Syftet med denna uppsats är att undersöka Bitcoins egenskaper och funktioner samt diskutera dess eventuella påverkan på dagens ekonomiska system. Den forskning som publicerats på ämnet undersöker främst tekniska och säkerhetsmässiga detaljer, det är därför intressant att analysera ur en ekonomisk synvinkel. Genom att använda makroekonomisk teori ska jag försöka klargöra om Bitcoin kan konkurrera med dagens valutor. Bitcoins volatilitet i värde gör det tvivelaktigt, en volatilitet som inte kan kontrolleras. Bitcoin uppfyller delar av de funktioner och egenskaper som definierar en valuta enligt makroekonomisk teori, men inte lika många som fiatpengar och Bitcoin bör därför inte kunna konkurrera med dagens valutor. Fiatpengar är att föredra även i andra områden som acceptans och säkerhet. Bitcoin är bättre ur ett kostnadsperspektiv, dock under specifika omständigheter som sällan uppfylls. Bitcoin kommer därför tills vidare fungera som ett alternativ betalningssystem och valuta för personer och företag som är villiga att ta de risker som medföljer. / The aim of this thesis is to investigate Bitcoins attributes and functions and discuss its effect on today’s economic system. The academic work that been published on the subject investigate technical and safety aspects, it is therefore interesting to analyse from a macroeconomic point of view. By using macroeconomic theory I will try to clarify if Bitcoin can compete with today’s currencies. Bitcoins volatility in value makes it doubtful. Bitcoins volatility in value makes it questionable, a volatility that cannot be controlled. Bitcoin meet part of the functions and characteristics that define a currency as macroeconomic theory, but not as many as fiatmoney and should therefore not be able to compete with today's currencies. Fiatmoney is preferable also in other areas such as acceptance and security. Bitcoin is better from a cost perspective, but under specific circumstances that are rarely met. Bitcoin will therefore until further serve as an alternative payment system and currency individuals and companies that are willing to take the risks included.

GARCH models have been widely used in finance to model volatility ever since the introduction of the ARCH model and its extension to the generalized ARCH (GARCH) model. Lately, there has been growing interest in modelling seasonal volatility, most recently with the introduction of the multiplicative seasonal GARCH models. As an application of the multiplicative seasonal GARCH model with real data, call prices from the major stock market index of India are calculated using estimated parameter values. It is shown that a multiplicative seasonal GARCH option pricing model outperforms the Black-Scholes formula and a GARCH(1,1) option pricing formula. A parametric bootstrap procedure is also employed to obtain an interval approximation of the call price. Narrower confidence intervals are obtained using the multiplicative seasonal GARCH model than the intervals provided by the GARCH(1,1) model for data that exhibits multiplicative seasonal GARCH volatility.

