<p>The purpose of this study is to identify an impact on an option pricing within NASDAQ OMX Stockholm Market, if the underlying</p><p>asset prices include jumps. The current financial crisis, when jumps are much more evident than ever, makes this issue very actual and important in the global sense for the portfolio hedging and other risk management applications for example for the banking sector. Therefore, an investigation is based on OMXS30 Index and SEB A Bank. To detect jumps the Barndorff-Nielsen and Shephard non-parametric bipower variation test is used. First it is examined on simulations, to be finally implemented on the real data. An affirmation of a jumps occurrence requires to apply an appropriate model for the option pricing. For this purpose the Kou model, a double exponential jump-diffusion one, is proposed, as it incorporates essential stylized facts not available for another models. Th parameters in the model are estimated by a new approach - a combined cumulant matching with lambda taken from the Barrndorff-Nielsen and Shephard test. To evaluate how the Kou model manages on the option pricing, it is compared to the Black-Scholes model and to the real prices of European call options from the Stockholm Stock Exchange. The results show that the Kou model outperforms the latter.</p>

本文改良了Shafiee-Topal(2010)所提出之跳躍模型之波動率，並歸納成三種模型:改良跳躍模型、改良平滑跳躍模型以及最佳化跳躍模型，並運用時間序列模型探討樣本期間內黃金價格。第一部份比較三種跳躍模型與Shafiee-Topal模型在訓練集及測試集的預測結果，並預測2012年至2018年之黃金價格走勢。第二部份探討黃金價格、原油價格以及美元加權指數之間的互動關係，建立多變數模型以預測黃金價格之長期趨勢。 首先，本文檢驗黃金價格、原油價格及美元加權指數樣本之恆定性，經由ADF 單根檢定法發現序列具有單根，進而使用TSP(Trend Stationary Process)估計模型參數。其次，黃金價格、原油價格及美元加權指數經共整合檢定發現，各模型變數間均具有共整合關係，即變數間具有長期均衡關係。黃金價格與原油價格呈正向反應，而黃金價格和原油價格與美元加權指數呈負向反應，除了受自身的預測解釋能力外，亦可以做為觀察其他變數的未來走勢方向及影響大小預估。最後，探討黃金價格受波動率的影響情形，本文改良Shafiee-Topal模型之波動率，並比較四種模型對黃金價格趨勢預測之結果，發現改良平滑跳躍模型在實際黃金價格波動率大時，其趨勢預測結果會優於Shafiee-Topal模型。 / This research advanced the volatility component (λ) of the jump and dip model (Shafiee and Topal,2010) on gold prices from 1968 to 2012 and estimated the gold price for the next 6 years. Based on the trend stationary process, we defined the three components and derived three new models: Adjusted Jump and Dip Model, Adjusted Smooth Jump and Dip Model and Optimized Jump and Dip Model. First part of the thesis compared the performance in prediction of the training data and the testing data for three different models and the jump and dip model. Second part of the thesis investigated the relationship among the gold price, crude oil price, and trade weighted U.S. dollar index of the concepts The result illustrated the long term trend of gold price described by a multivariate predictive model. We found evidence that different levels of volatility affect the prediction of gold price, and the adjusted jump and dip Model performs best when the true volatility is relatively high.

