The ability to predict stock returns is an ability many wish to possess, and in an accurate way as possible. For many years there has been an interest in the field of factor models explaining the returns, with the aim to increase the explanatory power. This is however a complex business since the factors and their improvement of explanatory power need to be significant. Now and then, researchers come up with new significant factors that have a positive impact on models. AQR Capital Management is no exception to this, since they in 2013 presented the factor Quality Minus Junk, earning significant risk-adjusted returns. This bachelor thesis work within mathematical statistics and industrial engineering and management, aims to investigate whether or not the commercial multi-factor model used at the public pension fund Fjärde AP-fonden will be improved by adding the factor Quality Minus Junk, in the sense of explanatory power. The method used is mainly based on multiple linear regression and three three-year time periods are studied ranging from 2010 to 2018. The results from this thesis work show that the QMJ factor provides significant increases in explanatory power for one of three time periods, the most recent period 2016$-$2018. However, since the results are inconclusive further studies are needed in order to better understand how to interpret the results and whether or not to include the QMJ factor in the model. / Förmågan att förutsäga aktiers avkastning önskar många besitta, och på ett så precist sätt som möjligt. Under många år har forskning pågått inom området för faktormodeller som förklarar avkastningar, med målet att öka modellernas förklaringsgrad. Detta är dock en komplex verksamhet eftersom faktorerna och deras förbättring av förklaringsgraden måste vara signifikanta för modellen. Då och då kommer forskare fram med nya sådana faktorer som har positiv påverkan på modeller. AQR Capital Management är inget undantag eftersom de 2013 presenterade sin faktor Quality Minus Junk som visar signifikanta riskjusterade avkastningar. Detta kandidatexamensarbete inom matematisk statistik och industriell ekonomi, ämnar att utreda huruvida den kommersiella faktormodellen som används på Fjärde AP-fonden förbättras genom tillägget av faktorn Quality Minus Junk, i förklaringsgradsmening. Metoden som används är till största delen baserad på multipel linjär regression och tre treårsperioder studeras i tidsintervallet 2010 till 2018. Resultaten från detta projekt visar på att faktorn Quality Minus Junk bidrar med signifikanta ökningar av förklaringsgraden för en av tre perioder, den senaste perioden 2016-2018. Eftersom resultaten är inkonklusiva krävs vidare studier för att bättre förstå och konkludera vad dessa resultat faktiskt innebär samt för att inkludera QMJ-faktorn i modellen eller ej.

Allocation to private infrastructure debt investments has increased in the recent years [15]. For managers of multi-asset portfolios, it is important to be able to assess the risk of the total portfolio and the contribution to risk of the various holdings in the portfolio. This includes being able to explain the risk of having private infrastructure debt investments in the portfolio. The modelling of private infrastructure debt face many challenges, such as the lack of private data and public indices for private infrastructure debt. In this thesis, two approaches for modelling private infrastructure debt in a parametric risk factor model are proposed. Both approaches aim to incorporate revenue risk, which is the risk occurring from the type of revenue model in the infrastructure project or company. Revenue risk is categorised into three revenue models; merchant, contracted and regulated, as spread level differences can be distinguished for private infrastructure debt investments using this categorisation. The difference in spread levels between the categories are used to estimate β coefficients for the two modelling approaches. The spread levels are obtained from a data set and from a previous study. In the first modelling approach, the systematic risk factor approach, three systematic risk factors are introduced where each factor represent infrastructure debt investments with a certain revenue model. The risk or the volatility for each of these factors is the volatility of a general infrastructure debt index adjusted with one of the β coefficients. In the second modelling approach, the idiosyncratic risk term approach, three constant risk terms for the revenue models are added in order to capture the revenue risk for private infrastructure debt investments. These constant risk terms are estimated with the β coefficients and the historical volatility of a infrastructure debt index. For each modelling approach, the commonly used risk measures standalone risk and risk contribution are presented for the entire block of the infrastructure debt specific factors and for each of the individual factors within this block. Both modelling approaches should enable for better explanation of risk