A. Papapantoleon | TU Delft Repository

Importance Sampling and Quantile Estimation for Concentration Credit Risk

Efficient algorithms for assessing concentration in credit portfolios

Master thesis (2025) - Q.T. van Hattem (author) , LE Meester (mentor) , A. Papapantoleon (graduation committee member) , Joris Bierkens (graduation committee member) , R. Vedder (mentor)

This thesis investigates efficient Monte Carlo methods for estimating the 99.9% Value-at-Risk of
concentrated credit portfolios modelled through a normal copula framework. Crude Monte Carlo
simulation is inefficient when estimating extreme loss levels. To address this ine ...

Risk-sensitive Reinforcement Learning for Portfolio Allocation

Master thesis (2024) - A.A. Sinha (author) , FA Oliehoek (mentor) , Luciano Cavalcante Siebert (graduation committee member) , A. Papapantoleon (graduation committee member) , M.M. Celikok (graduation committee member) , Rob Huisman (graduation committee member)

This study explores the application of risk-sensitive Reinforcement Learning (RL) in portfolio optimization, aiming to integrate asset pricing and portfolio construction into a unified, end-to-end RL framework. While RL has shown promise in various domains, its traditional risk-n ...

Portfolio optimization under the rough Bergomi model

Master thesis (2024) - G. Bifronte (author) , F. Yu (mentor) , A. Papapantoleon (graduation committee member) , E. Lorist (graduation committee member)

This thesis addresses the portfolio allocation problem within a financial market featuring one riskless asset and a risky asset exhibiting rough Bergomi volatility. The objective is to maximize the expected utility of terminal wealth with respect to power utility. The volati ...

Taming double-spending in offline payments

Master thesis (2024) - N. Evangelou (author) , Johan Pouwelse (mentor) , J.E.A.P. Decouchant (mentor) , Rowdy Chotkan (mentor) , Antonis Papapantoleon (graduation committee member)

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.
Of ...

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

Master thesis (2024) - L.H.L. Chin-A-Pauw (author) , F. Yu (mentor) , A. Papapantoleon (graduation committee member) , Alexis Derumigny (graduation committee member)

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 reinfo ...

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. matching or exceeding human performance in games), its adoption in practical applications (non- simulated environments) has lagged. Dulac-Arnold et al. [2019] suggest this is caused by a discrepancy in the experimental set-up in research and the conditions in practice. Specifically, they present a list of challenges that make the application of reinforcement learning in real-world settings more difficult. One of these challenges is non-stationary environments, which is common in financial environments. It is a challenge since, given an observed state, the optimal action may not always be the same as it may change over time due to non-stationarity. Therefore, more specifically, the goal of this thesis is to overcome the challenge of non-stationarity in the application of reinforcement learning to portfolio optimization. In this thesis, we use reinforced deep Markov models (RDMM) introduced by Ferreira [2020] (applied to an optimal execution problem and later used by Cartea et al. [2021] for statistical arbitrage on simulated price movements of an FX triplet) for its data efficiency and ability to handle complex environments. RDMM involve a partially observ- able Markov decision process (POMDP) which is also the setting used by Xie et al. [2021] to model non-stationarity in reinforcement learning. We extend RDMM to incorporate non-stationarity, using the framework suggested by Xie et al. [2021], and apply it to port- folio optimization. Our implementation is sample efficient which allows for quick learning, by doing this we attempt to improve on another challenge of reinforcement learning — i.e. sample-inefficiency [Dulac-Arnold et al., 2019]. Moreover, our implementation can handle continuous state and action spaces.
We compare the performance of our algorithms to classical portfolio optimization tech- niques such as Mean-Variance (MV) and Equal Risk Contribution (ERC), and to popular reinforcement learning techniques such as Deep Deterministic Policy Gradient (DDPG) and Soft Actor-Critic (SAC). We observe our implementation has higher sample-efficiency compared DDPG and SAC, and higher cumulative returns on the test set compared to MV, ERC, DDPG, and SAC.

Towards Arbitrage-free Implied Volatility Surfaces with Diffusion Probabilistic Models

Master thesis (2024) - Y. Hu (author) , S. Liu (mentor) , C. Vuik (mentor) , Antonis Papapantoleon (graduation committee member)

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 n ...

Multi Target XGBoost Cash Flow Prediction

An Efficient Machine Learning Algorithm For Future Liability Projections

Master thesis (2023) - S.J.G. van Schagen (author) , L.E. Meester (mentor) , M. Visser (mentor) , Antonis Papapantoleon (graduation committee member) , Joris Bierkens (graduation committee member)

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. M ...

