Modelling defaultable contingent claims has attracted a lot of interest in recent years, motivated in particular by the Late-2000s Financial Crisis. In several papers various approaches on the subject have been made. This thesis tries to summarize these results and derive explicit formulas for the prices of financial derivatives with credit risk. It is divided into two main parts. The first one is devoted to the well-known theory of modelling the default risk while the second one presents the results concerning pricing of the defaultable models that we obtained ourselves.

Previously we have presented a new scan-matching algorithm, based on the IDC - Iterative Dual Correspondence- algorithm, which showed a good localization performance even in the case of severe changes in the environment. The Problem of the IDC-algorithm is that there is no good way to estimate the covariance matrix of the position estimate, which prohibits an effective fusion with other position estimates from other sensors, e.g by means of the Kalman filter. In this paper we present a new way to estimate the covariance matrix, by estimating the Hessian matrix of the error function that is minimized by the IDC scan-matching algorithm. Simulation results show that the estimated covariance matrix correspond well to the real one.

Increasingly, absolute frequency and orientation maps are needed, e.g. for forensics. We introduce a non-linear scale space via the logarithm of trace of the Structure Tensor. Therein, frequency estimation becomes an orientation estimation problem. We show that this offers significant advantages, including construction of efficient isotropic estimations of dense maps of frequency. In fingerprints, both maps are shown to improve each other in an enhancement scheme via Gabor filtering. We suggest a novel continuous ridge counting method, relying only on dense absolute frequency and orientation maps, without ridge detection, thinning, etc. Furthermore, we present new evidence that frequency maps are useful attributes of minutiae. We verify that the suggested method compares favorably with state of the art using forensic fingerprints as test bed, and test images where the ground truth is known. In evaluations, we use public data sets and published methods only.

In this thesis we present a new space-time model of interacting agents in the financial market. It is a combination of the Curie-Weiss model and a model introduced by Järpe. We investigate properties such as the critical temperature and magnetization of the system. The distribution of the Hamiltonian function is obtained and a hypothesis test of independence is derived. The results are illustrated in an example based on real data.

To understand and model the dynamics of the implied volatility smile is essential for trading, pricing and risk management portfolio. We suggest a  linear Kalman filter for updating of the Stochastic Volatility Inspired (SVI) model of the volatility. From a risk management perspective we generate the 1-day ahead forecast of profit and loss (P\&L) of option portfolios. We compare the estimation of the implied volatility using the SVI model with the cubic polynomial model. We find that the SVI Kalman filter has outperformed the  others.

This work proposes a new exchangeability test for a random sequence through a martingale-based approach. Its main contributions include 1) an additive martingale which is more amenable for designing exchangeability tests by exploiting the Hoeffding-Azuma lemma and 2) different betting functions for constructing the additive martingale. By choosing the underlying probability density function of p-values as a betting function, it can be shown that, when a change-point appears, a satisfying trade-off between the smoothness and expected one-step increment of the martingale sequence can be obtained. An online algorithm based on beta distribution parametrization for constructing this betting function is discussed in detail as well. © 2021, IGI Global.

In this thesis we present the Laplace transform method of option pricing and it's realization, also compare it with another methods. We consider vanilla and exotic options, but more attention we pay to the two-asset correlation options. We chose the one of the modifications of Black-Scholes model, the Kou double exponential jump-diffusion model with the double exponential distribution of jumps, as model of the underlying stock prices development. The computations was done by the Laplace transform and it's inversion by the Euler method. We will present in details proof of finding Laplace transforms of put and call two-asset correlation options, the calculations of the moment generation function of the jump-diffusion by Levy-Khintchine formulae in cases without jumps and with independent jumps, and direct calculation of the risk-neutral expectation by solving double integral. Our work also contains the programme code for two-asset correlation call and put options. We will show the realization of our programme in the real data. As a result we see how our model complies on the NASDAQ OMX Stock-holm Market, considering the two-asset correlation options on three cases by stock prices of Handelsbanken, Ericsson and index OMXS30.