in private infrastructure debt investments by introducing revenue risk. However, the modelling approaches have not been backtested and therefore no conclusion can be made in regards to whether one of the proposed modelling approaches actually is better than current modelling approaches for private infrastructure debt. / Investeringar i privat infrastrukturskuld har ökat de senaste åren [15]. För βägare av portföljer med investeringar i samtliga tillgångsslag är det viktigt att kunna urskilja risken från de olika innehaven i portföljen. Det finns många utmaningar vad gäller modellering av privat infrastrukturskuld, så som den begränsade mängden privat data och publika index för privat infrastrukturskuld. I denna uppsats föreslås två tillvägagångssätt för att modellera privat infrastrukturskuld i en parametrisk riskfaktormodell. Båda tillvägagångssätten eftersträvar att inkorporera intäktsrisk, vilket är risken som beror på den underliggande intäktsmodellen i ett infrastrukturprojekt eller företag. Intäksrisk delas in i intäksmodellerna "merchant", "contracted" och "regulated", då en skillnad i spreadnivå mellan privata infrastrukturskuldinvesteringar kan urskiljas med denna kategorisering. Skillnaden i spreadnivå mellan de olika kategorierna används för att estimera β -koefficienter som används i båda tillvägagångssätten. Spreadnivåerna erhålls från ett dataset och från en tidigare studie. I det första tillvägagångssättet, den systematiska riskfaktor-ansatsen, introduceras tre systematiska riskfaktorer som representerar infrastrukturskuldinvesteringar med en viss intäktsmodell. Risken eller volatiliten för dessa faktorer är densamma som volatiliteten för ett index för infrastrukturskuld justerat med en av β -koefficienterna. I det andra tillvägagångssättet, den idriosynktratiska riskterm-ansatsen, adderas tre konstanta risktermer för intäktsmodellerna för att fånga upp intäktsrisken i de privata infrastrukturinvesteringarna. De konstanta risktermerna är estimerade med β -koefficienterna och en historisk volatilitet för ett index för infrastrukturskuld. För båda tillvägagångssätten presenteras riskmåtten stand-alone risk1 och risk contribution2. Riskmåtten ges för ett block av samtliga faktorer för infrastrukturskuld och för varje enskild faktor inom detta block. Båda tillvägagångssätten borde möjliggöra bättre förklaring av risken för privata infrastrukturskuldinvesteringar i en större portfölj genom att ta hänsyn till intäktsrisken. De två tillvägagångssätten för modelleringen har dock ej testats. Därför kan ingen slutsats dras med hänsyn till huruvida ett av tillvägagångssätten är bättre än de som används för närvärande för modellering av privat infrastrukturskuld.

[pt] A dissertação é composta por três ensaios empíricos que usam dados históricos de ações americanas. O primeiro avalia o desempenho de uma abordagem de otimização de carteiras baseada na otimização de Markowitz. Os resultados mostram valor econômico positivo do portfólio resultante, mesmo na presença de custos de transação. O segundo artigo visa comparar e combinar a técnica desenvolvida no artigo anterior à abordagem paramétrica e avalia o desempenho da combinação das técnicas. Os resultados mostram que o desempenho da técnica paramétrica é inferior à técnica de Markowitz modificada e pouco melhor do que o mercado agregado. Isto sugere que o valor econômico de explorar a estrutura de covariância entre as ações é superior a aumentar pesos em ações cujas características oferecem relações risco-retorno maiores até o período. O terceiro ensaio avalia modelos de previsão da variação de retornos entre ações. As estatísticas utilizadas apontam que os modelos padrão não possuem poder preditivo superior a modelos que supõem que não há variação ou que usam a média histórica. Por meio do uso tanto de combinações de modelos lineares quanto estimação restrita de modelos com muitos fatores, mostro que é possível obter resultados ligeiramente superiores. / [en] The dissertation consists of three empirical essays which use historical data of stocks listed in NYSE. The first essay evaluates a portfolio selection approach based on the Markowitz optimization. Results show the portfolios have positive economic value, even after including transaction costs. The second essay compares the technique proposed in the first essay to the parametric approach. Results show the parametric approach performs worse than the modified Markowitz approach and shlightly better than the aggregated market. This suggests that exploring the covariance structure of stocks provides better results than overweighting stocks with characteristics associated to better riskreturn ratios in the past. The third essay evaluates models that forecast the cross-sectional variation in stock returns. Given the statistics used, benchmark models do not show greater forecasting power than skeptical or naive models. By using linear model combination or lasso technique on a model with several factors, I show it is possible to obtain slightly better results.

