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This dissertation includes two topics. For the first topic about the housing price jump risk, we use EM gradient algorithms to estimate parameters of the jump diffusion model and test whether the US monthly housing price have jump risk during 1986 to 2006. Then, in order to obtain a viable pricing framework of mortgage insurance contracts, this paper uses the jump diffusion processes of Merton (1976) to model the dynamic process of housing price. Using this model, we investigate the impact of price jump risk on the valuation of mortgage insurance premium from jump intensity, abnormal volatility of jump size and normal volatility. Empirical results indicate that the abnormal volatility of jump size has the most significant impact on the mortgage insurance premium. For the second topic about the catastrophe risk, we investigate that, for catastrophic events, the assumption that catastrophe claims occur in terms of the Poisson process seems inadequate as it has constant intensity. We propose Markov Modulated Poisson process to model the arrival process for catastrophic events. Under this process, the underlying state is governed by a homogenous Markov chain, and it is the generalization of Cummins and Geman (1993, 1995), Chang, Chang, and Yu (1996), Geman and Yor (1997) and Vaugirard (2003a, 2003b). We apply Markov jump diffusion model to derive pricing formulas for catastrophe insurance products, included catastrophe futures call option, catastrophe PCS call spread and catastrophe bond. We use the data of PCS index and the annual number of hurricane events during 1950 to 2004 to test the quality of the fitting under the Markov Modulated Poisson process and the Poisson process. We reach the conclusion that the Markov Modulated Poisson process is fitter than the Poisson process and Weiner process in modeling the arrival rate of hurricane events when pricing three insurance products. Hence, if different status of climate environment has significant different arrival intensity in real economy, using jump diffusion model to evaluate CAT insurance products could cause significant mispricing.

This thesis is devoted to the study of different stochastic processes which have a common feature: they are Markov-modulated, which means that their evolution rules depend on the state occupied by an underlying Markov process. In the first part of this thesis, we analyse the stationary distribution and various first passage problems for Markov-modulated Brownian motions (MMBMs) as well as for two extensions: MMBMs with jumps and MMBMs modified by a temporary change of regime upon visits to level zero. The second part of this thesis is devoted to the use of Markov-modulated processes in mathematical finance, more precisely for the calculation of different option prices. We use a Fourier transform approach to price different European options (vanilla, exchange and quanto options) in the case where the value of the considered risky assets evolves like the exponential of a Markov-modulated Lévy process. The third part of this thesis is devoted to the study of some stochastic epidemic processes, namely the SIR processes. In our models, a Markov process is used to modulate the behaviour of the individuals who bring the disease. We use different martingale approaches as well as matrix analytic methods to obtain various information about the state of the population when the epidemic is over. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Access to the large web content hosted all over the world by users of the Internet engage many hosts, routers/switches and faster links. They challenge the internet backbone to operate at its capacity to assure e±cient content access. This may result in congestion and raises concerns over various Quality of Service (QoS) issues like high delays, high packet loss and low throughput of the system for various Internet applications. Thus, there is a need to develop effective congestion control mechanisms in order to meet various Quality of Service (QoS) related performance parameters. In this paper, our emphasis is on the Active Queue Management (AQM) mechanisms, particularly Random Early Detection (RED). We propose a threshold based novel analytical model based on standard RED mechanism. Various numerical examples are presented for Internet traffic scenarios containing both the burstiness and correlation properties of the network traffic.

