I denna uppsats diskuteras sambandet mellan risk, marknadseffektivitet och volatilitet. En studie görs för att se om volatiliteten (kursrörligheten) har ökat i enskilda aktier under tidsperioden 1988-1999. Vidare behandlas vilka faktorer som kan ge upphov till volatilitetsförändringar, vilka konsekvenser en förändrad volatilitet får för olika typer av investerare samt om utvecklingen är förenlig med EMH (den effektiva marknadshypotesen).Studier av volatilitet är intressanta p g a den risk som uppstår för olika marknadsaktörer vid en ökad volatilitet. Uppstår felvärderingar på aktiemarknaden finns även risk för att kapital fördelas på ett sätt som inte är optimalt. I stora drag kan man definiera begreppet volatilitet som ett mått på hur stor osäkerheten är inför den framtida kursutvecklingen för en aktie. Volatilitet är att betrakta som kortsiktiga rörelser i finansiella priser under loppet av en dag eller från en dag till en annan, men begreppet används också / In this paper the relation between risk, market efficiency and volatility is being discussed. A study has been made to see if there has been any change in the volatility for individual stocks during the time period 1988-1999. Further, the factors that contribute to changes in the volatility have been examined and also which the consequences are for various types of investors and whether the developement is consistent with the efficient market hypothesis. The study of volatility is interesting because of the risk that an increased volatility entails for the investors. When mispricing occurs in the stockmarket there is also a risk for a non-optimal allocation of capital. Roughly speaking the definition of volatility is how large the uncertainty is about the future return from an individual stock. Volatility is considered to be short time period movements in financial prices within a day or from one day to another but is also used as a mesaure to describe price

The purpose of this thesis is to examine whether the exchange rate volatility has an impact on Swedish exports. This relationship has been tested in several studies but no consistent result has been found. It is therefore an interesting subject to investigate further and it has not been thoroughly tested for Sweden using aggregated data. Since the exchange rate vola-tility may have an effect on exports, and therefore on the whole economy, the effect can support a certain exchange rate regime. All the data used in this thesis is based on the ag-gregated data for Sweden and the Euro zone between the years 1993 and 2006. The method chosen is a statistical analysis using regressions. Three variables other than ex-change rate volatility were included when conducting the regressions explaining Swedish exports and these are: the real effective exchange rate index, the industrial production in Sweden (“push” factor) and the import from the Euro Zone (“pull” factor). The overall conclusion found was that the industrial production in Sweden, the real effective exchange rate index, the time and lagged values of the export influence the export. There was no evi-dence found that the exchange rate volatility influences the exports for Sweden.

