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.

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.

Implied volatility surfaces are integral to option pricing and risk management but often display missing data. Prior research has typically engaged mathematical models or data-driven methods for generating or completing these surfaces. Given the similarity between implied volatility surfaces and images, our paper introduces the Denoising Diffusion Probabilistic Model (DDPM), a novel deep learning image generation model, for this task. A distinctive aspect of DDPM's training involves progressively adding noise to the surfaces until they resemble pure noise, and then learning to denoise back to the original surfaces. We employ the Heston model to simulate implied volatility surfaces, then train the DDPM using this synthetic data. Additionally, a Variational Autoencoder model (VAE) is implemented as a comparative benchmark for assessing DDPM's efficacy. Our experiments demonstrate that DDPM excels in generating and reconstructing missing areas in implied volatility surfaces, highlighting its potential in this field. Looking to the future, combining DDPM with VAE could provide more interpretable results, enhancing the model's utility and applicability in financial analysis.

This thesis is about pricing European options using a Fourier-based numerical method called the COS method under the rough Heston model. Besides examining the efficiency and accuracy of the COS method for pricing options under the rough Heston model, it is also investigated if the rough Heston model produces the advantages of the so-called rough volatility models. To do so, the characteristic function of the rough Heston model is derived, and the COS method for the rough Heston model and also a Monte Carlo simulation scheme is introduced. Throughout the thesis, the theoretical background of the rough Heston model, the numerical techniques and some numerical experiments on European option prices and implied volatility behaviors are presented. Also, a calibration of the rough Heston model is performed using Artificial Neural Networks. As a result of this thesis, pricing of European options using COS method is succeeded. Moreover, it is shown that the rough Heston model produces the rough volatility behaviors as expected.