A data proximity matrix is an important information source in random forests (RF) based data mining, including data clustering, visualization, outlier detection, substitution of missing values, and finding mislabeled data samples. A novel approach to estimate proximity is proposed in this work. The approach is based on measuring distance between two terminal nodes in a decision tree. To assess the consistency (quality) of data proximity estimate, we suggest using the proximity matrix as a kernel matrix in a support vector machine (SVM), under the assumption that a matrix of higher quality leads to higher classification accuracy. It is experimentally shown that the proposed approach improves the proximity estimate, especially when RF is made of a small number of trees. It is also demonstrated that, for some tasks, an SVM exploiting the suggested proximity matrix based kernel, outperforms an SVM based on a standard radial basis function kernel and the standard proximity matrix based kernel. © 2012 Elsevier Ltd. All rights reserved.

This study is based on three models, Markov model, Hidden Markov model and the Radial basis function neural network. A number of work has been done before about application of these three models to the stock market. Though, individual researchers have developed their own techniques to design and test the Radial basis function neural network. This paper aims to show the different ways and precision of applying these three models to predict price processes of the stock market. By comparing the same group of data, authors get different results. Based on Markov model, authors find a tendency of stock market in future and, the Hidden Markov model behaves better in the financial market. When the fluctuation of the stock price index is not drastic, the Radial basis function neural network has a nice prediction.

This paper proposes a method for conducting quantitative inductive research on survey data when the variable of interest follows an ordinal distribution. A methodology based on novel and traditional penalising models is described. The main aim of this study is to pedagogically present the method utilising the new penalising methods in a new application. A case was employed to outline the methodology. The case aims to select explanatory variables correlated with the target debt level in Swedish listed companies. The survey respondents were matched with accounting information from the companies’ annual reports. However, missing data were present: to fully utilise penalising models, we employed classification and regression tree (CART)-based imputations by multiple imputations chained equations (MICEs) to address this problem. The imputed data were subjected to six penalising models: grouped multinomial lasso, ungrouped multinomial lasso, parallel element linked multinomial-ordinal (ELMO), semi-parallel ELMO, nonparallel ELMO, and cumulative generalised monotone incremental forward stagewise (GMIFS). While the older models yielded several explanatory variables for the hypothesis formation process, the new models (ELMO and GMIFS) identified only one quick asset ratio. Subsequent testing revealed that this variable was the only statistically significant variable that affected the target debt level. © 2024 by the authors.

Data in sport and exercise psychology research are often analyzed based on the assumption that the relationships between two or more variables are linear in nature. But are all relationships in sport and exercise settings linear? The aim of this paper is to: a) discuss the potential shortcomings with using linear regression analysis, b) introduce quantile regression analysis (Q-regression) as an alternative to linear regression, and c) give examples of how to use Q-regression analysis in order to overcome some of the shortcomings of linear regression analysis. A comparison between the results from a linear regression analysis and a Q-regression analysis shows differences between the two methods. More specifically, the independent variables in the results of the Q-regression analysis were shown to have non-linear relationships with the dependent variable in given examples. Researchers are encouraged to consider using Q-regression analysis in studies where non-linear relationships could be expected.

Sport injuries are a major problem associated with sport participation. To develop preventive strategies and programs, it is important to identify factors that will increase the likelihood of sport injuries. In most sport injury risk factor research, statistical analyses are performed; however, many of the most common statistical analyses provide limited information about predictors of sport injury risk. The common analyses used in previous studies do not acknowledge the complexity associated with investigating risk factors for sport injuries. To better capture this complexity, suggested in most theoretical frameworks, more appropriate of statistical approaches should be used. In this article we present how latent profile analysis, latent change score analysis, and latent growth curve analysis can be used to overcome some of the limitations with more traditional analyses. Lastly, we also elaborate on future directions for analyses in sport injury risk factor research. More specifically, we present how advanced statistical models, such as classification and regression trees (CART) analysis and random forest analysis, can be used to provide researchers and clinicians with results that are more clinically meaningful.