<p>This dissertation consists of three essays on asymmetric correlations in financial markets. In the first essay, I have two main contributions. First, I show that dividend growth rates have symmetric correlations. Second, I show that asymmetric correlations are different than correlations being counter-cyclical. The correlation asymmetry I study in this dissertation should not be confused with correlations being counter-cyclical, i.e. being higher during recessions than during booms. I show that while counter-cyclical correlations can simply be explained by counter-cyclical aggregate market volatility, the correlation asymmetry with respect to joint upside and downside movements of returns are not just due to the heightened market volatility during those times. </p><p>In the second essay I present a model in order to explain the correlation asymmetry observed in the data. This is the first paper to offer an explanation for observed correlation asymmetry. I formalize the explanation using an equilibrium model. The model is useful to understand both the cross-section and time-series of correlation asymmetry. By the means of my model, we can answer questions about why some stocks have higher correlation asymmetry, and why the correlation asymmetry was higher during 1990s? In the model asset prices respond the realization of dividends and news about the future. However, price responses to news are asymmetric and this asymmetry is endogenous. Price responses are endogenously stronger conditional on bad news than conditional on good news. This asymmetry also generates the observed correlation asymmetry. The price responses are asymmetric due to the ambiguity about the news quality. Information about the quality of the signal is incomplete in the sense that the exact precision of the signal is unknown; it is only known to be in an interval, which makes the representative agent treat news as ambiguous. To model ambiguity aversion, I use Gilboa and Schmeidler (1989)'s max-min expected utility representation. The agent has a set of beliefs about the quality of signals, and the ambiguity-averse agent behaves as if she maximizes expected utility under a worst-case scenario. This incomplete information about the news quality, together with ambiguity-averse agents, generates an asymmetric response to news. Endogenous worst-case scenarios differ depending on the realization of news. When observing ``bad" news, the worst-case scenario is that the news is reliable and the prices of trees decrease strongly. On the other hand, when ``good news" is observed, under the worst-case scenario the news is evaluated as less reliable, and thus the price increases are mild. Therefore, price responses are stronger conditional on a negative signal and this asymmetry creates a higher correlation conditional on a negative signal than conditional on a positive signal. I also show that the results are robust to the smooth ambiguity aversion representation.</p><p>Motivated by the model, I uncover a new empirical regularity that is unknown in the literature. I show that correlation asymmetry is related to idiosyncratic volatility: the higher the idiosyncratic volatility, the higher the correlation asymmetry. This novel empirical finding is also useful to understand the time-series and cross-sectional variation in correlation asymmetry. Stocks with smaller market capitalizations have greater correlation asymmetry compared to stocks with higher market capitalization. However, an explanation for this finding has been lacking. According to the explanation offered in this paper, smaller size stocks have greater correlation asymmetry compared to bigger size stocks because small size stocks tend to have higher idiosyncratic volatilities compared to bigger size stocks. In the time-series, correlation asymmetry shows quite significant variation as well. The average correlation asymmetry is especially high for the 1990s and decreases significantly at the beginning of the 2000s. This pattern in times-series can also be explained in terms of the time-series behavior of idiosyncratic volatilities. Several papers including Brandt et al. (2010), document higher idiosyncratic volatilities during 1990s while the aggregate volatility stays fairly stable. Basically, the high idiosyncratic volatilities during the 1990s also caused greater correlation asymmetry. </p><p>In the third essay, I study the correlation of returns in government bond markets. Similar to the findings in equity markets, I show that there is some evidence for asymmetric correlations in government bond markets. First, I show that the maturity structure matters for correlation asymmetry in bonds markets: Unlike long-maturity bonds, shorter-maturity bonds tend to have asymmetric correlations. Second, I show that the correlation asymmetry observed in European bond markets disappears with the formation of a common currency area. Lastly, I study the correlation between equity and bond returns in different countries. For long-maturity bonds, correlations with the domestic equity returns are asymmetric for half of the countries in the sample, including the U.S. These findings show that results on asymmetric correlations from equity markets can generalize, at least to some extent, to other financial markets.</p> / Dissertation

This dissertation consists of three independent essays in Macroeconomics. The first essay analyzes monetary coordination between currency areas. It is shown that search frictions can generate the deviations from the law of one price and that each country is tempted to exploit these deviations by inflation. Monetary coordination eliminates the inefficiency caused by inflation. The welfare gains from coordination increase when the two economies become more integrated. In contrast to traditional models, the need for coordination exists even after each country is allowed to directly tax foreign holdings of its currency. The second essay studies the behavior of exchange rates in an environment with search frictions. In contrast to traditional models, even without any nominal rigidity, the model can generate enough volatility of exchange rates found in the data. The changes in the behavior of exchange rates under different regimes are also examined in this essay. The model shows a sharp increase in the volatility of exchange rates when moving from a pegged to a floating exchange regime, while there is no such systematic change in fluctuations of output or consumption. Moreover, the co-movements of output and consumption across countries are higher under a fixed rate regime than under a flexible rate regime. These results are consistent with empirical findings. The final essay focuses on the competition between groups of allied firms. In the essay we propose a model of group fitness and develop an approach to evaluate the fitness of groups and the utility of their member firms. A group has high fitness if member firms have four features: (i) high capacity, (ii) being embedded in dense relationships, (iii) holding complementary resources and (iv) having limited competition and conflict. We illustrate the effectiveness of our model and methodology by applying it to the airline groups between 1997 and 2002. By examining what really happened to the airline groups afterwards, we found that the predictions based on the comparison between the fitness scores of actual groups formed and those of the corresponding population constructed are reasonably accurate, and that the implications based on the ranking of individual firm utility within each group are generally supported.

