Climate risks are becoming an increasingly important element of credit risk modelling, both because physical hazards such as floods or earthquakes can severely damage residential properties and the probability of such events occurring is rising globally. Additionally, recent regulatory guidelines require banks to assess and manage climate and environmental (CER) risks, as they can affect both mortgage borrowers and lenders. This research examines the current climate risk framework of the Dutch bank ABN AMRO and proposes a methodology to address one of its limitations, which assumes that climate damages are reflected only in collateral values and do not affect the creditworthiness of the associated mortgages, thereby placing the entire impact on the Loss Given Default (LGD). After outlining the theoretical background in both credit and climate risk, and analysing the bank?s three-step climate risk quantification process, a behavioural model extension is introduced that captures how borrowers may respond to climate-related physical damage. The approach distinguishes four possible borrower actions: no repair of climate-related damage, self-funded repair, repair financed through an additional ABN AMRO loan, or repair financed through an external loan. These pathways link climate damage to credit risk parameters through collateral value, disposable income and loan exposure, with behavioural probabilities - calibrated using expert input - that ultimately affect not only the Loss Given Default (LGD), but also the Probability of Default (PD) and Exposure at Default (EAD). Results show that including behavioural choices could increase estimated climate impacts on Risk-Weighted Assets (RWA) and Expected Loss (EL) by nearly a factor of two. At mortgage portfolio level, the impacts remain well below 1% and do not exceed the bank?s materiality thresholds, which is consistent with the low probability of extreme climate events in the Netherlands. Overall, the findings highlight that extending the current framework with behavioural choices can substantially alter outcomes, underscoring the importance of underlying assumptions. The proposed framework contributes to an improved impact estimation of physical climate risk on the mortgage portfolio, offering a foundation for further research and potential application in regions where climate risks are more severe.

This thesis aims to determine which reordering and preconditioning techniques best exploit the sparsity and structure of Jacobian matrices arising from the district heating networks of Gradyent, to reduce computation times and enable real-time optimization.
District heating networks play a crucial role in the sustainable urban energy supply. Gradyent aims to improve the efficiency and sustainability of district heating networks using their real-time digital twin model. A key challenge to enable fast and accurate simulations is solving large, sparse, and ill-conditioned linear systems of arising within the Newton-Raphson method efficiently.
The application of suitable, computationally effective and applicable reordering and preconditioning techniques based on the structure of the networks improves the convergence rate of direct and iterative methods. Thus, reduces the computational times of linear solvers. For this thesis, first, a thorough understanding of the structure of the networks is established. Subsequently, different reordering and preconditioning techniques are analysed to determine which can be most effective when applied on the linear systems arising from Gradyent’s district heating networks.
The approach analyses the sparsity and block-structure of the Jacobians to identify suitable reordering and preconditioning techniques. It evaluates the performance of the reordering strategies–Reverse Cuthill–McKee, Approximate Minimum Degree, and Maximum Bipartite Matching–for reducing fill-in, bandwidth, and enhancing diagonal dominance. In addition, it investigates the effectiveness of ILU and block preconditioning methods, as well as hybrid techniques. Both direct (LU) and iterative (GMRES) linear solvers are used for this assessment.

In the operation of an electricity grid, such as the Dutch transmission system operated by TenneT, decisions need to be made on how to operate components in the grid. These decisions require a complex weighing of the associated costs and effects on the operating state of the grid. Optimal Power Flow (OPF) is a class of mathematical optimization problems that capture the effects of each decision, and aims to find an optimal way of controlling the electricity grid. OPF is well described in scientific literature, and both open-source and proprietary software exists to perform OPF calculations. However, most implementations are limited in their capabilities. Furthermore, applying existing methods to models of real-world grids is not always straightforward. During the calculations, problems can occur that can either slow down or completely prevent the methods from obtaining a solution. We call these convergence problems. In this thesis, two such types of convergence problems have been identified. The first convergence problem involves the modelling of switches, and the second involves the modelling of parallel transformers. Both problems have been investigated and strategies have been found to resolve these problems. The result is an OPF method that convergences on a model of the Dutch transmission network. The implementation of this model is very flexible, allowing it to be applied to a wide variety of OPF problems. This is an important step towards the application of OPF in operation of the Dutch transmission grid. The method has been tested on two different models, one of which is a model of the Dutch transmission grid. Both the computational performance and the resulting solutions have been compared for various OPF calculations.