Uppsatsen undersöker befolkningsförändring sett ur ett migrationsperspektiv under perioden 2000-2009. Studien visar att 145 av Sveriges 290 kommuner under 2000-talet har minskat i befolkning medan 140 kommuner har ökat i befolkning. Det grundläggande pro-blemet uppsatsen belyser är det faktum att 15 av dessa kommuner minskat i befolkning med mer än 10 procent. Utöver detta tillkommer att 29 kommuner haft en befolkningsökning på mer än 10 procent. Utvecklingen orsakar en skevhet i den kommunala organisationen som medför problem ibland annat den kommunala budgeteringen. Studiens utvalda förklaringsva-riabler är ett begränsat urval som baserats på tidigare forskning och teori kring migration och flyttmönster. Förklaringsvariablerna som användes behandlade följande områden:

arbetslöshet, utbildningsgrad, nystartade företag, skatteunderlag, kommunala utgifter, barnomsorgskostnad, nettokostnad för äldre och funktionshindrade samt jämställdhet. Uppsatsens syfte är inte att ge en heltäckande förklaringsmodell utan att undersöka ett urval av möjliga förklar-ingsfaktorer. Den totala förklaringsmodellen lämnas således för framtida forskare att reda ut.

Av studiens resultat framgår ingen av variablerna som dominerande gällande förklaringskraft, däremot framstår de totala kommunala utgifterna, barnomsorgskostnad och antal nystartade företag som viktigast sett till kommunal befolkningsutveckling. Skatteunderlag och jämställdhet var de variabler av minst betydelse.

The surface properties of newsprint and other paper qualities are to a great extent determined by the properties of the cellulose fibres. An appropriate description of these fibres as they appear in the paper is therefore important and can be used for quality classification and process monitoring. We suggest a model that considers the fibre geometry and appearance. It is based on a two-dimensional shot-noise process. The model is fit by minimizing a weighted least squares distance between the model-based and estimated covariance functions and this provides estimates of the fibre size, intensity and the non-uniform distribution of the fibre orientation. The model is applied to simulated and real data.

In this work we address to the problems of the loan origination decision-making systems. In accordance with the basic principles of the loan origination process we considered the main rules of a clients parameters estimation, a change-point problem for the given data and a disorder moment detection problem for the real-time observations. In the first part of the work the main principles of the parameters estimation are given. Also the change-point problem is considered for the given sample in the discrete and continuous time with using the Maximum likelihood method. In the second part of the work the disorder moment detection problem for the real-time observations is considered as a disorder problem for a non-homogeneous Poisson process. The corresponding optimal stopping problem is reduced to the free-boundary problem with a complete analytical solution for the case when the intensity of defaults increases. Thereafter a scheme of the real time detection of a disorder moment is given.

A model which possesses both spatial and time dependence is the Markov chain Markov field (see X. Guyon, 1995). Here inference about the parameter for spatio-temporal interaction of a special case of a Markov chain Markov field model is considered. A statistic which is minimal sufficient for the interaction parameter and its asympotic distribution are derived. A condition for stationarity of the sufficient statistic process and the stationary distribution are given. Likelihood based inference such as estimation, hypothesis testing and monitoring are briefly examined.

Ämnet kryptologi, dvs. kryptering, dekryptering och kodknäckning ­omfattar såväl matematik, dator­programmering som allmän finurlighet. Denna bok behandlar Caesarkrypto, substitutionskrypto, Vignèrekrypto, RSA-krypto och den bakom­liggande ­matematiken (ekvations­lösning, räkning med exponential­uttryck, resträkning, primtalsteori och rekursion) samt angränsande matematik (såsom kombinatorik, statisti­ska metoder för t.ex. detektering av krypterad kod och beräkning av överföringskvalitet). Boken innehåller även en så pass bred ­genomgång av elementär algebra (mängdlära, logik, trigono­metri, komplexa tal och rekurrensekva­tioner) och analys (funktioner i en variabel, derivata och integraler) att den kan användas vid inledande studier i matematik inom en mängd olika utbildningar. Boken syftar till att ge en introduktion till kryptologisk problem­lösning och visa på de stora synergieffekter som uppnås genom att tillämpa en väl­balanserad ­kombination av grundläggande matematik och elementär programmering inom området. Förhoppningen är också att läsaren, sporrad av de nyvunna insikterna om kryptologi, lockas till vidare kunskapsfördjupning. Boken vänder sig i första hand till blivande IT-forensiker som kan behöva kompetensen att kryptera och knäcka krypton i sin yrkesroll men som inte har en omfattande matematisk förkunskap. Den ­vänder sig även till studenter på landets tekniska högskolor och ­universitet.