Stochastic volatility (SV) models provide a natural framework for a representation of time series for financial asset returns. As a result, they have become increasingly popular in the finance literature, although they have also been applied in other fields such as signal processing, telecommunications, engineering, biology, and other areas. In working with the SV models, an important issue arises as how to estimate their parameters efficiently and to assess how well they fit real data. In the literature, commonly used estimation methods for the SV models include general methods of moments, simulated maximum likelihood methods, quasi Maximum likelihood method, and Markov Chain Monte Carlo (MCMC) methods. Among these approaches, MCMC methods are most flexible in dealing with complicated structure of the models. However, due to the difficulty in the selection of the proposal distribution for Metropolis-Hastings methods, in general they are not easy to implement and in some cases we may also encounter convergence problems in the implementation stage. In the light of these concerns, we propose in this thesis new estimation methods for univariate and multivariate SV models. In the simulation of latent states of the heavy-tailed SV models, we recommend the slice sampler algorithm as the main tool to sample the proposal distribution when the Metropolis-Hastings method is applied. For the SV models without heavy tails, a simple Metropolis-Hastings method is developed for simulating the latent states. Since the slice sampler can adapt to the analytical structure of the underlying density, it is more efficient. A sample point can be obtained from the target distribution with a few iterations of the sampler, whereas in the original Metropolis-Hastings method many sampled values often need to be discarded. In the analysis of multivariate time series, multivariate SV models with more general specifications have been proposed to capture the correlations between the innovations of the asset returns and those of the latent volatility processes. Due to some restrictions on the variance-covariance matrix of the innovation vectors, the estimation of the multivariate SV (MSV) model is challenging. To tackle this issue, for a very general setting of a MSV model we propose a straightforward MCMC method in which a Metropolis-Hastings method is employed to sample the constrained variance-covariance matrix, where the proposal distribution is an inverse Wishart distribution. Again, the log volatilities of the asset returns can then be simulated via a single-move slice sampler. Recently, factor SV models have been proposed to extract hidden market changes. Geweke and Zhou (1996) propose a factor SV model based on factor analysis to measure pricing errors in the context of the arbitrage pricing theory by letting the factors follow the univariate standard normal distribution. Some modification of this model have been proposed, among others, by Pitt and Shephard (1999a) and Jacquier et al. (1999). The main feature of the factor SV models is that the factors follow a univariate SV process, where the loading matrix is a lower triangular matrix with unit entries on the main diagonal. Although the factor SV models have been successful in practice, it has been recognized that the order of the component may affect the sample likelihood and the selection of the factors. Therefore, in applications, the component order has to be considered carefully. For instance, the factor SV model should be fitted to several permutated data to check whether the ordering affects the estimation results. In the thesis, a new factor SV model is proposed. Instead of setting the loading matrix to be lower triangular, we set it to be column-orthogonal and assume that each column has unit length. Our method removes the permutation problem, since when the order is changed then the model does not need to be refitted. Since a strong assumption is imposed on the loading matrix, the estimation seems even harder than the previous factor models. For example, we have to sample columns of the loading matrix while keeping them to be orthonormal. To tackle this issue, we use the Metropolis-Hastings method to sample the loading matrix one column at a time, while the orthonormality between the columns is maintained using the technique proposed by Hoff (2007). A von Mises-Fisher distribution is sampled and the generated vector is accepted through the Metropolis-Hastings algorithm. Simulation studies and applications to real data are conducted to examine our inference methods and test the fit of our model. Empirical evidence illustrates that our slice sampler within MCMC methods works well in terms of parameter estimation and volatility forecast. Examples using financial asset return data are provided to demonstrate that the proposed factor SV model is able to characterize the hidden market factors that mainly govern the financial time series. The Kolmogorov-Smirnov tests conducted on the estimated models indicate that the models do a reasonable job in terms of describing real data.

This thesis uses synthetically created variance swaps on VIX futures to quantify the variance risk premium in VIX options. The results of this methodology suggest that the average premium is -3.26%, meaning that the realized variance on VIX futures is on average less than the variance implied by the swap rate. This premium does not vary with time or the level of the swap rate as much as premiums in other asset classes. A negative risk premium should mean that VIX option strategies that are net credit should be profitable. This thesis tests two simple net credit strategies with puts and calls, and finds that the call strategy is profitable while the put strategy is not.

