This thesis has treated the subject of DCC-GARCH model’s forecasting ability and Value-at- Risk applications on the Scandinavian foreign exchange market. The estimated models were based on daily opening foreign exchange spot rates in the period of 2004-2013, which captured the information in the financial crisis of 2008 and Eurozone crisis in the early 2010s. The forecasts were performed on a one-day rolling window in 2014. The results show that the DCC-GARCH model accurately predicted the fluctuation in the conditional correlation, although not with the correct magnitude. Furthermore, the DCC-GARCH model shows good Value-at-Risk forecasting performance for different portfolios containing the Scandinavian currencies.

L'objectif de cette thèse est d'étudier le problème de la modélisation des changements de régime dans les modèles a corrélations conditionnelles dynamiques en nous intéressant plus particulièrement a l'approche Markov-switching. A la différence de l'approche standard basée sur le modèle à chaîne de Markov caché (HMM) de base, nous utilisons des extensions du modèle HMM provenant des modèles graphiques probabilistes. Cette discipline a en effet proposé de nombreuses dérivations du modèle de base permettant de modéliser des structures complexes. Cette thèse se situe donc a l'interface de deux disciplines: l'économétrie financière et les modèles graphiques probabilistes.Le premier essai présente un modèle construit a partir d'une structure hiérarchique cachée markovienne qui permet de définir différents niveaux de granularité pour les régimes. Il peut être vu comme un cas particulier du modèle RSDC (Regime Switching for Dynamic Correlations). Basé sur le HMM hiérarchique, notre modèle permet de capter des nuances de régimes qui sont ignorées par l'approche Markov-Switching classique.La seconde contribution propose une version Markov-switching du modèle DCC construite a partir du modèle HMM factorise. Alors que l'approche Markov-switching classique suppose que les tous les éléments de la matrice de corrélation suivent la même dynamique, notre modèle permet à tous les éléments de la matrice de corrélation d'avoir leur propre dynamique de saut. Markov-switching. A la différence de l'approche standard basée sur le modèle à chaîne de Markov caché (HMM) de base, nous utilisons des extensions du modèle HMM provenant des modèles graphiques probabilistes. Cette discipline a en effet propose de nombreuses dérivations du modèle de base permettant de modéliser des structures complexes. Cette thèse se situe donc a l'interface de deux disciplines: l'économétrie financière et les modèles graphiques probabilistes.Le premier essai présente un modèle construit a partir d'une structure hiérarchique cachée markovienne qui permet de définir différents niveaux de granularité pour les régimes. Il peut ^etre vu commeun cas particulier du modele RSDC (Regime Switching for Dynamic Correlations). Base sur le HMMhierarchique, notre modele permet de capter des nuances de regimes qui sont ignorees par l'approcheMarkov-Switching classique.La seconde contribution propose une version Markov-switching du modele DCC construite a partir dumodele HMM factorise. Alors que l'approche Markov-switching classique suppose que les tous les elementsde la matrice de correlation suivent la m^eme dynamique, notre modele permet a tous les elements de lamatrice de correlation d'avoir leur propre dynamique de saut.Dans la derniere contribution, nous proposons un modele DCC construit a partir d'un arbre dedecision. L'objectif de cet arbre est de relier le niveau des volatilites individuelles avec le niveau descorrelations. Pour cela, nous utilisons un arbre de decision Markovien cache, qui est une extension de HMM. / The objective of this thesis is to study the modelling of change in regime in the dynamic conditional correlation models. We focus particularly on the Markov-switching approach. Unlike the standard approach based on the Hidden Markov Model (HMM), we use extensions of HMM coming from probabilistic graphical models theory. This discipline has in fact proposed many derivations of the basic model to model complex structures. Thus, this thesis can be view at the interface of twodisciplines: financial econometrics and probabilistic graphical models.The first essay presents a model constructed from a hierarchical hidden Markov which allows to increase the granularity of the regimes. It can be seen as a special case of RSDC model (Regime Switching for Dynamic Correlations). Based on the hierarchical HMM, our model can capture nuances of regimes that are ignored by the classical Markov-Switching approach.The second contribution proposes a Markov-switching version of the DCC model that is built from the factorial HMM. While the classical Markov-switching approach assumes that all elements of the correlation matrix follow the same switching dynamic, our model allows all elements of the correlation matrix to have their own switching dynamic.In the final contribution, we propose a model DCC constructed based on a decision tree. The objective of this tree is to link the level of volatility with the level of individual correlations. For this, we use a hidden Markov decision tree, which is an extension of HMM.