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

Master thesis (2023) - M. Brands (author) , F. Fang (mentor) , C. Vuik (graduation committee member) , A. Papapantoleon (graduation committee member) , Xiaoyu Shen (graduation committee member)

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, s ...

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 proposed COS-CPD method utilizes the COS method to recover the exposure distribution by its Fourier-cosine series expansion, from which measures such as the PFE and EE can be obtained. The key insight is that the corresponding Fourier coefficients are readily available from the characteristic function, which can be solved using numerical integration methods. However, the efficiency of standard quadrature rules is limited to only a few risk factors, as the dimension of integration is determined by the number of risk factors involved.

To address this limitation, the COS-CPD method reduces the dimension of integration of the characteristic function through two steps. Firstly, the joint density function of the risk factors in the characteristic function is replaced by a dimension-reduced Fourier-cosine series expansion, which is obtained through CPD. With CPD, the computational complexity of computing the Fourier coefficient tensor is reduced to a linear growth with respect to the number of dimensions. Secondly, the portfolio is divided into segments that share the same risk factors. These two steps reduces the evaluation of the characteristic function to the calculation of only one- and two-dimensional integrals, which are solved by the Clenshaw-Curtis quadrature rule. As a result, the COS-CPD method is suitable for portfolios with more than three risk factors.

Numerical comparisons of the COS-CPD method and Monte Carlo (MC) method are made for netting-set PFE and EE profiles of multiple derivative portfolios up to five risk factors. For similar accuracy levels, the COS-CPD method greatly outperforms the Monte Carlo method in computation time. This difference increases for larger portfolios, which makes the COS-CPD method a much more efficient alternative for the MC method, especially for large portfolios.

Furthermore, the COS-CPD method is applied in the context of multi-asset option pricing. A six-dimensional basket option is considered, and the results are compared to a recently developed sparse grid method. The comparison shows that the COS-CPD method outperforms the sparse grid method in both accuracy and computation time. Moreover, the COS-CPD method allows the computation of the option value for multiple strike prices simultaneously, with no significant additional computational cost.

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

Master thesis (2023) - T. Wever (author) , F. Fang (graduation committee member) , K. Vuik (mentor) , A. Papapantoleon (graduation committee member)

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 Bouge ...

Estimating Option Implied Probability Distributions for Inflation

Master thesis (2023) - T.J.M. Schuttenbeld (author) , L.E. Meester (mentor) , A. Papapantoleon (graduation committee member) , F. Fang (graduation committee member)

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 ...

Electrical energy storage scheduling

Short-term scheduling for the intraday market using stochastic programming

Master thesis (2023) - A.A.C. Krijgsman (author) , Theresia van Essen (mentor) , A. Papapantoleon (graduation committee member) , Thomas Van der Vliet (coach)

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) eme ...

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

Master thesis (2023) - S.M. Marques da Rocha Feliciano Pereira (author) , F. Fang (mentor) , C. Vuik (graduation committee member) , A. Papapantoleon (graduation committee member) , Xiaoyu Shen (mentor)

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.

The u ...

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.

The underlying process is firstly modelled under geometric Brownian motion and, subsequently, under Heston's stochastic volatility model. A key insight is that the value of a barrier option can be expressed as a single-dimensional integral, whereby the integrand involves the so-called survival density function, which captures the barrier-brea-ching information. Therefore, the option can be valued using the one-dimensional COS method for European options, once the Fourier series coefficients of the survival density are obtained.

The coefficients of the sine series expansion of the survival density function are, in fact, a continuous function, which is closely related to the characteristic function of the density. This motivates us to directly recover that function, which we refer to as the target function herewith, by selecting an appropriate series expansion for it. Thereafter, we insert this series expansion into the partial differential equation (PDE) that the target function should satisfy, which can be derived from the pricing PDE. This results in a linear system, solving which we obtain the coefficients needed to reconstruct the target function. Notably, this approach is particularly advantageous when the reference values of option prices are limited or unavailable, as it relies solely on the PDE to calibrate the series coefficients.

Our choice of series expansion is driven by the need for precise global and local approximations. Our research shows that a proper expansion for the target function can be built up from integrating a two-dimensional Fourier series of its first derivative with respect to time, marking our second pivotal insight. That results in a trigonometric expansion that significantly enhances the accuracy compared to a direct Fourier series expansion on the target function. Finally, our third pivotal insight: applying a change of variables further improves error convergence.

Extensive testing results suggest that, for similar accuracy levels, our approach greatly outperforms Monte Carlo simulations in terms of computation time. It also demonstrates superior computational efficiency and accuracy compared to other advanced numerical methods in the existing literature.
The benefits of this method become even more prominent when pricing a large number of options simultaneously.

