Introduction: This thesis presents a python implementation and evaluation of the Heston-Queue-Hawkes (HQH) model, a recent jump-diffusion model for pricing options. The model is capable of tracking options for a wide range of different underlying assets. The model is expected to perform better on Fourier-based fast pricing algorithms such as the COS Method, however in this thesis we’ll only look at Monte Carlo solvers for the HQH model. The type of option studied in this master’s thesis is European options, however, the implementation could be extended to other types of options.  Methodology: The methodology for evaluating the HQH model (in this paper) involves the use of a custom Monte Carlo simulation implemented in Python. The Monte Carlo method enables simulating multiple scenarios and provides reliable results across a variety of situations, making it an appropriate tool for evaluating the model's performance.  Evaluation: The HQH model is evaluated on ease of implementation in python and it’s general ability to reflect different market phenomena such as volatility in price movements.  Improvement: This thesis also investigates the possibility of improving the model or adding corrections, parameters, readjustments, or the like to the model to improve results. The aim is to enhance the model's usefulness, and this evaluation seeks to identify potential improvements.  Worth noting: The goal of this thesis is to align with the research interests of financial institutions and provide a practical, applied approach to evaluating options pricing models. The research presented in this thesis aims to mirror the type of projects that a company like Visigon may be requested to undertake by a bank (and engineering work in general). Additionally, the findings and methodology developed in this thesis aims to inform and contribute to future research in options pricing models which may help markets perform better.