Gold and the Stock Market: 3 Essays on Gold Investments

This thesis gives an overview of the history of gold per se, of gold as an investment good and offers some institutional details about gold and other precious metal markets. The goal of this study is to investigate the role of gold as a store of value and hedge against negative market movements in turbulent times. I investigate gold’s ability to act as a safe haven during periods of financial stress by employing instrumental variable techniques that allow for time varying conditional covariance. I find broad evidence supporting the view that gold acts as an anchor of stability during market downturns. During periods of high uncertainty and low stock market returns, gold tends to have higher than average excess returns. The effectiveness of gold as a safe haven is enhanced during periods of extreme crises: the largest peaks are observed during the global financial crises of 2007-2009 and, in particular, during the Lehman default (October 2008). A further goal of this thesis is to investigate whether gold provides protection from tail risk. I address the issue of asymmetric precious metal behavior conditioned to stock market performance and provide empirical evidence about the contribution of gold to a portfolio’s systematic skewness and kurtosis. I find that gold has positive coskewness with the market portfolio when the market is skewed to the left. Moreover, gold shows low cokurtosis with the market returns during volatile periods. I therefore show that gold is a desirable investment good to risk averse investors, since it tends to decrease the probability of experiencing extreme bad outcomes, and the magnitude of losses in case such events occur. Gold thus bears very important and under-researched characteristics as an asset class per se, which this thesis contributed to address and unveil.

Understanding the impact of Twitter on the financial market

Negli anni la funzione dei social network è cambiata molte volte. Alle origini i social network erano uno strumento di connessione tra amici, ora sono siti internet in cui le persone mettono informazioni e quando un social network ha milioni di utenti, diventa un’incredibile sorgente di dati. Twitter è uno dei siti internet più visitati, e viene descritto come “the SMS of internet”, perchè è un social network che permette ai suoi utenti di inviare e leggere messaggi corti, di 140 caratteri, chiamati “tweets”. Con il passare del tempo Twitter `e diventato una fonte fondamentale di notizie. Il suo grande numero di utenti permette alle notizie di espandersi nella rete in modo virale. Molte persone hanno cercato di analizzare il potere dei tweet, come il contenuto positivo o negativo, mentre altri hanno cercato di capire se avessero un potere predittivo. In particolare nel mondo finanziario, sono state avviate molte ricerche per verificare l’esistenza di una effettiva correlazione tra i tweets e la fluttuazione del mercato azionario. L’effettiva presenza di tale relazione unita a un modello predittivo, potrebbe portare allo sviluppo di un modello che analizzando i tweets presenti nella rete, relativi a un titolo azionario, dia informazioni sulle future variazioni del titolo stesso. La nostra attenzione si è rivolata alla ricerca e validazione statistica di tale correlazione. Sono stati effettuati test su singole azioni, sulla base dei dati disponibili, poi estesi a tutto il dataset per vedere la tendenza generale e attribuire maggior valore al risultato. Questa ricerca è caratterizzata dal suo dataset di tweet che analizza un periodo di oltre 2 anni, uno dei periodi più lunghi mai analizzati. Si è cercato di fornire maggior valore ai risultati trovati tramite l’utilizzo di validazioni statistiche, come il “permutation test”, per validare la relazione tra tweets di un titolo con i relativi valori azionari, la rimozione di una percentuale di eventi importanti, per mostrare la dipendenza o indipendenza dei dati dagli eventi più evidenti dell’anno e il “granger causality test”, per capire la direzione di una previsione tra serie. Sono stati effettuati anche test con risultati fallimentari, dai quali si sono ricavate le direzioni per i futuri sviluppi di questa ricerca.

Formation of Exchange Rates on the Russian Market: Dynamics and Modelling

Galina Kovaleva. The Formation of the Exchange Rate on the Russian Market: Dynamics and Modelling. The Russian financial market is fast becoming one of the major sectors of the Russian economy. Assets have been increasing steadily, while new market segments and new financial market instruments have emerged. Kovaleva attempted to isolate the factors influencing exchange rates, determine patterns in the dynamic changes to the rouble/dollar exchange rate, construct models of the processes, and on the basis of these activities make forecasts. She studied the significance of economic indicators influencing the rouble/dollar exchange rate at different times, and developed multi-factor econometric models. In order to reveal the inner structure of the financial indicators and to work out ex-post forecasts for different time intervals, she carried out a series of calculations with the aim of constructing trend-cyclical (TC) and harmonic models, and Box and Jenkins models. She found that: 1. The Russian financial market is dependant on the rouble/dollar exchange rate. Its dynamics are formed under the influence of the short-term state treasury notes and government bonds markets, interbank loans, the rouble/DM exchange rate, the inflation rate, and the DM/dollar exchange rate. The exchange rate is influenced by sales on the Moscow Interbank Currency Exchange and the mechanism of those sales. 2. The TC model makes it possible to conduct an in-depth study of the structure of the processes and to make forecasts of the dynamic changes to currency indicators. 3. The Russian market is increasingly influenced by the world currency market and its prospects are of crucial interest for the world financial community.

