Taming double-spending in offline payments

Blockchain-based payment systems typically assume a synchronous communication network and a limited workload to confirm transactions within a bounded timeframe. These assumptions make such systems less effective in scenarios where reliable network access is not guaranteed.<br/>Offline payment systems are designed to allow users to register...

On Non-Stationarity in Reinforced Deep Markov Models with Applications in Portfolio Optimization

In this thesis, we aim to improve the application of deep reinforcement learning in portfo- lio optimization. Reinforcement learning has in recent years been applied to a wide range of problems, from games to control systems in the physical world and also to finance. While reinforcement learning has shown success in simulated environments (e.g....

Estimating Option Implied Probability Distributions for Inflation

This thesis investigates the estimation of option-implied probability density functions for inflation using inflation options, focusing not only on the expected value but the whole distribution. The aim is to identify the most effective method for measuring the market expectation of future inflation. The research explores both parametric and non...

Electrical energy storage scheduling: Short-term scheduling for the intraday market using stochastic programming

The global push for renewable energy faces challenges due to the unpredictable and inconsistent nature of wind and solar sources. These inherent characteristics of renewable energy sources add volatility to the electricity markets. In response, electrical energy storage (EES) emerges as a solution for maintaining grid flexibility, stability, and...

A novel cosine network for pricing European and barrier options under stochastic volatility models

This thesis presents a comprehensive exploration of the rough Heston model as a means to enhance financial derivative pricing and calibration in the context of the complex behavior of market volatility. Recognizing the limitations of classical models, such as the Black-Scholes and the standard Heston model, which assume constant or mean...

Solving the partial differential equation for pricing barrier options via trigonometric expansions

Barrier options, although highly liquid financial derivatives, present notable pricing challenges. In this thesis, we present a novel pricing approach for valuing continuously-monitored knock-out barrier options within the framework of stochastic volatility models.<br/><br/>The underlying process is firstly modelled under geometric Brownian...

Multi Target XGBoost Cash Flow Prediction: An Efficient Machine Learning Algorithm For Future Liability Projections

Insurers are required to have buffers to be able to meet financial obligations that result from their portfolios, which are determined using a cash flow model. The input of such a cash flow model consists among of things, of two mortality tables and the portfolio of an insurer. Mortality rates are simulated using the Lee-Carter model. These...

Solving multivariate expectations using dimension-reduced fourier-cosine series expansion and its application in finance

The computation of multivariate expectations is a common task in various fields related to probability theory. This thesis aims to develop a generic and efficient solver for multivariate expectation problems, with a focus on its application in the field of quantitative finance, specifically for the quantification of Counterparty Credit Risk (CCR...

The Characteristic Function of the Time-Integral of Geometric Brownian Motion and its Application in Asian Option Pricing

In this research a new method for pricing continuous Arithmetic averaged Asian options is proposed. The computation is based on Fourier-cosine expansion, namely the COS method. Therefore, we derive the characteristic function of Integrated Geometric Brownian Motion based on Bougerol's identity. <br/><br/>Extensive numerical error analysis on the...

An integrated benchmark model for Counterparty Credit Risk

The EAD metric is widely used in the calculations for the capital requirements concerning Counterparty Credit Risk (CCR). In this thesis we compare several methods for calculating this EAD. Basel III gives us two methods, the Standardized Approach for CCR (SA-CCR) and the Internal Model Method (IMM). Furthermore, we introduce an integrated...

Pricing multi-dimensional American options using kernel ridge regression

American option pricing has been an active research area in financial engineering over the past few decades. Since no analytic closed-form solution exists, various numerical approaches have been developed. Among all proposed methods, the least square Monte Carlo(LSMC) approach is the most successful and popular. The LSMC utilizes linear...

Spectral Calibration of Time-inhomogeneous Exponential Lévy Models: with Asymptotic Normality, Confidence Intervals, Simulations, and Empirical Results

The problem of calibrating time-inhomogeneous exponential Lévy models with finite jump activity based on market prices of plain vanilla options is studied. Belomestny and Reiß introduced an estimation procedure for calibration in the homogeneous case with one maturity. The open-ended question that will be addressed is if we can extend this model...

Restoration of Missing Data using a Human Adaptive Framework: The Cleansing Algorithm

Improving data quality is of the utmost importance for any data-driven company, as data quality is unmistakably tied to business analytics and processes. One method to improve upon data quality is to restore missing and wrong data entries.  </p><p class="MsoNormal">The goal of this research is construct an algorithm such...

Dimension reduction techniques for multi-dimensional numerical integrations based on Fourier-cosine series expansion

A wide range of practical problems involve computing multi-dimensional integrations. However, in most cases, it is hard to find analytical solutions to these multi-dimensional integrations. Their numerical solutions always suffer from the `curse of dimension', which means the computational complexity grows exponentially with respect to the...

Energy Study of Drying: Using Machine Learning to Predict the Energy Consumption of an Industrial Powder Drying Process

In this thesis, we use data science / statistical techniques to better understand the energy consumption behind a powder drying facility located in Zwolle, as part of Abbott's initiative to better manage its energy consumption. As powder drying is by far the facility's most energy intensive process, this project therefore focuses exclusively on...

Modelling the Libor transition: Implementing and extending the generalized forward market model

Interbank-offered-rates play a critical role in the hedging processes of banks, hedge funds or institutional investors. However, the financial stability board recommended to replace these rates by alternative risk-free-rates at the end of 2021. The new rates will be backward-looking rates and therefore, the payoff definitions of interest rate...

Investment Strategy for VIX Futures based on a Bayesian Approach to Term Structure Modelling

The VIX index, which is the expected volatility of the S&amp;P 500 index in 30 days, is of interest to a lot of investors on the US financial market. Allowing the volatility of the financial market to be used as a trading tool gives rise to interesting investment opportunities, such as hedging and speculation. In this thesis we will be creating...

Fast Calculations of Portfolio Credit Losses and Sensitivities

Computing portfolio credit losses and associated risk sensitivities is crucial for the financial industry to help guard against unexpected events. Quantitative models play an instrumental role to this end. As a direct consequence of their probabilistic nature, portfolio losses are usually simulated using Monte Carlo copula models, which in turn...