Quantum annealing is the continuous transformation of the energy working on a quantum system and then measuring said system. The adiabatic theorem and the Ising model let us leverage quantum annealing to find minimal solutions to quadratic unconstrained binary optimization (QUBO) models by setting the energy present during quantum annealing. In this thesis we investigate the QUBO-dization of the multiple object tracking (MOT) problem and perform parameter optimization on the Lagrangian multiplier, chain strength and annealing time parameters as well as test the effects on quantum annealing performance acquired by adding a pause or quench to the anneal schedule or by performing reverse annealing. Experiments were performed on state of the art quantum annealers developed by D-Wave and MOT problem instances were made by us to provide minimally preprocessed QUBO matrices. Reverse annealing was found to have a better performance than standard anneal schedules and we perceived a relation between the annealing time and quantum annealing performance. During testing we found additional factors that contribute to the performance such as the embedding of the Ising model onto the qubits of the quantum annealer. We combine our findings to provide a full quantum annealing schedule and initial state suitable for quantum annealing on state of the art quantum annealers developed by D-Wave.

Migraine is a prevalent, complex neurovascular disorder that mainly affects women. The exact pathophysiology of migraine is unclear, but research indicates that activation of the trigeminal nerve in the trigeminovascular system causes release of calcitonin gene-related peptide (CGRP), which triggers migraine attacks. Additionally, nitric oxide (NO) contributes to the pathophysiology of migraine headaches. Research suggests that migraine patients have an increased risk of cardiovascular disease. Over the past decades, non-invasive techniques, like Laser Doppler imaging (LDI) and Laser speckle contrast imaging (LSCI) have been developed for imaging tissue perfusion, which are valuable tools for investigating the underlying causes for this increased risk and facilitates the study of blood perfusion. The Erasmus Medical Center (Erasmus MC) used these techniques to perform measurements of the microvascular blood flow in the forearm as a measure of the microvasculature of women with and without migraine. This study, also known as the VASCULAR-study, focused on three regions of interest (ROIs), where NO was inhibited using iontophoresis with L-NMMA and neuropeptides were blocked using EMLA cream. The last ROI served as the control region. The measurements also consisted of three different phases: baseline, peak and plateau phase. Studies have shown that distinct biological mechanisms in the body can be linked to different frequency intervals and therefore, Fourier analysis (FA) and wavelet analysis (WA) were used to transform the VASCULAR-study data into the frequency domain. Two preliminary studies used FA to perform the transformation. This study primarily focused on WA. The research question is as follows: Does wavelet analysis (WA) of the VASCULAR-study data yield more insights than Fourier analysis (FA) into blood flow measurements in women, particularly in examining the role of nitric oxide (NO) and calcitonin gene-related peptide (CGRP) in the microvasculature among women with and without migraine? To address the research question, WA was conducted using the complex Morlet wavelet. Relative energy density was used as a quantitative metric to compare the group of women with migraine with the group of women without migraine. Relative energy density was also calculated for the results using FA. Statistical significance was assessed using p-values, where p-values below 0.05 were considered significant. The Mann-Whitney U-Test and the Wilcoxon Signed Rank Test were used to calculate the p-values. Significant differences between women without migraine and women with migraine were primarily found in respiratory and endothelial activity for both WA and FA. Women with migraine showed higher respiratory activity in regions where NO was inhibited, for both WA and FA. Although WA and FA revealed many similar results in the VASCULAR-study dataset, there were also some differences. These differences were mainly observed in endothelial activity, in the ROI where NO was inhibited. FA revealed significantly higher values in activity for women without migraine in both NO-independent and NO-dependent endothelial activity. Furthermore, using the time-frequency localization capability of the WA, it showed significantly higher activity in women with migraine between the peak and plateau phase.

In this thesis report, we investigate the difference in using physics-compatible elements and standard elements in the context of fluid flow through porous media. For this, we compare the H(div)-conforming Raviart-Thomas elements to Lagrange elements. Our model involves a channel flow over a porous media, resulting in a fluid and a porous subdomain. We distinguish between three approaches, as based on a research paper. The first is the one-domain approach called PE, which sets one equation on the entire domain. The other two approaches are two-domain approaches, which use different equations in the two subdomains, with interface conditions to couple them. The first of these is NSD, which couples Navier-Stokes with the Darcy equation. The second is NSF, which does the same but also incorporates the Forchheimer term, which models inertial effects.