Surveillance to detect changes of spatial patterns is of interest in many areas such as environmental control and regional analysis. Here the interaction parameter of the Ising model, is considered. A minimal sufficient statistic and its asymptotic distribution are used. It is demonstrated that the convergence to normal distribution is rapid. The main result is that when the lattice is large, all approximations are better in several respects. It is shown that, for large lattice sizes, earlier results on surveillance of a normally distributed random variable can be used in cases of most interest. The expected delay of alarm at a fixed level of false alarm probability is examined for some examples. Copyright © 1999 by Marcel Dekker, Inc.

We study, by means of simulations, the performance of the Shewhart method, the Cusum method, the Shiryaev-Roberts method and the likelihood ratio method in the case when the true shift differs from the shift for which the methods are optimal. The methods are compared for a fixed expected time until false alarm. The comparisons are made with respect to some measures associated with power such as probability of alarm when the change occurs immediately, expected delay of true alarm and predictive value of an alarm. Copyright © 2000 by Marcel Dekker, Inc.

The exchange rate market has traditionally played a key role in the financial market. The variation of the exchange rate which is called volatility is also an important feature for studying the exchange rate market because the increased volatility may have a negative effect on a nation's economy by increasing the uncertainty in the exchange market. In this paper the volatility of the exchange rate is considered by means of a Heterogeneous Autoregression Conditional Heteroskedastictity (HARCH) Model. It explains the volatility of the exchange rate market well. In addition, it is assumed that at a random time point a change of a parameter in the distribution of the random process underobservation may occur. Some methods such as the Shewhart method, the Culumative Sum Method (CUSUM) and the ExponentiallyWeighted Moving Average Method (EWMA) are investigated within the frames of this change-point problem. In order to evaluate them, Average Run Length (ARL) and Conditional Expected Delay (CED) will be used asperformance measures.

The aim of this paper is to present simulation methods for the pricing of American financial instruments. Three methods are presented. Each differs from the others in it's approach to the problem and the method of finding a solution. We illustrate the variety of possible approaches that can be adopted when dealing with this complicated problem. The results of using these algorithms are compared with examples found in literature on the subject. We try to identify the factors that influence price estimators and provide some new results about the properties and distributions of those estimators. We show that even a simple variance reduction technique has a positive effect for these algorithms. The purpose of this paper is to present the effectiveness of a simulation method in pricing American options. This is contrary to the opinion often stated in articles and monographs that the simulation approach is not adequate for the task. We provide an overview and comparison of earlier methods proposed and follow this with an extended discussion. This paper sets the foundations for further research into use of these algorithms for multidimensional problems, where they may offer a substantial advantage over deterministic methods.

The diversity of components in electricity distribution grids makes it impossible, or at least very expensive, to deploy monitoring and fault diagnostics to every individual element. Therefore, power distribution companies are looking for cheap and reliable approaches that can help them to estimate the condition of their assets and to predict the when and where the faults may occur. In this paper we propose a simplified representation of failure patterns within historical faults database, which facilitates visualization of association rules using Bayesian Networks. Our approach is based on exploring the failure history and detecting correlations between different features available in those records. We show that a small subset of the most interesting rules is enough to obtain a good and sufficiently accurate approximation of the original dataset. A Bayesian Network created from those rules can serve as an easy to understand visualization of the most relevant failure patterns. In addition, by varying the threshold values of support and confidence that we consider interesting, we are able to control the tradeoff between accuracy of the model and its complexity in an intuitive way. © 2016 IEEE

The problem of managing the portfolio provisions is of very high importance for any financial institution. In this paper we provide both static and dynamic models of provisions estimation for the case when the decision about provisions is made at the first moment of time subject to the absence of information and for the case of complete and incomplete information. Also the hedging strategy for the case of the defaultable market is presented in this work as another tool of reducing the risk of default. The default time is modelled as a first-passage time of a standard Brownian motion through a deterministic barrier. Some methods of numerical provision estimation are also presented.

The paper deals with the problem of finding an optimal one-time rebalancing strategy for the Bachelier model, and makes some remarks for the similar problem within Black-Scholes model. The problem is studied on finite time interval under mean-square criterion of optimality. The methods of the paper are based on the results for optimal stopping problem and standard mean-square criterion.

The solution of the problem, considered in the paper, let us interpret how and - that is more important for us -when investor should rebalance the portfolio, if he wants to hedge it in the best way. 