In den letzten Jahren gab es ein drastisches Wachstum in verfügbaren Finanzdaten. Finanzmärkte haben starke und oft nicht ganz vorhersagbare Änderungen ihrer Dynamik erlebt. Diese Tendenz hat dazu geführt, dass die Methoden der Risikomodellierung sowohl das Problem der hohen Dimensionalität als auch dynamische nicht Gaußsche Strukturen behandeln müssen. Das Ziel dieser Dissertation ist es, Methoden der Risikomodellierung vorzuschlagen, die gleichzeitig Reduzierung der Dimensionalität und dynamische Struktur in drei Anwendungen erlauben: 1) Asset Allocation und Hedging, 2) stochastische Modellierung von multivariaten Prozessen, 2) Messung der systemischen Risiken. Die vorgeschlagenen Methoden demonstrieren gute Ergebnisse im Vergleich mit den existierenden Methoden der Risikomodellierung und führen neue Verfahren zur Erkennung der extremen Risiken und Anomalien auf Finanzmärkten sowie zur deren Management. / Over the recent years, there have been a significant increase in financial data availability. On the other hand, financial markets have experienced sharp and often unforeseen changes in their dynamics. This tendency has caused the need for risk modeling approaches addressing both high dimensionality problem and accustoming for dynamic non Gaussian structure. The primary aim of this dissertation is to propose several risk modeling approaches which allow for simultaneous dimension reduction and dynamic structures in three setups: 1) asset allocation and hedging, 2) stochastic surface modeling and 3) systemic risk determination. Proposed models demonstrate good performance when compared to existing approaches for risk modeling and introduce new flexible ways to detect extreme risks and anomalies on financial markets as well as methods for their modeling and management.

Diese Dissertation befasst sich mit europäischer und US-amerikanischer Konjunkturgeschichte und Marktintegration im 19. und 20. Jahrhundert. Zur Analyse von konjunkturellen Schwankungen stellt sie der weitverbreiteten Historischen Volkswirtschaftlichen Gesamtrechnung (VGR) die Methode dynamischer Faktoranalyse zur Seite, die dazu beiträgt, die begrenzten historischen Zeitreihen effizient zu nutzen. Die nationale und internationale Entwicklung von Weizenmärkten seit dem Ende der Napoleonischen Kriege wird mit einem multivariaten dynamischen Faktormodell untersucht. Spektralanalyse wird zur Berechnung frequenzspezifischer Kohärenz von historischen Börsenindizes und konkurrierenden Schätzungen des Nationalprodukts in Deutschland zwischen 1850 und 1913 herangezogen. Ein wichtiges Ergebnis ist, dass Finanzdaten die Datierung der Konjunktur im Deutschen Kaiserreich erleichtern, was auch durch die Ergebnisse der Faktoranalyse bestätigt wird. Der verwendete Aktienindex, einzelne reale Konjunkturindikatoren und der dynamische Faktor korrelieren eng miteinder. Die Bildung sektoraler Sub-Indizes zeigt, dass der Übergang von einer landwirtschaftlich zu einer industriell geprägten Volkswirtschaft vermutlich früher geschehen ist als Beschäftigungsanteile aus der Historischen VGR vermuten lassen. Die Untersuchung der U.S.-Konjunktur ergibt die Annahme zeitvariierender Strukturparameter eine Erhöhung der Konjunkturschwankungsbreite nach dem 2. Weltkrieg verglichen mit der Zeit vor dem 1. Weltkrieg. Für die Weizenmarktintegration in Europa zeigt sich, dass die Entwicklung vor der Mitte des 19. Jahrhunderts schneller voran ging als danach, was eine Neuinterpretation der Rolle von Technologien wie dem Metallrumpf und dem Dampfschiff sowie dem Eintritt Amerikas als Weizenproduzenten nahelegt. / This thesis addresses historical business cycles and market integration in Europe and America in the 19th and 20th centuries. For the analysis of historical business cycles, the widely used methodology of historical national accounting is complemented with a dynamic factor model that allows for using scarce historical data efficiently. In order to investigate how national and international markets developed since the early 1800s, a multivariate dynamic factor model is used. Spectral analysis helps in measuring frequency specific correlation between financial indicators and rivaling national income estimates for Germany between 1850 and 1913. One result is that the historical stock market index used helps to discriminate between competing estimates of German national income. A dynamic factor estimated from a broad time series data set confirms this result. Sub-indices for agriculture and industry suggest that the German economy industrialized earlier than evidence from national accounting shows. The finding for the U.S. business cycle is that relaxing the assumption of constant structural parameters yields higher postwar aggregate volatility relative to the period before World War I. Concerning market integration, it is found that European wheat markets integrated faster before mid-19th century than after. Thus, the impact of the metal hull and steam ship as well as the relevance of American wheat for the world wheat market have perhaps been overstated.