Understanding stock-bond return correlation is a key facet in asset mix, asset allocation and in an investor’s portfolio optimisation strategy. For the last couple of decades, several studies have probed this cardinal relationship. While initial literature tries to understand the fundamental pattern of co-movements, later studies aim to model the economic state variables influencing such time-varying volatility behaviour of stock-bond returns. This study provides a systematic literature review in the field of stock and bond return correlation. The review investigates the existing literature in three key dimensions. First, it examines the effect of macro-economic variables on SB return co-movements. Second, it illustrates the effect of financial integration on the asset correlation dynamics. Third, it reviews the existing models that are employed to estimate the dynamic relationship. In addition to the systematic review, I conduct an empirical analysis of stock-bond return co-movements on U.S. capital market. Both the literature and the empirical investigation substantiate my claims on existing research gaps and respective scope for further research. Evidence shows that existing models impose strong restrictions on past stock-bond return variance dynamics and yield inconclusive results. I, therefore, propose an alternative method, i.e. copula function approach, to model stock and bond time-varying co-movements. Since the previous studies largely focus on developed economies, I suggest an empirically investigation of emerging economies as well. This will allow me to examine the effect of financial integration on the dynamic asset return correlation. Apart from this academic contribution, the study provides an illustration of the economic implications which relate to portfolio optimization and minimal-risk hedge ratio.

This dissertation consists of three independent essays in Macroeconomics. The first essay analyzes monetary coordination between currency areas. It is shown that search frictions can generate the deviations from the law of one price and that each country is tempted to exploit these deviations by inflation. Monetary coordination eliminates the inefficiency caused by inflation. The welfare gains from coordination increase when the two economies become more integrated. In contrast to traditional models, the need for coordination exists even after each country is allowed to directly tax foreign holdings of its currency. The second essay studies the behavior of exchange rates in an environment with search frictions. In contrast to traditional models, even without any nominal rigidity, the model can generate enough volatility of exchange rates found in the data. The changes in the behavior of exchange rates under different regimes are also examined in this essay. The model shows a sharp increase in the volatility of exchange rates when moving from a pegged to a floating exchange regime, while there is no such systematic change in fluctuations of output or consumption. Moreover, the co-movements of output and consumption across countries are higher under a fixed rate regime than under a flexible rate regime. These results are consistent with empirical findings. The final essay focuses on the competition between groups of allied firms. In the essay we propose a model of group fitness and develop an approach to evaluate the fitness of groups and the utility of their member firms. A group has high fitness if member firms have four features: (i) high capacity, (ii) being embedded in dense relationships, (iii) holding complementary resources and (iv) having limited competition and conflict. We illustrate the effectiveness of our model and methodology by applying it to the airline groups between 1997 and 2002. By examining what really happened to the airline groups afterwards, we found that the predictions based on the comparison between the fitness scores of actual groups formed and those of the corresponding population constructed are reasonably accurate, and that the implications based on the ranking of individual firm utility within each group are generally supported.

The financial crisis has made the stock markets a very turbulent place. Investors have therefore begun searching for stable and profitable investments. Nordic real estate has for decades steadily increased in value and the stocks of real estate companies are said to be less risky than the market. This has led to a view of them being a safe haven for risk adverse investors. Very few empirical studies have been done on how these supposedly safe stocks actually behave in the Nordic countries. The purpose of this study is hence to investigate how these real estate companies stocks perform. The method employed is deductive and quantitative.One part of the research is to test whether or not the stocks are more profitable than the overall market. Additionally, motivated by previous research on market efficiency, this paper looks into if any predictable patterns for the real estate stocks returns can be found. In other words, if historical returns can be used to predict future returns. Thirdly this research paper looks into how risky the real estate stocks are compared to the market. For the last part of research, an examination on whether the usage of CAPM as a return calculator is appropriate for real estate stocks in the Nordic countries. The statistical tool SPSS and Microsoft Excel has been used to examine the relationships between the variables. The paper has used time series regression to find beta values and alpha values for risk assessments and tests of CAPM, and autocorrelation tests to determine market efficiency, or more precisely random walk.The research is are done on all real estate stocks on the Swedish, Finnish, Danish and Norwegian markets, a total of 31, over a time period of 10 years, between 2003 and 2013 on daily, weekly and monthly data. The result of the first part was that the real estate stock returns in general were not more profitable than the overall market. This research also found significant predictability of future returns through historical data on a daily basis, and some signs on a weekly basis while no predictability on a monthly basis. The result of the third part of the research is that the risk levels for the real estate stocks are different and in general much lower than the market risk. Lastly the test of CAPM shows no significant difference between the expected returns and the actual observed returns.