<p>This dissertation explores issues regarding the short-lived temporal variation of the equity risk premium. In the past decade, the equity risk premium puzzle is resolved by many competing consumption-based asset pricing models. However, before \cite{btz:vrp:rfs}, the return predictability as an outcome of such models has limited empirical support in the short-run. Nowadays, there has been a consensus of the literature that the short-run equity return's predictability is intimately linked with the variance risk premium---the difference between options-implied and actual realized variation measures.</p><p>In this work, I continue to argue the importance of the short-lived components in the equity risk premium. Specifically, I first provide simulation evidence of the strong return predictability based on the variance risk premium in the U.S. aggregate market, and document new empirical findings in the international setting. Then I attempt to use a structural macro-finance model to guide through the predictability estimation with much more efficiency gain. Finally I decompose the equity risk premium into two short-lived parts --- tail risk and diffusive risk --- and propose a semi-parametric estimation method for each part. The results are arranged in the following order.</p><p>Chapter 1 of the dissertation is co-authored with Tim Bollerslev, James Marrone and Hao Zhou. In this chapter, we demonstrate that statistical finite sample biases cannot ``explain'' this apparent predictability in U.S. market based on variance risk premium. Further corroborating the existing evidence of the U.S., we show that country specific regressions for France, Germany, Japan, Switzerland, the Netherlands, Belgium and the U.K. result in quite similar patterns. Defining a ``global'' variance risk premium, we uncover even stronger predictability and almost identical cross-country patterns through the use of panel regressions. </p><p>Chapter 2 of the dissertation is co-authored with Tim Bollerslev and Hao Zhou. In this chapter, we examine the joint predictability of return and cash flow within a present value framework, by imposing the implications from a long-run risk model that allow for both time-varying volatility and volatility uncertainty. We provide new evidences that the expected return variation and the variance risk premium positively forecast both short-horizon returns \textit{and} dividend growth rates. We also confirm that dividend yield positively forecasts long-horizon returns, but that it does not help in forecasting dividend growth rates. Our equilibrium-based ``structural'' factor GARCH model permits much more accurate inference than %the reduced form VAR and</p><p>univariate regression procedures traditionally employed in the literature. The model also allows for the direct estimation of the underlying economic mechanisms, including a new volatility leverage effect, the persistence of the latent long-run growth component and the two latent volatility factors, as well as the contemporaneous impacts of the underlying ``structural'' shocks.</p><p>In Chapter 3 of the dissertation, I develop a new semi-parametric estimation method based on an extended ICAPM dynamic model incorporating jump tails. The model allows for time-varying, asymmetric jump size distributions and a self-exciting jump intensity process while avoiding commonly used but restrictive affine assumptions on the relationship between jump intensity and volatility. The estimated model implies that the average annual jump risk premium is 6.75\%. The model-implied jump risk premium also has strong explanatory power for short-to-medium run aggregate market returns. Empirically, I present new estimates of the model based equity risk premia of so-called "Small-Big", "Value-Growth" and "Winners-Losers" portfolios. Further, I find that they are all time-varying and all crashed in the 2008 financial crisis. Additionally, both the jump and volatility components of equity risk premia are especially important for the "Winners-Losers" portfolio.</p> / Dissertation

<p>The dissertation consists of four chapters that concern topics on copulas for high dimensions. Chapter 1 proposes a new general model for high dimension joint distributions of asset returns that utilizes high frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, which enables the use of high frequency data to accurately measure and forecast linear dependence, and the use of a new class of copulas designed to capture nonlinear dependence among the resulting linearly uncorrelated residuals. Estimation of the new class of copulas is conducted using a composite likelihood, making the model feasible even for hundreds of variables. A realistic simulation study verifies that multistage estimation with composite likelihood results in small loss in efficiency and large gain in computation speed. </p><p>Chapter 2, which is co-authored with Professor Andrew Patton, presents new models for the dependence structure, or copula, of economic variables based on a factor structure. The proposed models are particularly attractive for high dimensional applications, involving fifty or more variables. This class of models generally lacks a closed-form density, but analytical results for the implied tail dependence can be obtained using extreme value theory, and estimation via a simulation-based method using rank statistics is simple and fast. We study the finite-sample properties of the estimation method for applications involving up to 100 variables, and apply the model to daily returns on all 100 constituents of the S\&P 100 index. We find significant evidence of tail dependence, heterogeneous dependence, and asymmetric dependence, with dependence being stronger in crashes than in booms. </p><p>Chapter 3, which is co-authored with Professor Andrew Patton, considers the estimation of the parameters of a copula via a simulated method of moments type approach. This approach is attractive when the likelihood of the copula model is not known in closed form, or when the researcher has a set of dependence measures or other functionals of the copula that are of particular interest. The proposed approach naturally also nests method of moments and generalized method of moments estimators. Drawing on results for simulation based estimation and on recent work in empirical copula process theory, we show the consistency and asymptotic normality of the proposed estimator, and obtain a simple test of over-identifying restrictions as a goodness-of-fit test. The results apply to both $iid$ and time series data. We analyze the finite-sample behavior of these estimators in an extensive simulation study.</p><p>Chapter 4, which is co-authored with Professor Andrew Patton, proposes a new class of copula-based dynamic models for high dimension conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modelling high dimension covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enable the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008-09, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the pre-crisis period.</p> / Dissertation

