In this thesis, we expand the option pricing and virtual asset model system by Cheng (2005) and include new simulations and maximum likelihood estimation of the parameter of the stochastic differential equations. For easy manipulation of general users, the interface of original option pricing system is modified. In addition, in order to let the system more completely, some stochastic models and methods of pricing and estimation are added. This system can be divided into three major parts. One is an option pricing system; The second is an asset model simulation system; The last is estimation system of the parameter of the model. Finally, the analysis for the data of network are carried out. The differences of the prices between estimator of this system and real market are compared.

A Constant Proportion Debt Obligation is a credit derivative which has been introduced to generate a surplus return over a riskless market return. The surplus payments should be obtained by synthetically investing in a risky asset (such as a credit index) and using a linear leverage strategy which is capped for bounding the risk. In this thesis, we investigate two approaches for investigation of constant proportion debt obligations. First, we search for an optimal leverage strategy which minimises the mean-square distance between the final payment and the final wealth of constant proportion debt obligation by the use of optimal control methods. We show that the optimal leverage function for constant proportion debt obligations in a mean-square sense coincides with the one used in practice for geometric type diffusion processes. However, the optimal strategy will lead to a shortfall for some cases. The second approach of this thesis is to develop a pricing formula for constant proportion debt obligations. To do so, we consider both the early defaults and the default on the final payoff features of constant proportion debt obligations. We observe that a constant proportion debt obligation can be modelled as a barrier option with rebate. In this respect, given the knowledge on barrier options, the pricing equation is derived for a particular leverage strategy.

Traditionally, time series of wholesale electricity market spot prices have been modelled either by mimicking market operation and equilibrating demand and supply, or by specifying an exogenous process for prices. More recently, a number of hybrid models have been developed, combining the merits of both methods. In this vein, we present an econometric model for daily spot prices in the New Zealand Electricity Market (NZEM) that utilises reservoir management theory to incorporate information on the hydro storage level, a recognised driver of NZEM spot price behaviour. In order to forecast future storage levels and prices, we also construct a model for daily reservoir releases that can be used in conjunction with time series of inflows. This analysis reveals that releases in New Zealand are driven primarily by hydrological factors, as opposed to market conditions. The combined price and storage forecasting model can be applied in a variety of contexts, and offers an alternative perspective to the traditional models of NZEM behaviour. Finally, we calibrate a Cournot model of market behaviour in the National Electricity Market of Australia during daily peak, shoulder and off-peak periods, adding credibility to the future application of such models. The resulting model parameters are, in general, consistent with conventional wisdom. Spot prices from this market are then modelled by combining the output of the analytical model with a stochastic price process.

The main target of this thesis is to summarize and explain the specifics of power markets and test application of models, which might be used especially in risk management area. Thesis starts with definition of market subjects, typology of traded contracts and description of market development with focus on Czech Republic. Thesis continues with development of theoretical concepts of short term/spot electricity markets and potential link between spot and forward electricity markets. After deriving of those microeconomic fundamental models we continue with stochastic models (Jump Diffusion Mean Reverting process and Extreme Value Theory) in order to depict patterns of spot and forward power contracts price volatility. Last chapter deals with credit risk specifics of power trading and develops model (using concept known as Credit Value Adjustment) to compare economic efficiency of OTC and exchange power trading. Developed and described models are tested on selected power markets, again with focus on Czech power market data set.

In this paper we discuss the stable solvability of the inverse problem of parameter identification in a jump diffusion model. Therefore we introduce the forward operator of this inverse problem and analyze its properties. We show continuity of the forward operator and stability of the inverse problem provided that the domain is restricted in a specific manner such that techniques of compact sets can be exploited. Furthermore, we show that there is an asymptotical non-injectivity which causes instability problems whenever the jump intensity increases and the jump heights decay simultaneously.

In this note, we consider an inverse problem in a jump diffusion model. Using characteristic functions we prove the injectivity of the forward operator mapping the five parameters determining the model to the density function of the return distribution.

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.

可轉換公司債是一種複雜且擁有許多風險的商品，而對於台灣的可轉債市場來說，信用風險佔了評價裡很重要的一部份。本篇論文使用縮減式評價模型，考慮信用風險及股價跳躍。跳躍模型使用Pareto-Beta Jump-Diffusion模型，並且利用信用價差之動態過程，來對可轉換公司債作評價，而為了解決提前轉換的問題，也使用了最小平方蒙地卡羅法來處理。本篇論文分別對宏碁與新光金之可轉債做實證研究，實證結果顯示，加入了股價跳躍之後，的確可以使理論價格更貼近市場真實價格。

In the presented thesis we study three methods of pricing European currency barrier options. With help of these methods we value selected barrier options with underlying asset EUR/CZK. In the first chapter we introduce the basic definitions from the world of financial derivatives and we describe our data. In the second chapter we deal with the classical model based on geometric Brownian motion of underlying asset and we prove a theorem of valuating Up-In-barrier option in this model. In the third chapter we introduce a model with stochastic volatility, the Heston model. We calibrate this model to market data and we use it to value our barrier options. In the last chapter we describe a jump diffusion model. Again we calibrate this jump diffusion model to market data and price our barrier options. The aim of this thesis is to decribe and to compare different methods of valuating barrier options. 1

Value-at-Risk (VaR) model fails to predict financial risk accurately especially during financial crises. This is mainly due to the model’s inability to calibrate new market information and the fact that the risk measure is characterised by poor tail risk quantification. An alternative approach which comprises of the Expected Shortfall measure and the Lognormal Jump-Diffusion (LJD) model has been developed to address the aforementioned shortcomings of VaR. This model is called the Simulated-Expected-Shortfall (SES) model. The Maximum Likelihood Estimation (MLE) approach is used in determining the parameters of the LJD model since it’s more reliable and authenticable when compared to other nonconventional parameters estimation approaches mentioned in other literature studies. These parameters are then plugged into the LJD model, which is simulated multiple times in generating the new loss dataset used in the developed model. This SES model is statistically conservative when compared to peers which means it’s more reliable in predicting financial risk especially during a financial crisis. / Statistics / M.Sc. (Statistics)