In general, we were not able to make a completely successful model, as the PE case did not match the paper, and the results found when using Raviart-Thomas elements had some unexplained anomalies. We were also unable to carry out an accurate error analysis. We were able to see that Raviart-Thomas produced an exactly divergence-free solution, as expected from the theory, at the cost of more computing power and time needed.

Energy markets are characterized by unique features such as seasonality, mean reversion, and sudden price spikes, which make the valuation of related derivatives considerably more challenging than in traditional financial markets. This thesis investigates the use of the COS method, a Fourier-based numerical technique, for the efficient valuation of energy derivatives within a Markov-modulated framework.

The study begins with energy quanto options, whose payoff depends on two correlated underlyings incorporating jumps and regime-switching dynamics. By deriving the characteristic functions for two benchmark models and implementing the COS method, the results demonstrate high accuracy and significant computational speed-ups compared to FFT- and Monte Carlo-based benchmarks.

The research work then extends to electricity storage contracts, which feature early-exercise opportunities and operational constraints. In line with the methodology presented by [1], this section replicates their integration of the COS method into a dynamic programming framework to evaluate the option values across multiple contract types and volatility regimes. The obtained prices fall within the confidence intervals of the LSMC benchmark, confirming the robustness of the COS approach in handling path-dependent and constrained problems.

Finally, the pricing framework is generalized to electricity storage under a two-state Markov-modulated model, to better capture the behavior of electricity prices. The extended COS-based algorithm accurately reproduces complex probability densities and achieves stable convergence of the prices while maintaining high computational efficiency even under this more complex setting.

In conclusion, this thesis demonstrates that the COS method is a powerful and computationally efficient alternative to stochastic simulation methods for pricing energy derivatives. It is capable of handling jump dynamics, regime switching, early-exercise features and operational constraints, highlighting its potential for broader applications in energy finance.

[1] Boris C Boonstra and Cornelis W Oosterlee. “Valuation of electricity storage contracts using the COS method”. In: Applied Mathematics and Computation 410 (2021), p. 126416

This thesis investigates the use of path signatures as feature representations for derivative pricing within a regression-based framework. Motivated by the universal approximation theorem for signatures, which ensures that any continuous path functional can be approximated by a linear functional of its signature, the study evaluates the practical performance of this approach for a range of path-dependent derivatives. The analysis focuses on two research questions: how does signature-based regression compare with standard Monte Carlo simulation in terms of accuracy and stability, and whether more compact signature representations can alleviate the exponential growth in the signature dimension with respect to the number of assets. To address these questions, the work introduces an alternative representation of the signature features, the filtered signature, which uses the signatures of individual time-augmented asset paths rather than the full joint multi-asset signature, with the aim of reducing dimensionality while retaining essential path information. The effectiveness of this representation, along with the overall performance of the regression framework, is then evaluated through a series of numerical experiments. For the financial products analyzed, numerical results indicate that, when combined with analytically derived expected signatures, the price estimator obtained through signature-based regression exhibits markedly lower variance than standard Monte Carlo. As a result, the required number of simulated paths to achieve the same level of accuracy is reduced. The results further show that the filtered signature representation attains pricing accuracy and stability comparable to the full signature, while relying on significantly fewer terms. These findings suggest that essential and enough information about the path is encoded within the individual signatures of the time-augmented underlying assets, supporting the adoption of compact signature representations for multi-asset derivative pricing.

The numerical solution of the Helmholtz equation presents significant challenges in computational mathematics and scientific computing, particularly for high-frequency problems in heterogeneous media. This dissertation addresses these challenges through the development of high-performance iterative methods, focusing on the critical balance between numerical efficiency and practical implementation on modern computing architectures.

The research is motivated by the growing computational demands in seismic imaging and other wave propagation applications, where increasing frequencies and larger domains necessitate more efficient solution strategies. Traditional approaches often struggle with the combined challenges of wavenumber-dependent convergence, pollution errors, and substantial memory requirements, particularly for three-dimensional problems in heterogeneous media.