This thesis consists of four research papers. The main focus is on building the multivariate Conditional Correlation (CC-) GARCH models. In particular, emphasis lies on considering an extension of CC-GARCH models that allow for interactions or causality in conditional variances. In the first three chapters, misspecification testing and parameter restrictions in these models are discussed. In the final chapter, a computer package for building major variants of the CC-GARCH models is presented. The first chapter contains a brief introduction to the CC-GARCH models as well as a summary of each research paper. The second chapter proposes a misspecification test for modelling of the conditional variance part of the Extended Constant CC-GARCH model. The test is designed for testing the hypothesis of no interactions in the conditional variances. If the null hypothesis is true, then the conditional variances may be described by the standard CCC-GARCH model. The test is constructed on the Lagrange Multiplier (LM) principle that only requires the estimation of the null model. Although the test is derived under the assumption of the constant conditional correlation, the simulation experiments suggest that the test is also applicable to building CC-GARCH models with changing conditional correlations. There is no asymptotic theory available for these models, which is why simulation of the test statistic in this situation has been necessary. The third chapter provides yet another misspecification test for modelling of the conditional variance component of the CC-GARCH models, whose parameters are often estimated in two steps. The estimator obtained through these two steps is a two-stage quasi-maximum likelihood estimator (2SQMLE). Taking advantage of the asymptotic results for 2SQMLE, the test considered in this chapter is formulated using the LM principle, which requires only the estimation of univariate GARCH models. It is also shown that the test statistic may be computed by using an auxiliary regression. A robust version of the new test is available through another auxiliary regression. All of this amounts to a substantial simplification in computations compared with the test proposed in the second chapter. The simulation experiments show that, under both under both Gaussian and leptokurtic innovations, as well as under changing conditional correlations, the new test has reasonable size and power properties. When modelling the conditional variance, it is necessary to keep the sequence of conditional covariance matrices positive definite almost surely for any time horizon. In the fourth chapter it is demonstrated that under certain conditions some of the parameters of the model can take negative values while the conditional covariance matrix remains positive definite almost surely. It is also shown that even in the simplest first-order vector GARCH representation, the relevant parameter space can contain negative values for some parameters, which is not possible in the univariate model. This finding makes it possible to incorporate negative volatility spillovers into the CC-GARCH framework. Many new GARCH models and misspecification testing procedures have been recently proposed in the literature. When it comes to applying these models or tests, however, there do not seem to exist many options for the users to choose from other than creating their own computer programmes. This is especially the case when one wants to apply a multivariate GARCH model. The last chapter of the thesis offers a remedy to this situation by providing a workable environment for building CC-GARCH models. The package is open source, freely available on the Internet, and designed for use in the open source statistical environment R. With this package can estimate major variants of CC-GARCH models as well as simulate data from the CC-GARCH data generating processes with multivariate normal or Student's t innovations. In addition, the package is equipped with the necessary functions for conducting diagnostic tests such as those discussed in the third chapter of this thesis. / <p>Diss. Stockholm : Handelshögskolan, 2010. Sammanfattning jämte 4 uppsatser.</p>

This dissertation studies the cost of carry relationship and the international dynamics of mispricing, price and volatility in the three Nikkei futures markets - the Osaka Exchange (OSE), the Singapore Exchange (SGX) and the Chicago Mercantile Exchange (CME). Previous research does not fully consider the unique characteristics of the triple-listed Nikkei futures contracts, or the price and volatility dynamics in the three Nikkei futures exchanges at the same time. This dissertation makes a significant contribution to the existing literature. In particular, with a comprehensive new 19-year sample period, this dissertation helps deepen the understanding of the Nikkei spot-futures equilibrium and arbitrage behaviour, cross-border information transmission mechanism, and futures market integration. The first topic of the dissertation is to study the cost of carry relationship, mispricing and index arbitrage in the three Nikkei markets. The standard cost of carry model is adjusted for each Nikkei futures contract by allowing for the triple-listing nature and key institutional differences. Based on this, the economic significance of the Nikkei mispricing is explored in the presence of transaction costs. The static behaviour of the mispricing suggests that it is difficult especially for institutional investors to make arbitrage profits in the OSE and SGX, and that index arbitrage in the CME is not strictly risk-free due to the exchange rate effect. Smooth transition models are used to study the dynamic behaviour of the mispricing in the three markets. The results show that mean reversion in mispricing and limits to arbitrage are driven more by transaction costs than by heterogeneous arbitrageurs in the Nikkei markets. The second topic of the dissertation is to investigate the price discovery process in individual Nikkei markets and across the Nikkei futures markets. With smooth transition error correction models, this dissertation reports the leading role of the futures prices in the pre-crisis period and the leading role of the spot prices in the post-crisis period, in the first-moment information transmission process. Moreover, there is evidence of asymmetric adjustments in the Nikkei prices and volatilities. The cross-border dynamics suggest that the foreign Nikkei markets (the CME and SGX) act as the main price discovery vehicle, which implies the key functions of the equivalent, offshore markets in futures market globalisation. The third topic of the dissertation is to study the volatility transmission process in individual Nikkei markets and across the Nikkei futures markets, from the perspectives of the volatility interactions in and across the Nikkei markets and of the dynamic Nikkei market linkages. This dissertation finds bidirectional volatility spillover effects between the Nikkei spot and futures markets, and the information leadership of the foreign Nikkei markets (the CME and SGX) in the second-moment information transmission process across the border. It further examines the dynamic conditional correlations between the Nikkei markets. The results point to a dramatic integration process with strongly persistent and stable Nikkei market co-movements over time.