This paper puts forward a Bayesian Global Vector Autoregressive Model with Common Stochastic Volatility (B-GVAR-CSV). We assume that Country specific volatility is driven by a single latent stochastic process, which simplifies the analysis and implies significant computational gains. Apart from computational advantages, this is also justified on the ground that the volatility of most macroeconomic quantities considered in our application tends to follow a similar pattern. Furthermore, Minnesota priors are used to introduce shrinkage to cure the curse of dimensionality. Finally, this model is then used to produce predictive densities for a set of macroeconomic aggregates. The dataset employed consists of quarterly data spanning from 1995:Q1 to 2012:Q4 and includes 45 economies plus the Euro Area. Our results indicate that stochastic volatility specifications influences accuracy along two dimensions: First, it helps to increase the overall predictive fit of our model. This result can be seen for some variables under scrutiny, most notably for real GDP and short-term interest rates. Second, it helps to make the model more resilient with respect to outliers and economic crises. This implies that when evaluated over time, the log predictive scores tend to show significantly less variation as compared to homoscedastic models. (author's abstract) / Series: Department of Economics Working Paper Series

This thesis seeks to address three important issues relating to the efficient functioning of the Istanbul Stock Exchange. In particular the thesis seeks to answer the following questions 1. What makes markets informationally efficient or inefficient? 2. Has increased stock market volatility had an impact on the equity risk premium and the cost of equity capital to firms? and 3. How is it possible to reconcile the view that markets are weak form efficient and technical analysis is a pervasive activity in such markets? Unlike previous studies, this thesis seeks to examine the issue of efficiency when institutional features specific to the market under investigation are taken into account. Specifically, the thesis adopts a testing methodology which enables us to recognize possible non-linear behaviour, thin trading and institutional changes in testing market efficiency. The results from this investigation show that informationally efficient markets are brought about by improving liquidity, ensuring that investors have access to high quality and reliable information and minimising the institutional restrictions on trading. In addition, the results suggest that emerging markets may initially be characterised as inefficient but over time, with the right regulatory framework, will develop into efficient and effective markets. The second important issue to be examined in this thesis concerns the impact of regulatory changes on market volatility and the cost of equity capital to firms. It is not sufficient to simply examine whether volatility has increased following a fmancial market innovation such as changes in regulation. Rather, it is necessary to investigate why volatility has changed, if it has changed, and the impact of such a change on the equity risk premium and the cost of equity capital to firms. Only then can inferences be drawn about the desirability or otherwise of innovations which bring about increases in volatility. Surprisingly, these issues have not been addressed in the literature. The evidence presented here suggests that the innovations which have taken place in the ISE have increased volatility, but also improved the pricing efficiency of the market and reduced the cost of equity capital to firms. Finally, the thesis tries to identify the conditions under which weak-form efficiency is consistent with technical analysis. It is shown that this paradox can be explained if adjustments to information are not immediate, such that market statistics, in particular statistics on trading volume contain information not impounded in current prices. In this context technical analysis on volume can be viewed as part of the process by which traders learn about fundamentals. Therefore, the thesis investigates the issue whether studying the joint dynamics of stock prices and trading volume can be used to predict weakly efficient stock prices. In summary, the findings of this thesis will be of interest to international investors, stock market regulators, firms raising funds from stock markets and participants in emerging capital markets in general. The implication of the results presented here is that informational efficient emerging markets are brought about by improving liquidity, ensuring that investors have access to high quality and reliable information and minimising the institutional restrictions on trading. In addition, the evolution in the regulatory framework of, and knowledge and awareness of investors in, emerging markets may mean that they will initially be characterised by inefficiency, but over time will develop into informational efficient and effectively functioning markets which allocate resources efficiently. In addition, the results of this thesis have important implications, for emerging markets in general, in identifying the regulatory framework that will achieve efficient pricing and a reduction in the cost of equity capital to firms operating in the economy.