This work presents a comprehensive framework for solving large-scale Helmholtz problems through matrix-free parallel implementations of preconditioned iterative methods. The framework combines Complex Shifted Laplace Preconditioner (CSLP) with advanced deflation techniques, implemented in a manner that eliminates the need for explicit matrix storage while maintaining computational efficiency. A key innovation is the development of matrix-free implementations for higher-order deflation methods combined with the CSLP preconditioner, achieved through carefully designed re-discretization schemes that preserve the advantages of Galerkin coarsening.

The methodology progresses from two-dimensional implementations to fully three-dimensional frameworks, incorporating increasingly sophisticated preconditioning techniques. A significant achievement is the development of a matrix-free parallel multilevel deflation preconditioner that exhibits near wavenumber-independent convergence while maintaining excellent parallel scalability. The implementation utilizes a hybrid MPI+OpenMP parallelization strategy, effectively addressing both computational and memory challenges in extreme-scale scenarios.

Extensive numerical experiments validate the effectiveness of these methods across a range of problem types, from academic test cases to industrial-scale applications. Notably, the framework successfully resolves a challenging seismic model, involving approximately 3.8 billion degrees of freedom, while achieving 86\% parallel efficiency when scaling to 2304 CPU cores. This demonstration of practical viability for large-scale heterogeneous problems represents a significant advance in computational capabilities for seismic imaging applications.

The research makes several fundamental contributions to the field of numerical analysis and scientific computing. First, it establishes new approaches for matrix-free implementation of state-of-the-art preconditioners, significantly reducing memory requirements while maintaining numerical efficiency. Second, it demonstrates the achievement of close-to wavenumber-independent convergence through carefully designed deflation strategies in a parallel computing environment. Third, it provides a comprehensive framework for solving extreme-scale Helmholtz problems that combines numerical robustness with practical applicability.

The methodologies developed in this work contribute to the broader field of scientific computing, demonstrating how careful algorithm design, combined with modern computing architectures, can address previously intractable problems in wave propagation modeling.

Potato plant vitality is an important trait for good yield. Unfortunately, even with the best potato seed, vitality variation within a genotype is significant and it hurts both farmers and potato seed producers. HZPC and Averis Seeds hypothesize the existence of a link between the variation in the vitality of a plant and the field of production of the seed tuber. TU Delft, Utrecht University, HZPC and Averis Seeds joined forces to determine the existence and strength of this link in a multi-year
project funding this research. In this thesis we describe TU Delft’s contribution both in the measurement of plant vitality and the development of a model to predict plant vigor from chemical properties of the tuber...

This master's thesis provides information on the subject of option pricing theory. Moreover, this topic is linked with sustainable Finance, which is essential in the battle against climate change. Green financing is a growing phenomenon, and a green bond is only a relatively new example of a green financial derivative. A three-dimensional PDE is at hand to determine the price of a green bond (also called a coupon value), but this PDE can only be solved numerically. This master thesis aims to determine the stability of certain numerical schemes that can be used to find a solution to the PDE. We will use both forward and backward differences to derive the numerical schemes. After the derivation process, a Von Neumann analysis is performed to conclude whether or not the schemes are stable. The research reveals that most of the numerical schemes are not stable. Amongst others, this is caused by the positive value of the interest rate $r$, the value of the carbon price, and the absence of damping factors. In some numerical schemes, the amplification factor is larger than one, but not by much. In other schemes, we can reduce the amplification factor to increase the stability. This means that the numerical schemes can still be used to find a reasonable value for a green bond.

Understanding multiphase flows is critical in nuclear engineering, particularly for processes such as coolant dynamics in nuclear reactors and safety scenario analyses involving different fluid phases. Numerical simulations are a valuable tool for studying these phenomena, especially when experimental approaches are impractical due to cost or safety concerns. While direct numerical simulations (DNS) offer detailed insights, their computational expense makes them impractical for turbulent flows, necessitating the use of turbulence models for efficiency.

This thesis introduces a novel machine learning framework designed to improve Reynolds-averaged Navier-Stokes models in turbulent stratified gas-liquid flows while employing the Boussinesq approximation. The framework encompasses two methods for turbulent viscosity field inversion and introduces correction terms in the turbulence model equations to ensure an accurate prediction of the turbulent viscosity field. Through sparse symbolic regression, the framework consistently discovers models that improve the accuracy of the baseline RANS model, even in untrained flow scenarios, though further testing is needed for varied flow regimes.