過去實證研究發現，資產的動態過程存在不連續的跳躍與大波動伴隨大波動的波動度叢聚現象而造成資產報酬分配呈現出厚尾與高狹峰的情況，然而，此現象並不能完全被傳統所使用幾何布朗運動模型與跳躍擴散模型給解釋。因此，本文設定資產模型服從Lévy 過程中Generalized Hyperbolic (GH)的normal inverse Gaussian(NIG) 和 variance gamma (VG)兩個模型，然而，Lévy 過程是一個跳躍過程，是屬於一個不完備的市場，這將使得平賭測度並非唯一，因此，本文將採用Gerber 和 Shiu (1994)所提的Esscher 轉換來求得平賭測度。關於美式選擇權將採用LongStaff and Schwartz (2001)所提的最小平方蒙地卡羅模擬法來評價美式選擇權。實證結果發現VG有較好的評價績效，此外，進一步探討流動性與價內外的情況對於評價誤差的影響，亦發現部分流動性高的樣本就較小的評價誤差；此外，價外的選擇權其評價誤差最大。另一方面從交易的觀點來看，次貸風暴後交易對手信用風險愈來愈受到重視，此外，近年來由於巨災事件的頻傳，使得傳統保險公司風險移轉的方式，漸漸透過資本市場發行衍生性商品來進行籌資，以彌補其在巨災發生時所承擔的損失。因此，透過發行衍生性商品來進行籌資，必須考量交易對手的信用風險，否則交易對手違約，就無法獲得額外的資金挹注，因此，本文評價巨災權益賣權，並考量交易對手信用風險對於其價格的影響。 / In the traditional models such as geometric Brownian motion model or the Merton jump diffusion model can’t fully depict the distributions of return for financial securities and the those return always have heavy tail and leptokurtic phenomena due to the price jump or volatilities of return changing over time. Hence, the first article uses two time-changed Lévy models: (1) normal inverse Gaussian model and (2) variance gamma model to capture the dynamics of asset for pricing American option. In order to deal with the early-exercised problem of the American option, we use the LSM to determine the optimal striking point until maturity. In the empirical analyses, we can find the VG model have better performance than the other three models in some cases. In addition, with the comparison the pricing performance under different liquidity and moneyness conditions, we also find in some samples increasing the liquidity really can reduce the pricing errors, at the same time, the maximum pricing errors appears in the OTM samples in all cases. The global subprime crisis during 2008 and 2009 arouses much more attention of the counterparty risk and the number of default varies with economic condition. Hence, we investigate the counterparty risk impact on the price of the catastrophe equity put with a Markov-modulated default intensity model in the second study. At the same time, we also extend the stochastic interest rate setting in Jaimungal and Wang (2006) and relax some restrictive assumption of Black-Scholes model by taking the regime-switching effects of the economic status, then use the Markov-modulated processes to model the dynamics of the underlying asset and interest rate. In the numerical analyses, we illustrate the impact of the recovery rate, time to maturity, jump intensity of the equity and default intensity of counterparty on the CatEPut price.

Les processus Markoviens continus en temps sont largement utilisés pour tenter d’expliquer l’évolution des séquences protéiques et nucléotidiques le long des phylogénies. Des modèles probabilistes reposant sur de telles hypothèses sont conçus pour satisfaire la non-homogénéité spatiale des contraintes fonctionnelles et environnementales agissant sur celles-ci. Récemment, des modèles Markov-modulés ont été introduits pour décrire les changements temporels dans les taux d’évolution site-spécifiques (hétérotachie). Des études ont d’autre part démontré que non seulement la force mais également la nature de la contrainte sélective agissant sur un site peut varier à travers le temps. Ici nous proposons de prendre en charge cette réalité évolutive avec un modèle Markov-modulé pour les protéines sous lequel les sites sont autorisés à modifier leurs préférences en acides aminés au cours du temps. L’estimation a posteriori des différents paramètres modulants du noyau stochastique avec les méthodes de Monte Carlo est un défi de taille que nous avons su relever partiellement grâce à la programmation parallèle. Des réglages computationnels sont par ailleurs envisagés pour accélérer la convergence vers l’optimum global de ce paysage multidimensionnel relativement complexe. Qualitativement, notre modèle semble être capable de saisir des signaux d’hétérogénéité temporelle à partir d’un jeu de données dont l’histoire évolutive est reconnue pour être riche en changements de régimes substitutionnels. Des tests de performance suggèrent de plus qu’il serait mieux ajusté aux données qu’un modèle équivalent homogène en temps. Néanmoins, les histoires substitutionnelles tirées de la distribution postérieure sont bruitées et restent difficilement interprétables du point de vue biologique. / Time-continuous Markovian process are widely used to understand the mechanism of nucleotidic acids and proteins evolution along phylogeny. Already existing probabilistic models based on such hypothesis are designed to satisfy the non-homogeneity of functional and environmental constraints acting across those biological sequences. Recently, Markov-modulated models have been introduced to describe site-specific temporal rate variation (heterotachy). Moreover, studies have demonstrated that not only strength but also the nature of the constraint acting on a specific site can vary over time. Here we propose to accommodate this evolutionary reality with a Markov-modulated model for proteins under which sites are authorized to change their amino acids propensities across time. Posterior estimation of the stochastic kernel hidden parameters with Monte Carlo methods is a challenging approach that we partially overcome with parallel computing. Fine-tuning are otherwise planned to accelerate convergence toward the target posterior stationnary distribution. Qualitatively, our model seems to be able to capture temporal heterogeneity from real sequences data sets whose evolutionary history is assumed to be rich in substitutional switch events. Furthermore, evaluation of the model performance suggest that he provides a better fit to the data set than the time-homogeneous equivalent model. Nonetheless, substitutional histories sampled from the posterior distribution are quite noisy and remain difficult to interpret biologically.