My dissertation investigates the financial linkages and transmission of economic shocks between the US and the smallest emerging markets (frontier markets). The first chapter sets up an empirical model that examines the impact of US market returns and conditional volatility on the returns and conditional volatilities of twenty-one frontier markets. The model is estimated via maximum likelihood; utilizes the GARCH model of errors, and is applied to daily country data from the MSCI Barra. We find limited, but statistically significant exposure of Frontier markets to shocks from the US. Our results suggest that it is not the lagged US market returns that have impact; rather it is the expected US market returns that influence frontier market returns The second chapter sets up an empirical time-varying parameter (TVP) model to explore the time-variation in the impact of mean US returns on mean Frontier market returns. The model utilizes the Kalman filter algorithm as well as the GARCH model of errors and is applied to daily country data from the MSCI Barra. The TVP model detects statistically significant time-variation in the impact of US returns and low, but statistically and quantitatively important impact of US market conditional volatility. The third chapter studies the risk-return relationship in twenty Frontier country stock markets by setting up an international version of the intertemporal capital asset pricing model. The systematic risk in this model comes from covariance of Frontier market stock index returns with world returns. Both the systematic risk and risk premium are time-varying in our model. We also incorporate own country variances as additional determinants of Frontier country returns. Our results suggest statistically significant impact of both world and own country risk in explaining Frontier country returns. Time-variation in the world risk premium is also found to be statistically significant for most Frontier market returns. However, own country risk is found to be quantitatively more important.

Cette thèse traite la contribution des méthodes d'ondelettes sur la modélisation des séries temporelles économiques et financières et se compose de deux parties: une partie univariée et une partie multivariée. Dans la première partie (chapitres 2 et 3), nous adoptons le cas univarié. Premièrement, nous examinons la classe des processus longue mémoire non-stationnaires. Une étude de simulation a été effectuée afin de comparer la performance de certaines méthodes d'estimation semi-paramétrique du paramètre d'intégration fractionnaire. Nous examinons aussi la mémoire longue dans la volatilité en utilisant des modèles FIGARCH pour les données de l'énergie. Les résultats montrent que la méthode d'estimation Exact Local Whittle de Shimotsu et Phillips [2005] est la meilleure méthode de détection de longue mémoire et la volatilité du pétrole exhibe une forte évidence de phénomène de mémoire longue. Ensuite, nous analysons le risque de marché des séries de rendements univariées de marchés boursier, qui est mesurée par le risque systématique (bêta) à différents horizons temporels. Les résultats montrent que le Bêta n'est pas stable, en raison de multi-trading stratégies des investisseurs. Les résultats basés sur l'analyse montrent que le risque mesuré par la VaR est plus concentrée aux plus hautes fréquences. La deuxième partie (chapitres 4 et 5) traite l'estimation de la variance et la corrélation conditionnelle des séries temporelles multivariées. Nous considérons deux classes de séries temporelles: les séries temporelles stationnaires (rendements) et les séries temporelles non-stationnaires (séries en niveaux). / This thesis deals with the contribution of wavelet methods on modeling economic and &#64257;nancial time series and consists of two parts: the univariate time series and multivariate time series. In the &#64257;rst part (chapters 2 and 3), we adopt univariate case. First, we examine the class of non-stationary long memory processes. A simulation study is carried out in order to compare the performance of some semi-parametric estimation methods for fractional differencing parameter. We also examine the long memory in volatility using FIGARCH models to model energy data. Results show that the Exact local Whittle estimation method of Shimotsu and Phillips [2005] is the better one and the oil volatility exhibit strong evidence of long memory. Next, we analyze the market risk of univariate stock market returns which is measured by systematic risk (beta) at different time horizons. Results show that beta is not stable, due to multi-trading strategies of investors. Results based on VaR analysis show that risk is more concentrated at higher frequency. The second part (chapters 4 and 5) deals with estimation of the conditional variance and correlation of multivariate time series. We consider two classes of time series: the stationary time series (returns) and the non-stationary time series (levels). We develop a novel approach, which combines wavelet multi-resolution analysis and multivariate GARCH models, i.e. the wavelet-based multivariate GARCH approach. However, to evaluate the volatility forecasts we compare the performance of several multivariate models using some criteria, such as loss functions, VaR estimation and hedging strategies.