Diese Arbeit besteht aus vier Essays, die empirische und methodische Beiträge zur Messung von Konjunkturzyklen und deren Zusammenhänge zu demographischen Variablen liefern. Der erste Essay analysiert unter Zuhilfenahme eines Bayesianischen Dynamischen Faktormodelles die Volatilität des US-amerikanischen Konjunkturzyklus seit 1867. In dem Essay wird gezeigt, dass die Volatilität in der Periode vor dem Ersten Weltkrieg und nachdem Zweiten Weltkrieg niedriger war als in der Zwischenkriegszeit. Eine geringere Volatilität für die Periode nach dem Zweiten Weltkrieg im Vergleich zu der Periode vor dem Ersten Weltkrieg kann nicht bestätigt werden. Der zweite Essay hebt die Bayesianischen Eigenschaften bezüglich dynamischer Faktormodelle hervor. Der Essay zeigt, dass die ganze Analyse hindurch - im Gegensatz zu klassischen Ansätzen - keine Annahmen an die Persistenz der Zeitreihen getroffen werden muss. Des Weiteren wird veranschaulicht, wie im Bayesianischen Rahmen die Anzahl der Faktoren bestimmt werden kann. Der dritte Essay entwickelt einen neuen Ansatz, um altersspezifische Sterblichkeitsraten zu modellieren. Kovariate werden mit einbezogen und ihre Dynamik wird gemeinsam mit der von latenten Variablen, die allen Alterklassen zugrunde liegen, modelliert. Die Resultate bestätigen, dass makroökonomische Variablen Prognosekraft für die Sterblichkeit beinhalten. Im vierten Essay werden makroökonomischen Zeitreihen zusammen mit altersspezifischen Sterblichkeitsraten einer strukturellen Analyse unterzogen. Es wird gezeigt, dass sich die Sterblichkeit von jungen Erwachsenen in Abhängigkeit von Konjunkturzyklen deutlich von den der anderen Alterklassen unterscheidet. Daher sollte in solchen Analysen, um Scheinkorrelation vorzubeugen, zwischen den einzelnen Altersklassen differenziert werden. / The thesis consists of four essays, which make empirical and methodological contributions to the fields of business cycle analysis and demography. The first essay presents insights on U.S. business cycle volatility since 1867 derived from a Bayesian dynamic factor model. The essay finds that volatility increased in the interwar periods, which is reversed after World War II. While evidence can be generated of postwar moderation relative to pre-1914, this evidence is not robust to structural change, implemented by time-varying factor loadings. The second essay scrutinizes Bayesian features in dynamic index models. The essay shows that large-scale datasets can be used in levels throughout the whole analysis, without any pre-assumption on the persistence. Furthermore, the essay shows how to determine the number of factors accurately by computing the Bayes factor. The third essay presents a new way to model age-specific mortality rates. Covariates are incorporated and their dynamics are jointly modeled with the latent variables underlying mortality of all age classes. In contrast to the literature, a similar development of adjacent age groups is assured, allowing for consistent forecasts. The essay demonstrates that time series of covariates contain predictive power for age-specific rates. Furthermore, it is observed that in particular parameter uncertainty is important for long-run forecasts, implicating that ignoring parameter uncertainty might yield misleadingly precise predictions. In the fourth essay the model developed in the third essay is utilized to conduct a structural analysis of macroeconomic fluctuations and age-specific mortality rates. The results reveal that the mortality of young adults, concerning business cycles, noticeably differ from the rest of the population. This implies that differentiating closely between particular age classes, might be important in order to avoid spurious results.