Key findings include the superior performance of sparse symbolic regression models over neural network (NN) models in improving the baseline RANS model accuracy. Notably, LASSO and elastic net techniques yielded the most successful models, significantly reducing baseline errors. However, these models did not surpass the Egorov damping approach in terms of accuracy, indicating the need for further refinement.

The developed models were numerically stable and robust, which is important for practical use. However, a main limitation is that the models' accuracy during training did not always correlate with the results when coupled with the RANS equations. Moreover, data from more varied flow conditions is needed to properly assess the generalizability of the models.

Overall, this research highlights the potential of data-driven turbulence modelling to enhance two-phase flow simulations, marking a significant step forward while also identifying areas for future improvement and exploration.

This report focuses on the process of aberration coefficient retrieval for measurements of a
Scanning Electron Microscope (SEM). The main goal is the verification of a modified aberration coefficient retrieval method described in an article by Uno et al. (2005) [1]. This method
first extracts spot profiles, represented by Point Spread Functions (PSFs), from a through-focus
series of SEM images using Fourier analysis. The size and shape of a PSF characterize the
performance of a SEM. Afterward, it determines characteristics from these PSFs to obtain
aberration coefficients.
The process consisted of three parts: probe shape extraction, PSF analysis, and aberration coefficient calculation, which included a consistency test. The original method was changed to
include clarification for the use of complex conjugates of functions in the Fourier domain, the
proper statistical definition of standard deviation as characteristic of the width of a PSF, the
curvature definition used in the method, and contour tracing around a PSF to exclude points
outside the contour. These adaptations were made to clarify the methods described in the reference article.
This modified method was tested against generated PSFs that used the wave-optical description
of five different aberrations. By increasing the value of these individual simulated aberration
coefficients C
′
n with factors s ∈ [1, S], S ∈ N, the consistency of this retrieval method could be
determined by calculating the ratio Q between simulated and retrieved aberration coefficients
as a function of the factor s.
The spot shape extraction method was implemented and assessed successfully, as the shapes of
the generated PSFs were successfully retrieved, and the use of complex conjugates of functions
in the Fourier domain resulted in rotated spot shapes.
Contour tracing around the retrieved PSFs had mixed results. For all aberrations, it presented
a good measure of the spot shape. The implementation of an algorithm that excluded points
pj outside the contour was not accurate for one test aberration: two-fold astigmatism A2. The
contour tracing algorithm excludes important parts of the shapes of the PSFs. From research
into the characteristics asymmetry µ, width σ, and curvature ρ it could be inferred that the first
two characteristics µ and σ represent the mean and standard deviation of discrete probability
mass functions. Curvature ρ has no such direct statistical equivalent, it was concluded however
that this characteristic presented a good measure for the effect of third-order spherical aberration in a PSF.
Results could not indicate that this wave-optical description of aberrations is consistent with
all of the coefficients retrieved using the modified method of aberration coefficient retrieval
as the ratio Q remained consistent for defocus Cdf , three-fold astigmatism A2 and third-order
spherical aberration Cs as a function of the factors s.
Continued research into this method of aberration retrieval is recommended, specifically in the
definitions of the characteristic curvature ρ, more advanced contour tracing, and the digitizations of aberration coefficients. More assessment and modification of this method of aberration
coefficient retrieval can allow for a more comprehensive understanding of the method, which
will eventually contribute to an automatic aberration corrector that could speed up SEM measurements significantly.

Achtergrond: Migraine is een veel voorkomende hoofdpijnaandoening waar neuropeptiden een belangrijke rol lijken te spelen. Dit onderzoek richt zich op het begrijpen van de invloed van zenuwen en gladde spiercellen op de bloeddoorstroming bij vrouwen met en zonder migraine. Hiervoor worden LSCI en LDPI metingen getransformeerd met een Fourier transformatie. Doel: Het doel van dit onderzoek is om verschillen te identificeren in de invloed van zenuwen en gladde spiercellen op de bloeddoorstroming tussen vrouwen met en zonder migraine. Methode: LSCI metingen van de VASCULAR studie worden gebruikt. Fourier analyse wordt toegepast om de invloed van zenuwen en gladde spiercellen te vinden in verschillende fasen: baseline, piek en plateau. Vergelijkingen worden gemaakt tussen vrouwen met migraine (met en zonder aura) en controlepersonen zonder migraine. Resultaten: In de baseline fase zijn minimale verschillen in de invloed van de zenuwen en de gladde spiercellen gevonden tussen vrouwen met en zonder migraine. Tijdens de piek fase lijkt er een verhoogde invloed van de zenuwen en de gladde spiercellen op de bloeddoorstroming bij vrouwen zonder migraine. Conclusie: In ons onderzoek zijn geen evidente verschillen gevonden. Wel lijken er enkele trends in de data te zitten. Verdere studies zijn nodig om deze bevindingen te bevestigen en uit te breiden.