Price risk management in the copper market using commodity derivatives and options strategies

Metals price risk management is a key issue related to financial risk in metal markets because of uncertainty of commodity price fluctuation, exchange rate, interest rate changes and huge price risk either to metals’ producers or consumers. Thus, it has been taken into account by all participants in metal markets including metals’ producers, consumers, merchants, banks, investment funds, speculators, traders and so on. Managing price risk provides stable income for both metals’ producers and consumers, so it increases the chance that a firm will invest in attractive projects. The purpose of this research is to evaluate risk management strategies in the copper market. The main tools and strategies of price risk management are hedging and other derivatives such as futures contracts, swaps and options contracts. Hedging is a transaction designed to reduce or eliminate price risk. Derivatives are financial instruments, whose returns are derived from other financial instruments and they are commonly used for managing financial risks. Although derivatives have been around in some form for centuries, their growth has accelerated rapidly during the last 20 years. Nowadays, they are widely used by financial institutions, corporations, professional investors, and individuals. This project is focused on the over-the-counter (OTC) market and its products such as exotic options, particularly Asian options. The first part of the project is a description of basic derivatives and risk management strategies. In addition, this part discusses basic concepts of spot and futures (forward) markets, benefits and costs of risk management and risks and rewards of positions in the derivative markets. The second part considers valuations of commodity derivatives. In this part, the options pricing model DerivaGem is applied to Asian call and put options on London Metal Exchange (LME) copper because it is important to understand how Asian options are valued and to compare theoretical values of the options with their market observed values. Predicting future trends of copper prices is important and would be essential to manage market price risk successfully. Therefore, the third part is a discussion about econometric commodity models. Based on this literature review, the fourth part of the project reports the construction and testing of an econometric model designed to forecast the monthly average price of copper on the LME. More specifically, this part aims at showing how LME copper prices can be explained by means of a simultaneous equation structural model (two-stage least squares regression) connecting supply and demand variables. A simultaneous econometric model for the copper industry is built: {█(Q_t^D=e^((-5.0485))∙P_((t-1))^((-0.1868) )∙〖GDP〗_t^((1.7151) )∙e^((0.0158)∙〖IP〗_t ) @Q_t^S=e^((-3.0785))∙P_((t-1))^((0.5960))∙T_t^((0.1408))∙P_(OIL(t))^((-0.1559))∙〖USDI〗_t^((1.2432))∙〖LIBOR〗_((t-6))^((-0.0561))@Q_t^D=Q_t^S )┤ P_((t-1))^CU=e^((-2.5165))∙〖GDP〗_t^((2.1910))∙e^((0.0202)∙〖IP〗_t )∙T_t^((-0.1799))∙P_(OIL(t))^((0.1991))∙〖USDI〗_t^((-1.5881))∙〖LIBOR〗_((t-6))^((0.0717) Where, Q_t^D and Q_t^Sare world demand for and supply of copper at time t respectively. P(t-1) is the lagged price of copper, which is the focus of the analysis in this part. GDPt is world gross domestic product at time t, which represents aggregate economic activity. In addition, industrial production should be considered here, so the global industrial production growth that is noted as IPt is included in the model. Tt is the time variable, which is a useful proxy for technological change. A proxy variable for the cost of energy in producing copper is the price of oil at time t, which is noted as POIL(t ) . USDIt is the U.S. dollar index variable at time t, which is an important variable for explaining the copper supply and copper prices. At last, LIBOR(t-6) is the 6-month lagged 1-year London Inter bank offering rate of interest. Although, the model can be applicable for different base metals' industries, the omitted exogenous variables such as the price of substitute or a combined variable related to the price of substitutes have not been considered in this study. Based on this econometric model and using a Monte-Carlo simulation analysis, the probabilities that the monthly average copper prices in 2006 and 2007 will be greater than specific strike price of an option are defined. The final part evaluates risk management strategies including options strategies, metal swaps and simple options in relation to the simulation results. The basic options strategies such as bull spreads, bear spreads and butterfly spreads, which are created by using both call and put options in 2006 and 2007 are evaluated. Consequently, each risk management strategy in 2006 and 2007 is analyzed based on the day of data and the price prediction model. As a result, applications stemming from this project include valuing Asian options, developing a copper price prediction model, forecasting and planning, and decision making for price risk management in the copper market.

Cloud Computing in the EU Policy Sphere Interoperability, Vertical Integration and the Internal Market

Cloud computing is a new development that is based on the premise that data and applications are stored centrally and can be accessed through the Internet. Thisarticle sets up a broad analysis of how the emergence of clouds relates to European competition law, network regulation and electronic commerce regulation, which we relate to challenges for the further development of cloud services in Europe: interoperability and data portability between clouds; issues relating to vertical integration between clouds and Internet Service Providers; and potential problems for clouds to operate on the European Internal Market. We find that these issues are not adequately addressed across the legal frameworks that we analyse, and argue for further research into how to better facilitate innovative convergent services such as cloud computing through European policy – especially in light of the ambitious digital agenda that the European Commission has set out.