Diese Arbeit besteht aus vier Essays, die empirische und methodische Beiträge zu den Gebieten der Finanzmarktökonomik und der Makroökonomik liefern. Der erste Essay beschäftigt sich mit der Spezifikation der Investoren verfügbaren Informationsmenge in Tests bedingter Kapitalmarktmodelle. Im Speziellen schlägt es die Verwendung dynamischer Faktoren als Instrumente vor. Diese fassen per Konstruktion die Information in einer Vielzahl von Variablen zusammen und stellen daher intuitive Maße für die Investoren zur Verfügung stehenden Informationen dar. Es wird gezeigt, dass so die Schätzfehler bedingter Modelle im Vergleich zu traditionellen, auf einzelnen Indikatoren beruhenden Modellvarianten substantiell verringert werden. Ausgehend von Ergebnissen, dass die Zentralbank zur Festlegung des kurzfristigen Zinssatzes eine große Menge an Informationen berücksichtigt, wird im zweiten Essay im Rahmen eines affinen Zinsstrukturmodells eine ähnliche Idee verwandt. Speziell wird die Dynamik des kurzfristigen Zinses im Rahmen einer Faktor-Vektorautoregression modelliert. Aufbauend auf dieser dynamischen Charakterisierung der Geldpolitik wird dann die Zinsstruktur unter der Annahme fehlender Arbitragemöglichkeiten hergeleitet. Das resultierende Modell liefert bessere Vorhersagen US-amerikanischer Anleihenzinsen als eine Reihe von Vergleichsmodellen. Der dritte Essay analysiert die Vorhersagekraft der Zinsstrukturkomponenten "level", "slope", und "curvature" im Rahmen eines dynamischen Faktormodells für makroökonomische und Zinsdaten. Das Modell wird mit einem Metropolis-within-Gibbs Sampling Verfahren geschätzt, und Überraschungsänderungen der drei Komponenten werden mit Hilfe von Null- und Vorzeichenrestriktionen identifiziert. Die Analyse offenbart, dass der "curvature"-Faktor informativer in Bezug auf die zukünftige Entwicklung der Zinsstruktur und der gesamtwirtschaftlichen Aktivität ist als bislang vermutet. Der vierte Essay legt eine monatliche Chronologie der Konjunkturzyklen im Euro-Raum vor. Zunächst wird mit Hilfe einer verallgemeinerten Interpolationsmethode eine monatliche Zeitreihe des europäischen BIP konstruiert. Anschließend wird auf diese Zeitreihe ein Datierungsverfahren angewandt, das kurze und flache Konjunkturphasen ausschließt. / This thesis consists of four essays of independent interest which make empirical and methodological contributions to the fields of financial economics and macroeconomics. The first essay deals with the proper specification of investors’ information set in tests of conditional asset pricing models. In particular, it advances the use of dynamic factors as conditioning variables. By construction, dynamic factors summarize the information in a large number of variables and are therefore intuitively appealing proxies for the information set available to investors. The essay demonstrates that this approach substantially reduces the pricing errors implied by conditional models with respect to traditional approaches that use individual indicators as instruments. Following previous evidence that the central bank uses a large set of conditioning information when setting short-term interest rates, the second essay employs a similar insight in a model of the term structure of interest rates. Precisely, the dynamics of the short-term interest rate are modelled using a Factor-Augmented Vector-Autoregression. Based on this dynamic characterization of monetary policy, the term structure of interest rates is derived under the assumption of no-arbitrage. The resulting model is shown to provide superior out-of-sample forecasts of US government bond yields with respect to a number of benchmark models. The third essay analyzes the predictive information carried by the yield curve components level, slope, and curvature within a joint dynamic factor model of macroeconomic and interest rate data. The model is estimated using a Metropolis-within-Gibbs sampling approach and unexpected changes of the yield curve components are identified employing a combination of zero and sign restrictions. The analysis reveals that the curvature factor is more informative about the future evolution of the yield curve and of economic activity than has previously been acknowledged. The fourth essay provides a monthly business cycle chronology for the Euro area. A monthly series of Euro area real GDP is constructed using an interpolation routine that nests previously suggested approaches as special cases. Then, a dating routine is applied to the interpolated series which excludes business cycle phases that are short and flat.

