variables correlated with the gold price.
Chapter 3 – Data and Methods: Details the ARIMA modeling approach, the rationale for selecting specific macroeconomic variables, and the methodology used for data collection and model evaluation.
Chapter 4 – Analysis and Results: Presents the descriptive statistics, model fit results, and a comparison of the model's performance during normal times versus identified crisis periods.
Chapter 5 – Discussion and Conclusions: Summarizes the research findings, discusses the implications for market efficiency theory, acknowledges study limitations, and suggests areas for future research.
Keywords
Gold price, ARIMA model, Multivariate analysis, Macroeconomic variables, Financial crisis, Efficient Market Hypothesis, Behavioral finance, Inflation hedge, Safe haven, Time series analysis, Gold valuation, Market irrationality, Financial volatility, Money supply, Investor psychology
Frequently Asked Questions
What is the primary focus of this dissertation?
The dissertation examines how well gold price movements since the end of the Bretton Woods era can be explained using a multivariate ARIMA model that incorporates various macroeconomic independent variables.
What independent variables are tested in the model?
The model tests inflation, real interest rates, silver prices, US dollar money supply (M2), oil prices, the MSCI World index, and the S&P 500.
What is the central research question?
The central question is how effectively a multivariate ARIMA model, using specific financial and economic variables, can explain gold price variations, evaluated through criteria like R-squared and mean absolute percentage error (MAPE).
What methodology is employed to analyze the data?
The author uses an ARIMA (autoregressive, integrated, moving average) time series model, utilizing SPSS Expert Modeler to identify the best-fitting model and statistically significant predictors.
What is covered in the main body of the work?
The work covers the theoretical framework of gold markets, the data and methods for model construction, the empirical analysis of model performance, and a discussion on why model fit varies between normal periods and times of crises.
What are the main keywords characterizing this study?
The study is characterized by terms such as gold price, ARIMA model, multivariate analysis, market efficiency, and behavioral finance.
How did the model perform during periods of crisis?
The analysis found that the model explained significantly less variability during crises compared to normal times, suggesting that behavioral factors like fear and herding become more dominant than fundamental economic drivers.
Why did the author compare "normal" times with "crises"?
The author aimed to identify whether irrational investor behavior during crises—such as fleeing to "safe haven" assets—leads to a divergence in the predictive power of macroeconomic variables compared to stable market conditions.
- Arbeit zitieren
- Stefan Heini (Autor:in), 2014, Explaining the gold price after the Bretton Woods Agreement using independent variables. An ARIMA model approach, München, GRIN Verlag, https://www.grin.com/document/304522