This paper examines return co-movements and volatility spillovers between major exchange rates before and after the introduction of euro. Dynamic correlations and VAR-based spillover index results suggest significant return co-movements and volatility spillovers, however, their extend is, on average, lower in the post-euro period. Co-movements and spillovers are positively associated with extreme episodes and US dollar appreciations. The euro (Deutsche mark) is the dominant net transmitter of volatility, while the British pound the dominant net receiver of volatility in both periods. Nevertheless, cross-market volatility spillovers are bidirectional, and the highest spillovers occur between European markets. (author's abstract)

Background: Since the early 60’s, the CAPM or Capital Asset Pricing Model, has been an invaluable tool for assessing an asset's expected return, assuming that the asset is added to an already well-diversified portfolio of assets. CAPM theory assume that the unsystematic risk can be diversified and that the systematic, market-specific, risk is determined by the Beta value, from the Greek β. An investor who takes big risks expect higher returns. One of the CAPM’s basic assumptions is that disruption in the market is not taken into account. This assumption may lead to results that do not correspond to reality. Objective: This study examined the relationship between systematic risk, and return on a stable and volatile market. Methodology: The study was performed using a quantitative research with secondary data, in which 30 companies listed on the OMX 30 on the Stockholm stock exchange was studied. The investigation period was from 2003 to 2012 and was divided into three parts. Using the statistics program SPSS and Excel the data required to answer the purpose of the essay was calculated. Results: The analysis of the first time period between 2003 and 2007 showed that there was a statistically significant relationship between beta value and the average return for the period. The second time period between 2008 and 2012, which was characterized by an extremely volatile stock market, showed different results. The result of this period showed no statistical relationship existed when the market was characterized by high volatility. The third and final period between 2003 and 2012, which was a combination of a stable and a volatile market. The results for this period showed no significant association between beta value and average returns. The conclusion of this study is therefore that the CAPM model to assess an asset's return fails when the market is unstable, e.g. due to a financial crisis. To compensate for this error that is built into the model, one should therefore use alternative models, or revised versions of the CAPM, if the aim is to produce data in a realistic way that can be used as basis for investment decisions. / Bakgrund: Sedan början av 60-talet har CAPM, eller Capital Asset Pricing Model, varit ett ovärderligt instrument för att bedöma en tillgångs förväntade avkastning, där man antar att tillgången läggs till i en redan väldiversifierad portfölj av tillgångar. CAPM teorin antar vidare att den osystematiska risken diversifieras bort samt att den systematiska, marknadsspecifika, risken bestäms med hjälp av Beta-värdet, från grekiskans β. En investerare som tar stora risker förväntar sig högre avkastning. Ett av CAPM:s grundantaganden är att störningar på marknaden inte tas hänsyn till. Detta antagande kan leda till resultat som inte stämmer överens med verkligheten.   Syfte: I denna studie undersöktes sambandet mellan systematisk risk, samt avkastning på en stabil respektive volatil marknad. Metod: Undersökningen genomfördes med en kvantitativ forskningsmetodik med sekundära data där 30 bolag noterade på OMX30 på stockholmsbörsen studerades. Undersökningsperioden var mellan 2003 till 2012 och delades upp till tre delar Med hjälp av statistikprogrammet SPSS samt Excel beräknades nödvändiga data för att svara på uppsatsens syfte. Resultat: Analysen av den första tidsperioden mellan 2003-2007 visade att det förelåg ett statistiskt signifikant samband mellan betavärdet och den genomsnittliga avkastningen för perioden. Den andra tidsperioden mellan 2008-2012, som kännetecknades av en mycket volatil aktiemarknad, visade annorlunda resultat. Resultatet av denna tidsperiod visade att inget statistiskt samband förelåg när marknaden kännetecknades av en hög volatilitet. Den tredje och sista och perioden mellan 2003-2012, som alltså var en kombination av en stabil och en volatil marknad. Resultatet för denna tidsperiod visade inget signifikant samband mellan betavärdet och den genomsnittliga avkastningen. Slutsatsen av denna studie blir därmed att CAPM som metod för att bedöma en tillgångs avkastning fallerar när marknaden är ostabil, t.ex. beroende på en finanskris. För att kompensera för detta fel som är inbyggt i modellen bör därför alternativa modeller, eller justerade versioner av CAPM, användas om syftet är att ta fram data som på ett verklighetstroget sätt kan vara underlag för investeringsbedömningar.