The distribution of asset returns may lead to structural breaks. Thesebreaks may result in changes of the optimal portfolio weights. For a port-folio investor, the ability of timely detection of any systematic changesin the optimal portfolio weights is of a great interest.In this master thesis work, the use of the Shewhart method, as amethod for detecting a sudden parameter change, the implied changein the multivariate portfolio weights and its performance is reviewed.

Clearing houses or huge broker-dealers usually apply portfolio approach for the estimation of traders’ liabilities and restriction of their risk. It supposes that they analyse possible changes of portfolio’s value within a given time horizon. For clearing houses (in USA as well as in Europe) there exists a standard methodology called SPAN, developed by Chicago Mercantile Exchange, where scenario approach is implemented. Sometimes this approach fails due to the finite number (16) of considered scenarios. It seems that parametric approach provides better quality, but in this case man needs a fast algorithm for the Implied Volatility (IV) curve forecast. By now there exists an algorithm which uses a polynomial approximation for the IV curve merged with Kalman Filtering procedure as a forecasting tool. We develop a new model and provide statistical evidence which shows that approximation of IV curve by a special class of functions give better results. Correspondent analysis is based on the data from US options market.

This paper introduces a new type of brain emotional learning inspired models (BELIMs). The suggested model is  utilized as a suitable model for predicting geomagnetic storms. The model is known as BELPM which is an acronym for Brain Emotional Learning-based Prediction Model. The structure of the suggested model consists of four main parts and mimics the corresponding regions of the neural structure underlying fear conditioning. The functions of these parts are implemented by assigning adaptive networks to the different parts. The learning algorithm of BELPM is based on the steepest descent (SD) and the least square estimator (LSE). In this paper, BELPM is employed to predict geomagnetic storms using the Disturbance Storm Time (Dst) index. To evaluate the performance of BELPM, the obtained results have been compared with the results of the adaptive neuro-fuzzy inference system (ANFIS). © 2014 Polish Information Processing Society.

In this paper we examine energy derivatives pricing. The previous studies considered the same source of uncertainty for the spot and the futures prices. We investigate the problem of futures pricing with two independent sources of risk. In general the structure of the oil and gas futures markets is closely related to some stock indices. Therefore, we develop a model for the futures market and compound derivatives with pricing in accordance with the correspondent index. We derive a framework for energy derivatives pricing, compute the price of the European call option on futures and corresponding hedging strategy. We calculate the price of the European call option adjusted for an index level, study the American put option on futures and corresponding hedging strategies.

The advancements of the telematics and connectivity solutions have provided new opportunities for the field of predictive maintenance. The number of sensors installed on a vehicle is increasing over time, and manufacturers are looking for new ways to improve the uptime of their fleet while at the same time reducing the costs related to unexpected breakdowns. The nature of the aggregated data from vehicles is sequential, and it is interesting to investigate existing methods for modeling partially observable state sequences to detect common patterns of failure. In this paper, we introduce a new approach for predicting turbocharger failures of Volvo trucks. The first step of the method deals with modeling a sequence of readouts from each vehicle using a Markov process. To do so, we identify the most informative signals and then employ spatial similarity clustering on the readouts. We interpret each cluster as a Markov state and further convert the history of a truck into a trajectory of states. This trajectory is then aligned with repairs information to form a standard sequence labeling problem. Finally, we train a hidden Markov model (HMM) classifier for assessing the health condition of the equipment. Empirical evaluations obtained on our realworld dataset of trucks suggest that the proposed method improves the AUC score of the final system up to 6% for predicting failures of a turbocharger.

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.

Asian options are exotic financial derivative products which price must be calculated by numerical evaluation. In this thesis, we study certain ways of solving partial differential equations, which are associated with these derivatives. Since standard numerical techniques for Asian options are often incorrect and impractical, we discuss their variations, which are efficiently applicable for handling frequent numerical instabilities reflected in form of oscillatory solutions. We will show that this crucial problem can be treated and eliminated by adopting flux limiting techniques, which are total variation dimishing.