Numerical tests reveal algebraic convergence for the series expansion reconstruction. For the option price, theoretical error analysis aligns with our findings, also predicting algebraic convergence.

An integrated benchmark model for Counterparty Credit Risk

Master thesis (2023) - T.P.S. Heijnders (author) , C. Vuik (mentor) , A. Papapantoleon (graduation committee member) , F. Fang (coach) , Xiaoyu Shen (coach)

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 Interna ...

Pricing multi-dimensional American options using kernel ridge regression

Master thesis (2023) - Q. Chen (author) , N. Parolya (mentor) , Antonis Papapantoleon (graduation committee member)

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 ...

Energy Study of Drying

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

Master thesis (2022) - M. El Ouasgiri (author) , P. Chen (mentor) , A. Papapantoleon (graduation committee member) , Robin Verhoek (coach)

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 energ ...

Restoration of Missing Data using a Human Adaptive Framework

The Cleansing Algorithm

Master thesis (2022) - S.S. Dijkstra (author) , P. van Buuren (mentor) , Piao Chen (mentor) , Geurt Jongbloed (graduation committee member) , A. Papapantoleon (graduation committee member)

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.

The goal of this research is construct an algorithm such that it is possible to restore missing and wrong data entries, while making use of a human adaptive framework. This algorithm has been constructed in a modular fashion and consists of three main modules: Data Transformation, Data Structure Analysis and Model Selection. Data Transformation has concerned itself with conversion of raw data to data types and forms the other modules can use.

Data Structure Analysis has been designed to deal with correctly missing data and dichotomy in the target feature by making use of three clustering algorithms: DBSCAN, K-Means and Diffusion Maps. DBSCAN is used to determine the necessity of clustering as well as the initialisation of the K-Means algorithm. K-Means and Diffusion Maps have been used as clustering methods in the one-dimensional target feature and the two-dimensional input-target feature pairs, respectively. Data Structure Analysis has further been designed to perform feature selection through three filter methods: CorrCoef, FCBF and Treelet.

Model Selection has proposed a novel approach to selection of the best model of a candidate set through the optimisation of a conditional model ranking strategy based on the prior construction of theoretical testing. Our candidate set consisted of Expectation Maximisation, K-Means, Multi-Layer Perceptron, Nearest Neighbor, Random Forest, Linear Regression, Polynomial Regression, ElasticNet Regression.

In terms of restorability, it was shown that the optimal configuration of the Cleansing Algorithm for the restoration of missing data, was provided by opting not to use clustering, using a custom alteration to the Treelet algorithm for feature selection and making use of the model selection strategy. This not only lead to the greatest restorability of 56.90% on Aegon data sets, which was an improvement of 44.83% when compared to not using the Cleansing Algorithm, but also to the reduction of computation time by over 400%. A more realistic restorability due to the presence of correctly missing data, was given by the same configuration making use of one-dimensional output clustering. This resulted in a restorability on Aegon data sets of 43.10%. As such it was deemed possible to restore missing data on Aegon data sets.

With respect to the human adaptive framework, it was determined that the construction of the algorithm be modular in the sense that any alternate feature selection or clustering approach can be implemented with ease. Furthermore, the model selection module allows us to customize the theoretical testing and choice of regression or classification models for the restoration of missing data. In doing so, the algorithm has laid the foundations for human adaptivity of the Cleansing Algorithm.

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

Master thesis (2022) - Z. CHENG (author) , F. Fang (mentor) , C. Vuik (graduation committee member) , A. Papapantoleon (graduation committee member) , Xiaoyu Shen (graduation committee member)

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 com ...

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

Master thesis (2021) - T.C.A.M. Hack (author) , C. W. Oosterlee (mentor) , F. Fang (graduation committee member) , Antonis Papapantoleon (graduation committee member) , Natalia Larina-Borovykh (graduation committee member) , George Inkoom (graduation committee member)

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-look ...

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

Master thesis (2021) - O. Oostdam (author) , Joris Bierkens (mentor) , Kees de Graaf (mentor) , A. Papapantoleon (graduation committee member)

The VIX index, which is the expected volatility of the S&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, s ...

Fast Calculations of Portfolio Credit Losses and Sensitivities

Master thesis (2021) - A. Nayak (author) , F. Fang (mentor) , Xiaoyu Shen (mentor) , C Vuik (mentor) , Drona Kandhai (mentor) , Antonis Papapantoleon (graduation committee member)

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 ar ...