The Thesis describes (i) the development of add-on instrumentation extending the capabilities of conventional NMR spectrometers and (ii) the application of the designed equipments and techniques for investigating various colloidal systems. The new equipments are: Novel designs of stopped-flow and temperature–jump inserts intended for conventional Bruker wide-bore superconductive magnets. Both inserts are loaded directly from above into the probe space and can be used together with any 10 mm NMR probe with no need for any auxiliary instruments. A set of 5 mm and 10 mm 1H – 19F – 2H NMR probes designed for heteronuclear 1H – 19F cross-relaxation experiments in Bruker DMX 200, AMX 300 and DMX 500 spectrometers, respectively. A two–stage low-pass filter intended for suppressing RF noise in electrophoretic NMR experiments. The kinetics of micellar dissolution and transformation in aqueous solutions of sodium perfluorooctanoate (NaPFO) is investigated using the stopped-flow NMR instrument. The sensitivity of NMR as detection tool for kinetic processes in micellar solutions is clarified and possible artefacts are analysed. In the NaPFO system, the micellar dissolution is found to proceed faster than 100 ms while surfactant precipitation occurs on the time scale of seconds-to-minutes. The kinetics of the coil-to–globule transition and intermolecular aggregation in a poly (Nisopropylacrylamide) solution are investigated by the temperature-jump NMR instrument. As revealed by the time evolution of the 1H spectrum, the T2 relaxation time and the self-diffusion coefficient D, large (&gt;10 nm) and compact aggregates form in less than 1 second upon fast temperature increase and dissolve in less than 3 seconds upon fast temperature decrease. The intermolecular 1H – 19F dipole-dipole cross-relaxation between the solvent and solute molecules, whose fast rotational diffusion is in the extreme narrowing limit, is investigated. The solutes are perfluorooctanoate ions either in monomeric or in micellar form and trifluoroacetic acid and the solvent is water. The obtained cross-relaxation rates are frequency-dependent which clearly proves that there is no extreme narrowing regime for intermolecular dipole-dipole relaxation. The data provide strong constraints for the dynamic retardation of solvent by the solute. / QC 20100929

The purpose of this study is to identify an impact on an option pricing within NASDAQ OMX Stockholm Market, if the underlying asset prices include jumps. The current financial crisis, when jumps are much more evident than ever, makes this issue very actual and important in the global sense for the portfolio hedging and other risk management applications for example for the banking sector. Therefore, an investigation is based on OMXS30 Index and SEB A Bank. To detect jumps the Barndorff-Nielsen and Shephard non-parametric bipower variation test is used. First it is examined on simulations, to be finally implemented on the real data. An affirmation of a jumps occurrence requires to apply an appropriate model for the option pricing. For this purpose the Kou model, a double exponential jump-diffusion one, is proposed, as it incorporates essential stylized facts not available for another models. Th parameters in the model are estimated by a new approach - a combined cumulant matching with lambda taken from the Barrndorff-Nielsen and Shephard test. To evaluate how the Kou model manages on the option pricing, it is compared to the Black-Scholes model and to the real prices of European call options from the Stockholm Stock Exchange. The results show that the Kou model outperforms the latter.

Modern aircraft are getting more and better sensors. As a result of this, the pilots are getting moreinformation than they can handle. To solve this problem one can automate the information processingand instead provide the pilots with conclusions drawn from the sensor information. An aircraft’smovement can be used to determine which class (e.g. commercial aircraft, large military aircraftor fighter) it belongs to. This thesis focuses on comparing three classification schemes; a Bayesianclassification scheme with uniform priors, Transferable Belief Model and a Bayesian classificationscheme with entropic priors.The target is modeled by a jump Markov linear system that switches between different modes (flystraight, turn left, etc.) over time. A marginalized particle filter that spreads its particles over thepossible mode sequences is used for state estimation. Simulations show that the results from Bayesianclassification scheme with uniform priors and the Bayesian classification scheme with entropic priorsare almost identical. The results also show that the Transferable Belief Model is less decisive thanthe Bayesian classification schemes. This effect is argued to come from the least committed principlewithin the Transferable Belief Model. A fixed-lag smoothing algorithm is introduced to the filter andit is shown that the classification results are improved. The advantage of having a filter that remembersthe full mode sequence (such as the marginalized particle filter) and not just determines the currentmode (such as an interacting multiple model filter) is also discussed.