District heating leverages centralised, high efficiency combined heat and power (CHP) systems. It uses waste heat to lower energy consumption and reduce greenhouse emissions. The system also supports renewable energy sources like geothermal and biomass, providing a sustainable heating alternative.

This report examines a nonlinear network of pressure and flow challenges. It focuses on enhancing the Newton-Raphson method and refining direct solving techniques for a single time-step. As net- works grow in complexity, efficient and effective solutions become crucial. Various strategies to speed up the Newton-Raphson algorithm in Gradyent’s heat network solver are discussed, where derivative calculations are straightforward. Both direct and iterative methods to improve the algorithm’s steps are explored. The effectiveness of these enhancements is tested and evaluated across networks of different sizes.sc

Implied volatility is critical in financial markets, especially for option pricing. Traditional methods for its calculation sometimes are not well suited to some scenarios. Recent developments in neural networks have provided more efficient alternatives.

Leveraging advances in quantum computing, our research introduces quantum neural networks for computing implied volatility, assessing the feasibility and characteristics of this novel approach. We focus on two quantum neural network architectures: Dissipative Quantum Neural Networks (DQNN) and Parameterized Variational Quantum Circuits (PVQNN). DQNN, similar to classical neural networks in structure and training ease, faces challenges with quantum state outputs and data decoding, impacting performance negatively. Besides, limited by the reliance on network output states at each layer, DQNN faces challenges in implementation with the current state of quantum hardware.

In contrast, PVQNN offers a more promising solution. Compared to DQNN, PVQNN requires fewer qubits, can apply traditional optimizers to train the model, and can run on NISQ devices. This research thoroughly examines various aspects influencing PVQNN's performance, including training data characteristics, data re-uploading technology, network size, data encoding methods, and quantum circuit design. The selected PVQNN model can achieve high accuracy in implied volatility computation with $R^2$ of approximately 0.999. In addition, we find that the PVQNN can obtain satisfactory results even with limited training data, setting it apart from traditional neural networks.

This thesis not only adopts a new model to compute implied volatility but also deepens the understanding of quantum neural networks in financial modeling. However, due to resource constraints, our experiments are conducted in simulations on traditional computers, and thus our study focuses mainly on the expressive power of QNNs rather than their operational efficiency.

This thesis presents a novel approach to the pricing of green bonds, a growing segment in financial markets with an emphasis on environmental sustainability. Unlike traditional financial instruments, green bonds uniquely incorporate environmental considerations, particularly carbon price (c_t), along with traditional factors like the short rate (r_t), into their valuation. This integration is increasingly relevant in today’s economy, reflecting a shift towards sustainable finance. The core of this research involves applying advanced numerical methods, including the Finite Difference Method, Crank-Nicolson discretization, GMRES and Bi-CGSTAB, in order to develop and analyze pricing models for both green and conventional bonds. The study aims to assess how environmental factors impact the efficiency of these numerical techniques and to compare the outcomes with conventional bond models. The research reveals that green bonds, compared to conventional bonds, present unique numerical challenges, notably requiring more iterations for convergence in iterative methods GMRES and Bi-CGSTAB because of the high carbon price volatility (σc) and the ’Greenium’ phenomenon. Moreover, the comparative analysis showed that while Bi-CGSTAB outperforms GMRES in the green bond model, the opposite is true for conventional bonds. This study not only contributes to the theoretical understanding of green bond pricing but also offers practical insights for financial analysts and investors navigating this evolving market.

Keywords: Green Bonds, Bond Pricing, Zero-Coupon Bond, Short Rate Modeling, Numerical Methods, Crank-Nicolson, GMRES, BiCGSTAB, Environmental Finance, Sustainable Investing, Comparative Analysis, Financial Modeling.