We present a simple trick to get an approximate estimate of the weight decay parameter lambda. The method combines early stopping and weight decay, into the estimate lambda=parallel to del E(W(es))parallel to/parallel to 2W(es)parallel to, where W(es) is the set of weights at the early stopping point, and E(W) is the training data fit error. The estimate is demonstrated and compared to the standard cross-validation procedure for lambda selection on one synthetic and four real life data sets. The result is that lambda is as good an estimator for the optimal weight decay parameter value as the standard search estimate, but orders of magnitude quicker to compute. The results also show that weight decay can produce solutions that are significantly superior to committees of networks trained with early stop ping.

In this thesis we investigate the case of monitoring of stocks havingjust been introduced for public trading on the nancial market. Theempirical distribution of the change-point for 20 assets for 60 days was calculated to check the support for the assumption that the priceinitially drop or rise to some steady level.The price process X = {Xt : t in Z} is assumed to be an AR(1) process with a shift in the mean value from a slope to a constant. The Shiryaev-Roberts, Shewhart, EWMA, Likelihood ratio and CUSUM proceduresfor detecting a change-point in such a process are derived. The expecteddelay of the motivated alarm according to these methods is achievedunder the assumptions of a Poisson, uniform, binomial and geometric distributed 

by means of simulations.

One of the major problem faced by banks is how to manage the risk

exposure in large portfolios. According to Basel II regulation banks

has to measure the risk using Value-at-Risk with confidence level 99%.

However, this regulation does not specify the way to calculate Valueat-

Risk. The easiest way to calculate Value-at-Risk is to assume that

portfolio returns are normally distributed. Altough, this is the most

common way to calculate Value-at-Risk, there exists also other methods.

The previous crisis shows that the regular methods are unfortunately

not always enough to prevent bankruptcy. This paper is devoted to

compare the classical methods of estimating risk with other methods

such as Cornish-Fisher Expansion (CFVaR) and assuming generalized

hyperbolic distribution. To be able to do this study, we estimate the risk

in a large portfolio consisting of ten stocks. These stocks are chosen from

the NASDAQ 100-list in order to have highly liquid stocks (bluechips).

The stocks are chosen from different sectors to make the portfolio welldiversified.

To investigate the impact of dependence between the stocks

in the portfolio we remove the two most correlated stocks and consider

the resulting eight stock portfolio as well. In both portfolios we put equal

weight to the included stocks.

The results show that for a well-diversified large portfolio none of the

risk measures are violated. However, for a portfolio consisting of only

one highly volatile stock we prove that we have a violation in the classical

methods but not when we use the modern methods mentioned above.

Time series forecasting is an important problem with various applications in different domains. Improving forecast performance has been the center of investigation in the last decades. Several research studies have shown that old statistical method, such as ARIMA, are still state-of-the-art in many domains and applications. However, one of the main limitations of these methods is their low performance in longer horizons in multistep scenarios. We attack this problem from an entirely new perspective. We propose a new universal post-correction approach that can be applied to fix the problematic forecasts of any forecasting model, including ARIMA. The idea is intuitive: We query the last window of observations plus the given forecast, searching for similar “shapes” in the history, and using the future shape of the nearest neighbor, we post-correct the estimates. To ensure that post-correction is adequate, we train a meta-model on the successfulness of post-corrections on the training set. Our experiments on three diverse time series datasets show that the proposed method effectively improves forecasts for 30 steps ahead and beyond. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The work is focused on the Value at Risk and the Expected Shortfallcalculation. We assume the returns to be based on two pillars - the white noise and the stochastic volatility. We assume that the white noise follows the NIG distribution and the volatility is modeled using the nGARCH, NIG-GARCH, tGARCH and the non-parametric method. We apply the models into the stocks of three Banks of the Nordic market. We consider the daily and the intraday returns with the frequencies 5, 10, 20 and 30 minutes. We calculate the one step ahead VaR and ES for the daily and the intraday data. We use the Kupiec test and the Markov test to assess the correctness of the models. We also provide a new concept of improving the daily VaR calculation by using the high frequency returns. The results show that the intraday data can be used to the one step ahead VaR and the ES calculation. The comparison of the VaR for the end of the following trading day calculated on the basis of the daily returns and the one computed using the high frequency returns shows that using the intraday data can improve the VaR outcomes.