Syfte: Syftet med denna studie var att se hur och varför volatiliteten påverkades i DJIA, S&amp;P 500 och NASDAQ Composite under IT-bubblan. Metod: Års- och månadsvolatiliteten för DJIA, S&amp;P 500 och NASDAQ Composite har beräknats under 1995-2004 med hjälp av data från Yahoo Finance. Empiri: Resultatet visar att volatiliteten var väsentligt högre i NASDAQ Composite än vad den var i S&amp;P 500 och DJIA som i sin tur höll en liknande volatilitet i förhållande till varandra. Analys: I analysen framträdde det att volatiliteten blev väsentligt högre i samband med att bubblan sprack under maj 2000 fram till dess att paniken lade sig kort efter maj 2002. Det fanns en hög överensstämmelse mellan denna rapport och övriga tidigare studier. Teorierna var mestadels väl applicerbara. Slutsats: Volatiliteten för DJIA, S&amp;P 500 och NASDAQ Composite var som högst mellan 2000 och 2002 under undersökningsperioden 1995-2004. IT-bubblan uppstod samt sprack till följd av irrationellt investeringsbeteende bland investerarna på aktiemarknaden och paniken som uppstod efteråt gjorde att volatiliteten på aktiemarknaden höll sig förhållandevis hög fram tills den lade sig kort efter maj 2002. NASDAQ Composite hade högst volatilitet till följd av IT-bubblan medan DJIA och S&amp;P 500 hade likvärdig volatilitet. Samtliga index följde ett liknande mönster, detta var troligtvis på grund av att företag från NASDAQ Composite kunde återfinnas i S&amp;P 500 samt DJIA. / Purpose: The purpose of this thesis is to see how and why the volatility was affected in DJIA, S&amp;P 500 and NASDAQ Composite during the Dot-com bubble. Method: The yearly and monthly volatility of DJIA, S&amp;P 500 and NASDAQ Composite were computed with data from a period spanning 1995-2004, which were collected from Yahoo Finance. Empiricism: The results illustrate that the volatility was vastly higher in NASDAQ Composite than in DJIA and S&amp;P 500 which in turn yielded a comparable volatility in relation to each other. Analysis: The analysis extracted the fact that the volatility rose considerably after the bubble burst during May 2000 and started waning after the panic died out circa May 2002. There were a relatively high harmony between the results of this report and the earlier studies which it was compared to. Conclusion: The volatility for DJIA, S&amp;P 500 and NASDAQ 500 was higher between 200 and 2002 than during the rest of the observed period. The Dot-com bubble arose due to irrational investment behavior among investors and the panic which arose afterwards contributed to the increasing volatility which maintained a high level until it subsided after May 2002. NASDAQ Composite had the highest volatility during the Dot-com bubble while DJIA and S&amp;P 500 had a similar volatility. All indexes followed a similar pattern, this was probably due to that companies from NASDAQ Composite reasonably should be found in S&amp;P 500 and DJIA.

This thesis investigates why and how macroeconomic volatility in source countries interacts with their FDI outflows. The study focuses on FDI flowing out from OECD countries to less developed countries in the ASEAN region. Using a panel data encompassing 52 country-pairs over the period 1996-2011, I find a negative correlation between FDI outflows and macroeconomic volatility in source countries. More specifically the empirical results suggest an adverse relationship between inflation and output volatility (business cycles fluctuations) and FDI flows – the more macroeconomic volatility in developed economies the lesser FDI flows to less developed economies, which is explained by Keynesian theories. These findings derive from a gravity model approach, which enabled me to control for host country determinants. In order to estimate these relationships I adopted a random effects model and a tobit model. The reason behind the use of these two models derives from the different views within this branch of research because of censored FDI statistics. The thesis is inspired by Éric Rougier’s et al. work on how macroeconomic volatility in European countries interacts with FDI flows to the MENA region (2012).