In this thesis, a new method based on characteristic functions is proposed to estimate the jump component in a finite-activity Levy process, which includes the jump frequency and the jump size distribution. Properties of the estimators are investigated, which show that this method does not require high frequency data. The implementation of the method is discussed, and examples are provided. We also perform a comparison which shows that our method has advantages over an existing threshold method. Finally, two applications are included: one is the classification of the increments of the model, and the other is the testing for a change of jump frequency.

This thesis includes three chapters on electricity and natural gas prices. In the first chapter, we give a brief introduction to the characteristics of power prices and propose a mean reversion jump diffusion model, in which jump intensity depends on temperature data and overall system load, to model electricity prices. Compared to the models used in the literature, we find the model proposed in this chapter is better to capture the tail behavior in the electricity prices. In the second chapter, we use the model proposed in the first chapter to simulate the spark spread option and value the power generations. In order to simulate power generation, we first propose and estimate mean reversion jump diffusion model for natural gas prices, in which jump intensity is defined as a function of temperature and storage. Combing the model with the electricity models in chapter 1, we find that the value of power generation is closer to the real value of the power plants as reflected in the recent market transaction than one obtains from many other models used in literature. The third chapter investigates extremal dependence among the energy market. We find a tail dependence that exceeds the Pearson correlation ρ, which means the traditional Pearson correlation is not appropriate to model tail behavior of oil, natural gas and electricity prices. However, asymptotic dependence is rejected in all pairs except Henry Hub gas return and Houston Ship Channel gas return. We also find that extreme value dependence in energy market is stronger in bull market than that in bear market due to the special characteristics in energy market, which conflicts the accepted wisdom in equity market that tail correlation is much higher in periods of volatile markets from previous literature.

Digital options, the basic building blocks for valuing complex financial assets, they play an important role in options valuation and hedging. We survey the digital options pricing formula under diffusion processes and jump-diffusion processes. Since the existent first-touch digital options pricing formulas with jump-diffusion processes are all in their Laplace transform of the option value. To inverse the Laplace transforms is critical when doing options valuation. Therefore, we adopt a phase-type jump-diffusion model which is developed by Chen, Lee and Sheu [2007] as our main model, and use FFT inversion to get the first-touch digital option price under (2,2)-factor exponential jump-diffusion processes.

The purpose of this paper is to explore the information content in a trading, confirm the relationship between information-trading probability (PIN) and asset returns, and apply PIN to construct an investing strategy on a point of uninformed trader¡¦s view. I develop a decision marking model about trading decision between under order-driven market which is combined on the decision tree of the concept of D. Easley et al. (1997) and Merton (1976) jump diffusion model for modifying the PIN model to apply to order-driven market. As a result, the daily PIN were positive relatively with return, and the investing strategy which was based my model could make profit significantly in the sample period at TWSE in 2003, this investing strategy can earn profit in down and up market condition both. This result supports that hedging against information asymmetric risk is potential.

In this thesis, we suggest using nano-relays in a network using molecular com- munication in free space to improve the performance of the system in terms of delay. An approximation method for jump diffusion processes, which is based on Markov chains, is used to model molecular propagation in such scenarios. The model is validated through comparing analytic results with simulation results. The results illustrate the advantage of using nano-relays over diffusion in terms of delay. The proposed model is then used to inves- tigate the effect of different parameters, such as filaments’ length and the number of filaments attached to each nano-relay, on the delay performance of the communication technique. We used transient solution of the model in the first set of results. How- ever, stationary solution of the model can generate useful results, too. In the second set of results, the model is extended for an unbounded scenario. Con- sidering the propagation as a one-sided skip free process and using matrix analytic methods, we find the final distribution for the position of informa- tion molecules. It will be shown that it is possible to keep molecules in a desired region. The effect of different parameters on the final distribution for the position of information molecules is investigated, too. This analysis can be useful in drug delivery applications. / February 2016

>Magister Scientiae - MSc / We discuss efficient Monte Carlo methods for pricing of electricity derivatives. Electricity derivatives are risk management tools used in deregulated electricity markets. In the past,research in electricity derivatives has been dedicated in the modelling of the behaviour of electricity spot prices. Some researchers have used the geometric Brownian motion and the Black Scholes formula to offer a closed-form solution. Electricity spot prices however have unique characteristics such as mean-reverting, non-storability and spikes that render the use of geometric Brownian motion inadequate. Geometric Brownian motion assumes that changes of the underlying asset are continuous and electricity spikes are far from being continuous. Recently there is a greater consensus on the use of Mean-Reverting Jump-Diffusion (MRJD) process to describe the evolution of electricity spot prices. In this thesis,we use Mean-Reverting Jump-Diffusion process to model the evolution of electricity spot prices. Since there is no closed-form technique to price these derivatives when the underlying electricity spot price is assumed to follow MRJD, we use Monte Carlo methods to value electricity forward contracts. We present variance reduction techniques that improve the accuracy of the Monte Carlo Method for pricing electricity derivatives.