The research presented in this thesis addresses different aspects of dynamic portfolio construction and portfolio risk measurement. It brings the research on dynamic portfolio optimization, replicating portfolio construction, dynamic portfolio risk measurement and volatility forecast together. The overall aim of this research is threefold. First, it is aimed to examine the portfolio construction and risk measurement performance of a broad set of volatility forecast and portfolio optimization model. Second, in an effort to improve their forecast accuracy and portfolio construction performance, it is aimed to propose new models or new formulations to the available models. Third, in order to enhance the replication performance of hedge fund returns, it is aimed to introduce a replication approach that has the potential to be used in numerous applications, in investment management. In order to achieve these aims, Chapter 2 addresses risk measurement in dynamic portfolio construction. In this chapter, further evidence on the use of multivariate conditional volatility models in hedge fund risk measurement and portfolio allocation is provided by using monthly returns of hedge fund strategy indices for the period 1990 to 2009. Building on Giamouridis and Vrontos (2007), a broad set of multivariate GARCH models, as well as, the simpler exponentially weighted moving average (EWMA) estimator of RiskMetrics (1996) are considered. It is found that, while multivariate GARCH models provide some improvements in portfolio performance over static models, they are generally dominated by the EWMA model. In particular, in addition to providing a better risk-adjusted performance, the EWMA model leads to dynamic allocation strategies that have a substantially lower turnover and could therefore be expected to involve lower transaction costs. Moreover, it is shown that these results are robust across the low - volatility and high-volatility sub-periods. Chapter 3 addresses optimization in dynamic portfolio construction. In this chapter, the advantages of introducing alternative optimization frameworks over the mean-variance framework in constructing hedge fund portfolios for a fund of funds. Using monthly return data of hedge fund strategy indices for the period 1990 to 2011, the standard mean-variance approach is compared with approaches based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investors. In order to estimate portfolio CVaR, CDaR and Omega, a semi-parametric approach is proposed, in which first the marginal density of each hedge fund index is modelled using extreme value theory and the joint density of hedge fund index returns is constructed using a copula-based approach. Then hedge fund returns from this joint density are simulated in order to compute CVaR, CDaR and Omega. The semi-parametric approach is compared with the standard, non-parametric approach, in which the quantiles of the marginal density of portfolio returns are estimated empirically and used to compute CVaR, CDaR and Omega. Two main findings are reported. The first is that CVaR-, CDaR- and Omega-based optimization offers a significant improvement in terms of risk-adjusted portfolio performance over mean-variance optimization. The second is that, for all three risk measures, semi-parametric estimation of the optimal portfolio offers a very significant improvement over non-parametric estimation. The results are robust to as the choice of target return and the estimation period. Chapter 4 searches for improvements in portfolio risk measurement by addressing volatility forecast. In this chapter, two new univariate Markov regime switching models based on intraday range are introduced. A regime switching conditional volatility model is combined with a robust measure of volatility based on intraday range, in a framework for volatility forecasting. This chapter proposes a one-factor and a two-factor model that combine useful properties of range, regime switching, nonlinear filtration, and GARCH frameworks. Any incremental improvement in the performance of volatility forecasting is searched for by employing regime switching in a conditional volatility setting with enhanced information content on true volatility. Weekly S&P500 index data for 1982-2010 is used. Models are evaluated by using a number of volatility proxies, which approximate true integrated volatility. Forecast performance of the proposed models is compared to renowned return-based and range-based models, namely EWMA of Riskmetrics, hybrid EWMA of Harris and Yilmaz (2009), GARCH of Bollerslev (1988), CARR of Chou (2005), FIGARCH of Baillie et al. (1996) and MRSGARCH of Klaassen (2002). It is found that the proposed models produce more accurate out of sample forecasts, contain more information about true volatility and exhibit similar or better performance when used for value at risk comparison. Chapter 5 searches for improvements in risk measurement for a better dynamic portfolio construction. This chapter proposes multivariate versions of one and two factor MRSACR models introduced in the fourth chapter. In these models, useful properties of regime switching models, nonlinear filtration and range-based estimator are combined with a multivariate setting, based on static and dynamic correlation estimates. In comparing the out-of-sample forecast performance of these models, eminent return and range-based volatility models are employed as benchmark models. A hedge fund portfolio construction is conducted in order to investigate the out-of-sample portfolio performance of the proposed models. Also, the out-of-sample performance of each model is tested by using a number of statistical tests. In particular, a broad range of statistical tests and loss functions are utilized in evaluating the forecast performance of the variance covariance matrix of each portfolio. It is found that, in terms statistical test results, proposed models offer significant improvements in forecasting true volatility process, and, in terms of risk and return criteria employed, proposed models perform better than benchmark models. Proposed models construct hedge fund portfolios with higher risk-adjusted returns, lower tail risks, offer superior risk-return tradeoffs and better active management ratios. However, in most cases these improvements come at the expense of higher portfolio turnover and rebalancing expenses. Chapter 6 addresses the dynamic portfolio construction for a better hedge fund return replication and proposes a new approach. In this chapter, a method for hedge fund replication is proposed that uses a factor-based model supplemented with a series of risk and return constraints that implicitly target all the moments of the hedge fund return distribution. The approach is used to replicate the monthly returns of ten broad hedge fund strategy indices, using long-only positions in ten equity, bond, foreign exchange, and commodity indices, all of which can be traded using liquid, investible instruments such as futures, options and exchange traded funds. In out-of-sample tests, proposed approach provides an improvement over the pure factor-based model, offering a closer match to both the return performance and risk characteristics of the hedge fund strategy indices.