El objetivo de esta tesis es modelar y predecir la volatilidad de las series financieras con modelos de volatilidad en tiempo discreto y continuo.En mi primer capítulo, intento modelar las principales características de las series financieras, como a persistencia y curtosis. Los modelos de volatilidad estocástica estimados son extensiones directas de los modelos de Gallant y Tauchen (2001), donde incluyo un elemento de retro-alimentación. Este elemento es de extrema importancia porque permite captar el hecho de que períodos de alta volatilidad están, en general, seguidos de periodos de gran volatilidad y viceversa. En este capítulo, como en toda la tesis, uso el método de estimación eficiente de momentos de Gallant y Tauchen (1996). De la estimación surgen dos modelos posibles de describir los datos, el modelo logarítmico con factor de volatilidad y retroalimentación y el modelo logarítmico con dos factores de volatilidad. Como no es posible elegir entre ellos basados en los tests efectuados en la fase de la estimación, tendremos que usar el método de reprogección para obtener mas herramientas de comparación. El modelo con un factor de volatilidad se comporta muy bien y es capaz de captar la "quiebra" de los mercados financieros de 1987.En el segundo capítulo, hago la evaluación del modelo con dos factores de volatilidad en términos de predicción y comparo esa predicción con las obtenidas con los modelos GARCH y ARFIMA. La evaluación de la predicción para los tres modelos es hecha con la ayuda del R2 de las regresiones individuales de la volatilidad "realizada" en una constante y en las predicciones. Los resultados empíricos indican un mejor comportamiento del modelo en tiempo continuo. Es más, los modelos GARCH y ARFIMA parecen tener problemas en seguir la marcha de la volatilidad "realizada". Finalmente, en el tercer capítulo hago una extensión del modelo de volatilidad estocástica de memoria larga de Harvey (2003). O sea, introduzco un factor de volatilidad de corto plazo. Este factor extra aumenta la curtosis y ayuda a captar la persistencia (que es captada con un proceso integrado fraccional, como en Harvey (1993)). Los resultados son probados y el modelo implementado empíricamente. / The purpose of my thesis is to model and forecast the volatility of the financial series of returns by using both continuous and discrete time stochastic volatility models.In my first chapter I try to fit the main characteristics of the financial series of returns such as: volatility persistence, volatility clustering and fat tails of the distribution of the returns.The estimated logarithmic stochastic volatility models are direct extensions of the Gallant and Tauchen's (2001) by including the feedback feature. This feature is of extreme importance because it allows to capture the low variability of the volatility factor when the factor is itself low (volatility clustering) and it also captures the increase in volatility persistence that occurs when there is an apparent change in the pattern of volatility at the very end of the sample. In this chapter, as well as in all the thesis, I use Efficient Method of Moments of Gallant and Tauchen (1996) as an estimation method. From the estimation step, two models come out, the logarithmic model with one factor of volatility and feedback (L1F) and the logarithmic model with two factors of volatility (L2). Since it is not possible to choose between them based on the diagnostics computed at the estimation step, I use the reprojection step to obtain more tools for comparing models. The L1F is able to reproject volatility quite well without even missing the crash of 1987.In the second chapter I fit the continuous time model with two factors of volatility of Gallant and Tauchen (2001) for the return of a Microsoft share. The aim of this chapter is to evaluate the volatility forecasting performance of the continuous time stochastic volatility model comparatively to the ones obtained with the traditional GARCH and ARFIMA models. In order to inquire into this, I estimate using the Efficient Method of Moments (EMM) of Gallant and Tauchen (1996) a continuous time stochastic volatility model for the logarithm of asset price and I filter the underlying volatility using the reprojection technique of Gallant and Tauchen (1998). Under the assumption that the model is correctly specified, I obtain a consistent estimator of the integrated volatility by fitting a continuous time stochastic volatility model to the data. The forecasting evaluation for the three estimated models is going to be done with the help of the R2 of the individual regressions of realized volatility on the volatility forecasts obtained from the estimated models. The empirical results indicate the better performance of the continuous time model in the out-of-sample periods compared to the ones of the traditional GARCH and ARFIMA models. Further, these two last models show difficulties in tracking the growth pattern of the realized volatility. This probably is due to the change of pattern in volatility in this last part of the sample. Finally, in the third chapter I come back to the model specification and I extend the long memory stochastic volatility model of Harvey (1993) by introducing a short run volatility factor. This extra factor increases kurtosis and helps the model capturing volatility persistence (that it is captured by a fractionally integrated process as in Harvey (1993) ). Futhermore, considering some restrictions of the parameters it is possible to fit the empirical fact of small first order autocorrelation of squared returns. All these results are proved theoretically and the model is implemented empirically using the S&P 500 composite index returns. The empirical results show the superiority of the model in fitting the main empirical facts of the financial series of returns.