This work presents a class of models in asset pricing, whose underlying has dynamics of Affine jump diffusion type. We first present L´evy processes with their properties. We then introduce Affine jump diffusion processes which are basically a particular class of L´evy processes. Our motivation for these is driven by the fact that many financial models are built on them. Affine jump diffusion processes present good analytical properties that allow one to get close form formulas for a wide range of option pricing. The approach we use here is based on the paper by Duffie D, Pan J, and Singleton K. An example will show how incorporating parameters such as the volatility of the underlying asset in the model, can influence the resulting price of the financial instrument under consideration. We will also show how this class of models incorporate well known models, specially those used to model interest rates dynamics, like for instance the Vasicek model. / Dissertation (MSc (Mathematics of Finance))--University of Pretoria, 2008. / Mathematics and Applied Mathematics / unrestricted

The inverse problem in finance consists of determining the unknown parameters of the pricing equation from the values quoted from the market. We formulate the inverse problem as a minimization problem for an appropriate cost function to minimize the difference between the solution of the model and the market observations. Efficient gradient based optimization requires accurate gradient estimation of the cost function. In this thesis we highlight the adjoint method for computing gradients of the cost function in the context of gradient based optimization and show its importance. We derive the continuous adjoint equations with appropriate boundary conditions for three main option pricing models: the Black-Scholes model, the Heston's model and the jump diffusion model, for European type options. These adjoint equations can be used to compute the gradient of the cost function accurately for parameter estimation problems. The adjoint method allows efficient evaluation of the gradient of a cost function F(σ) with respect to parameters σ where F depends on σ indirectly, via an intermediate variable. Compared to the finite difference method and the sensitivity equation method, the adjoint equation method is very efficient in computing the gradient of the cost function. The sensitivity equations method requires solving a PDE corresponding to each parameter in the model to estimate the gradient of the cost function. The adjoint method requires solving a single adjoint equation once. Hence, for a large number of parameters in the model, the adjoint equation method is very efficient. Due to its nature, the adjoint equation has to be solved backward in time. The adjoint equation derived from the jump diffusion model is harder to solve due to its non local integral term. But algorithms that can be used to solve the Partial Integro-Differential Equation (PIDE) derived from jump diffusion model can be modified to solve the adjoint equation derived from the PIDE. / Ph. D.

In this masterthesis I have rewied basic approaches to volatility estimating. These approaches are based on classical and Bayesian statistics. I have applied the volatility models for the purpose of volatility forecasting of a different foreign exchange (EURUSD, GBPUSD and CZKEUR) in the different period (from a second period to a day period). I formulate the models EWMA, GARCH, EGARCH, IGARCH, GJRGARCH, jump diffuison with constant volatility and jump diffusion model with stochastic volatility. I also proposed an MCMC algorithm in order to estimate the Bayesian models. All the models we estimated as univariate models. I compared the models according to Mincer Zarnowitz regression. The most successfull model is the jump diffusion model with a stochastic volatility. On the second place they were the GJR- GARCH model and the jump diffusion model with a constant volatility. But the jump diffusion model with a constat volatilit provided much more overvalued results.The rest of the models were even worse. From the rest the IGARCH model is the best but provided undervalued results. All these findings correspond with R squared coefficient.

The paper examines the impact of fees on the return of a fund investment using different simulation and fee structure models. The results show that fees have a significant expected impact, particularly for well-performing funds. Two simulation models were used, the Geometric Brownian Motion (GBM) model and Merton Jump Diffusion (MJD) model. Two fee structures were also analysed for each simulation, a High-water mark fee structure and a Hurdle fee structure. Comparing the GBM and MJD models, the two tend to generate very similar fee statistics even though the MJD model's day-to-day returns fit better with empirical data. When comparing the HWM and Hurdle fee models, larger differences are observed. While overall average fee statistics are similar, the performance fee statistics are significantly higher in the Hurdle fee structure for assets achieving higher returns, e.g. at least an 8% annual return. However, the HWM fee structure tends to generate higher performance fees for assets with low returns. Regression models are also developed for each combination of the simulation model and fee structure. The regression models reflect the above conclusions and can for investors serve as simple key indicators to estimate expected fund fee payments. The GBM regression results are likely more useful than the MJD regression results, as the parameters of the former are easier to calculate based on historical return data. / Uppsatsen undersöker effekten av avgifter på avkastningen av en fondinvestering med hjälp av olika simuleringar och avgiftsmodeller. Resultaten visar att avgifter förväntas ha en betydande påverkan, särskilt för fonder som genererar hög avkastning. Två simuleringar användes, Geometric Brownian Motion (GBM) och Merton Jump Diffusion (MJD). Två avgiftsstrukturer analyserades också för varje simulering, en High-water mark avgiftsstruktur och en Hurdle avgiftsstruktur. Jämförelse mellan GBM och MJD-modellerna visar att de två tenderar att generera mycket liknande avgiftsstatistik trots att MJD-modellens dagliga avkastning passar bättre med empiriska data. Vid jämförelse av HWM- och Hurdle avgiftsmodellerna observeras större skillnader. Medan den övergripande genomsnittliga avgiftsstatistiken är liknande för avgiftsmodellerna, är resultatbaserade avgifterna betydligt högre i Hurdle avgiftsstrukturen för tillgångar som uppnår högre avkastning, t.ex. minst 8% årlig avkastning. Däremot tenderar HWM-avgiftsstrukturen att generera högre resultatbaserade avgifter för tillgångar med låg avkastning. Regressionsmodeller utvecklades också för varje kombination av simulering och avgiftsstruktur. Regressionmodellerna återspeglar ovanstående slutsatser och kan för investerare fungera som enkla nyckeltal för att uppskatta förväntad kostnad av fondavgifter. GBM-regressionsresultaten är sannolikt mer användbara än MJD-regressionsresultaten, eftersom parametrarna för den förra är lättare att beräkna baserat på historisk avkastningsdata.