Les processus Markoviens continus en temps sont largement utilisés pour tenter d’expliquer l’évolution des séquences protéiques et nucléotidiques le long des phylogénies. Des modèles probabilistes reposant sur de telles hypothèses sont conçus pour satisfaire la non-homogénéité spatiale des contraintes fonctionnelles et environnementales agissant sur celles-ci. Récemment, des modèles Markov-modulés ont été introduits pour décrire les changements temporels dans les taux d’évolution site-spécifiques (hétérotachie). Des études ont d’autre part démontré que non seulement la force mais également la nature de la contrainte sélective agissant sur un site peut varier à travers le temps. Ici nous proposons de prendre en charge cette réalité évolutive avec un modèle Markov-modulé pour les protéines sous lequel les sites sont autorisés à modifier leurs préférences en acides aminés au cours du temps. L’estimation a posteriori des différents paramètres modulants du noyau stochastique avec les méthodes de Monte Carlo est un défi de taille que nous avons su relever partiellement grâce à la programmation parallèle. Des réglages computationnels sont par ailleurs envisagés pour accélérer la convergence vers l’optimum global de ce paysage multidimensionnel relativement complexe. Qualitativement, notre modèle semble être capable de saisir des signaux d’hétérogénéité temporelle à partir d’un jeu de données dont l’histoire évolutive est reconnue pour être riche en changements de régimes substitutionnels. Des tests de performance suggèrent de plus qu’il serait mieux ajusté aux données qu’un modèle équivalent homogène en temps. Néanmoins, les histoires substitutionnelles tirées de la distribution postérieure sont bruitées et restent difficilement interprétables du point de vue biologique. / Time-continuous Markovian process are widely used to understand the mechanism of nucleotidic acids and proteins evolution along phylogeny. Already existing probabilistic models based on such hypothesis are designed to satisfy the non-homogeneity of functional and environmental constraints acting across those biological sequences. Recently, Markov-modulated models have been introduced to describe site-specific temporal rate variation (heterotachy). Moreover, studies have demonstrated that not only strength but also the nature of the constraint acting on a specific site can vary over time. Here we propose to accommodate this evolutionary reality with a Markov-modulated model for proteins under which sites are authorized to change their amino acids propensities across time. Posterior estimation of the stochastic kernel hidden parameters with Monte Carlo methods is a challenging approach that we partially overcome with parallel computing. Fine-tuning are otherwise planned to accelerate convergence toward the target posterior stationnary distribution. Qualitatively, our model seems to be able to capture temporal heterogeneity from real sequences data sets whose evolutionary history is assumed to be rich in substitutional switch events. Furthermore, evaluation of the model performance suggest that he provides a better fit to the data set than the time-homogeneous equivalent model. Nonetheless, substitutional histories sampled from the posterior distribution are quite noisy and remain difficult to interpret biologically.