Introduction: The National Hockey League Entry Draft Combine (NHLED Combine) is considered one of the toughest physical fitness tests an ice hockey player has to go through. The NHLED Combine consists of several fitness tests evaluating the athlete’s aerobic- and anaerobic capacity; lower body power, upper body strength and power, flexibility and anthropometrics; no lower body strength test are employed. Squats are the only exercise used by all National Hockey League (NHL) strength and conditioning coaches yet it is not included in the NHLED Combine. Purpose: The purpose of this study was to determine which off-ice test correlates best with on-ice performance measured as forward skating speed. We hypothesised that squat one repetition maximum (squat 1RM) would be a better or equal predictor of on-ice performance compared to the current NHLED Combine tests standing long jump (SLJ) and Wingate anaerobic test (WAnT). Method: Eleven male subjects, aged 17.8 ± 0.8 years, performed an on-ice sprint followed by the off-ice tests SLJ, WAnT and squat 1RM. Results: A correlation was found between sprint time on-ice and SLJ (r= -0,727, p= 0.006), Wingate anaerobic test mean power/ body weight (WAnT MP/BW) (r= -0,607, p= 0,024), squat 1RM (r= -0,600, p= 0.026) and squat 1 repetition maximum/body weight (squat 1RM/BW) (r= -0,609, p= 0.023). Conclusion: The results indicate that squat 1RM and squat 1RM/BW are equally good predictors of hockey performance as SLJ and WAnT MP/BW. / knäböj, hockey, is, nhl, skridskoåkning, skridskoskär, sprint, horisontalhopp, wingate, styrka, kraftutveckling, fystest

Jump diffusion models are being used more and more often in financial applications. Consisting of a Brownian motion (with drift) and a jump component, such models have a number of parameters that have to be set at some level. Maximum Likelihood Estimation (MLE) turns out to be suitable for this task, however it is computationally demanding. For a complicated likelihood function it is seldom possible to find derivatives. The global maximum of a likelihood function defined for a jump diffusion model can however, be obtained by numerical methods. I chose to use the Bound Optimization BY Quadratic Approximation (BOBYQA) method which happened to be effective in this case. However, results of Maximum Likelihood Estimation (MLE) proved to be hard to interpret.

In the literature, many methods are proposed to value American options. However, due to computational difficulty, there are only approximate solution or numerical method to evaluate American options. It is not easy for general investors either to understand nor to apply. In this thesis, we build up an option pricing and virtual asset model system, which provides a friendly environment for general public to calculate early exercise boundary of an American option. This system modularize the well-handled pricing models to provide the investors an easy way to value American options without learning difficult financial theories. The system consists two parts: the first one is an option pricing system, the other one is an asset model simulation system. The option pricing system provides various option pricing methods to the users; the virtual asset model system generates virtual asset prices for different underlying models.

In this thesis, we expand the option pricing and virtual asset model system by Cheng (2005) and include new simulations and maximum likelihood estimation of the parameter of the stochastic differential equations. For easy manipulation of general users, the interface of original option pricing system is modified. In addition, in order to let the system more completely, some stochastic models and methods of pricing and estimation are added. This system can be divided into three major parts. One is an option pricing system; The second is an asset model simulation system; The last is estimation system of the parameter of the model. Finally, the analysis for the data of network are carried out. The differences of the prices between estimator of this system and real market are compared.

Inference of regulatory relations in dynamical systems is a promising active research area. Recently, most of the investigations in this field have been stimulated by the researches in functional genomics. In this thesis, the inferential modeling problem for switching hybrid systems is studied. The hybrid systems refers to dynamical systems in which discrete and continuous variables regulate each other, in other words the jumps and flows are interrelated. In this study, piecewise linear approximations are used for modeling purposes and it is shown that piecewise linear models are capable of displaying the evolutionary characteristics of switching hybrid systems approxi- mately. For the mentioned systems, detection of switching instances and inference of locally linear parameters from empirical data provides a solid understanding about the system dynamics. Thus, the inference methodology is based on these issues. The primary difference of the inference algorithm is the idea of transforming the switch- ing detection problem into a jump detection problem by derivative estimation from discrete data. The jump detection problem has been studied extensively in signal processing literature. So, related techniques in the literature has been analyzed care- fully and suitable ones adopted in this thesis. The primary advantage of proposed method would be its robustness in switching detection and derivative estimation. The theoretical background of this robustness claim and the importance of robustness for real world applications are explained in detail.