Implied volatility surfaces (IVS) are essential for option pricing and risk management. Recently, generative deep learning models, such as the Denoising Diffusion Probabilistic Model (DDPM), have gained popularity for generating IVS. However, these machine-generated surfaces do not necessarily meet arbitrage-free constraints, e.g., calendar spread and butterfly arbitrage.
This thesis addresses this limitation by integrating arbitrage-free constraints into the DDPM generation process. We introduce a classifier specifically designed to detect violations of arbitrage-free conditions. During the DDPM’s iterative generation process, the classifier guides the formation of the IVS at each step, effectively minimizing potential arbitrage opportunities. To evaluate the effectiveness of our approach, we compare the extent of arbitrage violations in IVS generated by the standard DDPM with those produced by our enhanced DDPM-Classifier model. The results demonstrate that the DDPM-Classifier significantly reduces arbitrage opportunities and improves the quality of IVS.

Counterparty Credit Risk (CCR) refers to the risk that a counterpary involved in a financial contract will default before the final settlement of the contract, resulting in unrealized financial gains. One risk measure for managing counterparty credit risk is the Potential Future Exposure (PFE). The PFE is defined as the 97.5%-quantile of the exposure distribution. The traditional numerical method for computing the PFE is the Monte Carlo simulation method. Recent research has produced a semi-analytical method based on Fourier-cosine series expansion which produce PFE estimates with at least five times the accuracy of the Monte Carlo simulation in one-tenth of the CPU time. In this thesis this COS-PFE framework is combined with Monte Carlo simulation with the goal of reducing the variance of the PFE estimates.
In this thesis three methods are developed. Firstly, the Control Variate method that uses the COS-PFE framework to retrieve the CDF of the portfolio's exposure from which control variates are sampled. The second method developed uses Adaptive Importance Sampling. This method uses the COS-PFE framework to estimate the PFE and expected exposure (EE) of the portfolio. Using the risk factor that has the highest correlation with the exposure of the portfolio a shift is found such that the new joint probability distribution, from which the risk factors are sampled, has an EE that coincides with the portfolio's PFE. The third method developed in this thesis uses Adaptive Importance Sampling based on the Cross-Entropy method. This method aims to find an auxiliary probability distribution which minimizes the Kullbeck-Leibler divergence between itself and the theoretical zero-variance estimator.
The methods were tested using portfolios containing 100, 1000 and 10000 derivatives, both with and without collaterals. Testing showed that the control variate method was not able to produce a variance reduction compared to the straight forward Monte Carlo simulation. However, it did demonstrate the ability to produce a variance reduction for the EE.
The method that finds an optimal shift was successful in producing a variance reduction. The variance of the PFE estimates produced by this method was, on average, 3.5 times lower than the variance found using the straight forward Monte Carlo simulation. However, the algorithm required between 96 and 1773 seconds to produce a PFE estimate.
The adaptive importance sampling method using the cross-entropy approach was tested on all portfolio, both with and without collateral. This method was shown to produce PFE estimates with a significantly lower variance than the straight forward Monte Carlo simulation. For the uncollateralized portfolios containing 100, 1000 and 10000 derivatives the variance of the PFE estimates were on average 35.4, 38.6 and 37.2 times smaller compared to straight forward Monte Carlo simulation. For the portfolios with collateral this performance remains. We conclude that this method produces more accurate PFE estimations with significant lower variance than straight forward Monte Carlo simulation within the same CPU time.

We price continuously monitored barrier options under GBM and SABR through a newly developed neural network, the COS-CPD network, based on the COS-CPD method. With the pricing PDE and the COS method, we transform the problem of pricing the barrier options into a problem of finding the survival characteristic function through an initial boundary value problem. By applying trigonometric expansion, derived by integrating out the Fourier expansion on the time derivative of the unknown function, to the survival characteristic function, we find an approximation that can be inserted into the IBVP. Without CPD, this results in a linear system which can be solved to find the expansion coefficients, but this leads to the curse of dimensionality. If we include CPD to remove this curse of dimensionality, resulting in the COS-CPD network, the Alternating Least Squares method must be used to find the factor matrices that replace the original expansion coefficient tensor.