Traffic modeling in computer networks has been researched for decades. A good model should reflect the features of real-world network traffic. With a good model, synthetic traffic data can be generated for experimental studies; network performance can be analysed mathematically; service provisioning and scheduling can be designed aligning with traffic changes. An important part of traffic modeling is to capture the dependence, either the dependence among different traffic flows or the temporal dependence within the same traffic flow. Nevertheless, the power of dependence models, especially those that capture the functional dependence, has not been fully explored in the domain of computer networks. This thesis studies copula theory, a theory to describe dependence between random variables, and applies it for better performance evaluation and network resource provisioning. We apply copula to model both contemporaneous dependence between traffic flows and temporal dependence within the same flow. The dependence models are powerful and capture the functional dependence beyond the linear scope. With numerical examples, real-world experiments and simulations, we show that copula modeling can benefit many applications in computer networks, including, for example, tightening performance bounds in statistical network calculus, capturing full dependence structure in Markov Modulated Poisson Process (MMPP), MMPP parameter estimation, and predictive resource provisioning for cloud-based composite services. / Graduate / 0984 / fdong@uvic.ca

"A distributed queueing Medium Access Control (MAC) protocol is used in Distributed Queue Dual Bus (DQDB) networks. A modified version of the MAC protocol was proposed by R.R. Pillai and U. Mukherji in an attempt to overcome some of the shortcomings of the DQDB MAC protocol. They analyzed the performance of the system for Bernoulli arrivals and for large propagation delays between the nodes. We extend the performance analysis of the modified MAC protocol for a DQDB type of Network. The parameter of interest to us is the bus access delay. This has two components, viz., the request bus access delay and the data bu6 access delay. We use the model at the request point at node and present methods to evaluate the delay experienced in such a model. The model is an n-priority ./D/l queue with D vacations (non-preemptive priority) where n is the number of nodes sending requests on the request bus for transmission on the data bus. The methods presented help to evaluate the request bus access delay when the arrivals at each node are Markovian Arrival Processes (MAPs). The algorithms for evaluating the mean request bus access delay are based on matrix geometric techniques. Thus, one can use the algorithms developed in the literature to solve for the finite buffers case too. This model, for the request bus access delay, holds irrespective of the propagation delay between the nodes. We also evaluate the inter-departure time of class 1 customers and virtual customers in a 2-priority M/G/l system with G vacations (non-preemptive priority). In the case of Poisson arrivals at all the nodes, we would have a 2-priority M/D/l system with D vacations (non-preemptive priority). We thus evaluate the inter-arrival time of the free slots on the data bus as seen by Node 2. Note that this is independent of the number of active nodes in the network We then develop methods to evaluate the mean data bus access delay experienced by the customers at Node 2 in a three-node network with 2 nodes communicating with the third when the propagation delay between the nodes is large. We consider the case of finite Local Queue buffers at the two nodes. Using this assumption we arrive at process of arrivals to the Combined Queue and the process of free slots on the data bus to be Markov Modulated Bernoulli processes. The model at the combined queue at Node 2 then has a Quasi Birth-Death evolution. Thus, this system is solved by using the Ramaswami-Latouche algorithm. The stationary probabilities are then used to evaluate the mean data bus access delay experienced at Node 2. The finite buffer case of this system can be solved by G.Wi Stewart's algorithm. The method in modelling the system and the results are presented in detail for Poisson arrivals. The extension of this to more complex processes is also explained. We encounter in the analysis an explosion of the state-space of the system. We try to counter this by considering approximations to the process of free slots on the data bus. The approximations considered are on the basis of what are known as Idealized Aggregates. The performance of the approximation is also detailed. It works very well under low and moderate load but underestimates the mean delay under heavy load. Thereafter, we discuss the performance of the system with reference to the mean of the access delay and the standard deviation of the access delay under varying traffic at the two nodes. For this part we use simulation results to discuss the performance. The comparison between the performance measures at both the nodes is also done. Then we develop methods/techniques to understand the performance of the system when we have finite propagation delays between the nodes. We concentrate on the 3-node problem and calculate performance bounds based on linear programs. This is illustrated in detail for Bernoulli arrivals for the case of 1 slot propagation delay between the nodes as well as for the case of 2 slots propagation delay. The performance of the bounds obtained is also detailed. The presence of an idling system at the combined queue of Node 2 makes the bounds somewhat loose. Finally, we discuss the performance of the system with reference to the mean access delay and the standard deviation of the access delay under varying load on the system. Again, we rely on simulation studies. Finally, we study the performance of the system as a multiplexer. For this, we re­strict the traffic to Markov Modulated Processes (or those which would satisfy the Gartner-Ellis Theorem requirements). The traffic is characterized by what are known as Envelope Processes - Lower and Upper. The class of processes which satisfy the conditions of the Gartner-Ellis theorem come under the category where both the Envelope Processes exist and the Minimum Envelope Rate and the Maximum Lower Envelope Rate are the same. We use the system evolution equations at the combined queue at any node to develop re­lations between the various input and output processes. First, this is done for a. system of this kind, in isolation. Then, we consider this system as a part of the modified protocol and present relations, among the various input and output processes, which are specific to the modified protocol. The possible use of all of the above to do Admission Control at the entry point to the Asynchronous Transfer Mode (ATM) network is also presented.