Additive Manufacturing (AM) is increasingly being used in healthcare sectors for its potential to fabricate patient-specific customized implants, and specifically in dentistry, AM finds its applications in maxillofacial implants, dentures, and other prosthetic aids. However, in most applications, AM is largely being used for prototyping purposes. The full-scale realization of AM can only be achieved if the downsides of AM are addressed and resolved. Hence this paper focuses on providing a detailed analysis of surface quality, dimensional accuracy, and mechanical properties of the biocompatible material produced, using the Stereolithography (SLA) method for a dental application. For quality analysis, test artefacts were produced, and the quality was assessed before and after the sterilization process. The results suggest that micro-surface roughness essential for cell growth is similar for all build inclinations and well within the control limit required for effective bone regeneration. Multi-scale surface characterization revealed that the sterilization process involving heat can potentially alter the micro-roughness features of resin-based materials. The results from the dimensional analysis show that the SLA parts produced had negligible dimensional deviations from the CAD model to the printed parts and were unaffected by the sterilization process. The tensile test results suggest that the part orientation does not affect the tensile strength and that the sterilization process seems to have an insignificant effect on the tensile properties of the SLA parts. Furthermore, the results were validated by producing a membrane barrier for Guided Bone Regeneration (GBR). The validation results showed that excess resin entrapment was due to the geometrical design of the membrane barrier. In conclusion, this paper provides an overview of quality variations that can help in optimizing the AM and sterilization process to suit dental needs. © 2023 IOP Publishing Ltd.

This work mainly studies modeling and existence issues for martingale models of option markets with one stock and a collection of European call options for one fixed maturity and infinetely many strikes. In particular, we study Dupire's and Schweizer-Wissel's models, especially the latter one. These two types of models have two completely different pricing approachs, one of which is martingale approach (in Dupire's model), and other one is a market approach (in Schweizer-Wissel's model). After arguing that Dupire's model suffers from the several lacks comparing to Schweizer-Wissel's model, we extend the latter one to get the variations for the case of options on interest rate indexes and futures options. Our models are based on the newly introduced definitions of local implied volatilities and a price level proposed by Schweizer and Wissel. We get explicit expressions of option prices as functions of the local implied volatilities and the price levels in our variations of models. Afterwards, the absence of the dynamic arbitrage in the market for such models can be described in terms of the drift restrictions on the models' coefficients. Finally we demonstrate the application of such models by a simple example of an investment portfolio to show how Schweizer-Wissel's model works generally.

Password security is one of the most critical aspects of IT security, as password-based authentication is still the primary authentication method. Unfortunately, our passwords are subject to different types of weaknesses and various types of password-guessing attacks. The first objective of this thesis is to provide a general perception of the trends in offline password-guessing tools, methods, and techniques. The study shows that the most cited tools are Hashcat, John the Ripper, Ordered Markov ENumerator (OMEN), and PassGan. Methods are increasingly evolving and becoming more sophisticated by emerging Deep Learning and Neural Networks. Unlike methods and tools, techniques are not subject to significant development, noting that dictionary and rule-based attacks are at the top of used techniques. The second objective of this thesis is to explore to what extent Swedish personal names are used in real-life passwords. Hence, an experiment is conducted for this purpose. The experiment results show that about 26% of Swedish users use their personal names when they create passwords, making them vulnerable to easy guessing by password-guessing tools. Furthermore, a simple analysis of the resulting password recovery file is performed in terms of password length and complexity. The resulting numbers show that more than half of guessed passwords are shorter than eight characters, indicating incompliance with the recommendations from standard organizations. In addition, results show a weak combination of letters, digits, and special characters, indicating that many Swedish users do not maintain sufficient diversity when composing their passwords. This means less password complexity, making passwords an easy target to guess. This study may serve as a quick reference to getting an overview of trends in the password-guessing field. On the other side, the resulting rate of Swedish personal names in Swedish password leaks may draw the attention of active social actors regarding information security to improve password security measures in Sweden.

In this paper we introduce a model of default contagion in the financail market. The structure of the companies are represented by a Heavy Gravity Portfolio, where we assume there are N sectors in the market and in each sector i, there is one big trader and ni supply companies.The supply companies in each sector are directly inuenced by the bigtrader and the big traders are also pairwise interacting with each other.This development of the Ising model is called Heavy gravity portfolioand according to this, the relation between expectation and correlationof the default of companies are derived by means of simulations utilisingthe Gibbs sampler. Finally methods for maximum likelihood estimationand for a likelihood ratio test of the interaction parameter in the modelare derived.