The price level convergence of the transition economies towards the reference economies is linked to the relative price of nontradables, which is explained by the total factor productivity differentials in tradable and nontradable sector. Basic concept is offered by the Balassa Samuelson model and its modifications. Testable equations are derived from these models, and the panel data approach is applied for their estimation. The results indicate faster growth of the relative price of nontradables in transition economies as succession of higher growth rate of the total factor productivity in tradable sector. Hence estimated models confirm the price level convergence of transition economies towards the reference economies. The analyses of price dynamics of the complementary field, i. e. of the tradables, follows, and the basic concept is represented by the rational bubble hypothesis. The stress is putted on the impact of the word prices on the price levels of the Czech Republic. After a cointegration analysis of the time series is carried out, the influence of the word prices of tradable commodities is estimated within a vector error correction model and regression analysis. This cost factors analysis is afterwards related to the export dynamics of the Czech Republic, and models suitable for quantitative analysis of export dynamics as well as its prediction based on vector error correction model and regression analysis are evaluated. Their forecasting ability is assessed within a simulation of ex-post forecasts and a root mean squared error. The aim is to consider the relationship between the price levels and the export dynamics, for the relation of both variables evaluated within the Granger causality seems to be less straightforward then the standard export equations suggest, and the estimated equations confirm significant influence of the export dynamics on the price level.

This thesis is composed of three chapters which propose some novel approaches to model and forecast financial time series. The first chapter focuses on high frequency financial returns and proposes a quantile regression approach to model their intraday seasonality and dynamics. The second chapter deals with the problem of forecasting the yield curve including large datasets of macroeconomics information. While the last chapter addresses the issue of modelling the term structure of interest rates. <p><p>The first chapter investigates the distribution of high frequency financial returns, with special emphasis on the intraday seasonality. Using quantile regression, I show the expansions and shrinks of the probability law through the day for three years of 15 minutes sampled stock returns. Returns are more dispersed and less concentrated around the median at the hours near the opening and closing. I provide intraday value at risk assessments and I show how it adapts to changes of dispersion over the day. The tests performed on the out-of-sample forecasts of the value at risk show that the model is able to provide good risk assessments and to outperform standard Gaussian and Student’s t GARCH models.<p><p>The second chapter shows that macroeconomic indicators are helpful in forecasting the yield curve. I incorporate a large number of macroeconomic predictors within the Nelson and Siegel (1987) model for the yield curve, which can be cast in a common factor model representation. Rather than including macroeconomic variables as additional factors, I use them to extract the Nelson and Siegel factors. Estimation is performed by EM algorithm and Kalman filter using a data set composed by 17 yields and 118 macro variables. Results show that incorporating large macroeconomic information improves the accuracy of out-of-sample yield forecasts at medium and long horizons.<p><p>The third chapter statistically tests whether the Nelson and Siegel (1987) yield curve model is arbitrage-free. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities. Still, central banks and public wealth managers rely heavily on it. Using a non-parametric resampling technique and zero-coupon yield curve data from the US market, I find that the no-arbitrage parameters are not statistically different from those obtained from the Nelson and Siegel model, at a 95 percent confidence level. I therefore conclude that the Nelson and Siegel yield curve model is compatible with arbitrage-freeness.<p> / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished