Credit risk of derivative securities includes the risk of underlying company and the risk of seller's nonfulfilment of contracts. Take bonds for example, we regard Treasury bills as default-free bonds, and corporate bonds as risky bonds. When the liability of property of derivative securities underlying company is less than 1, we regard the company is of bankruptcy. And then the seller of derivative securities will break the contract. The essay extends two period risky bonds pricing valuation of Jarrow and Turnbull(1995) to multiperiod situation, and derive arbitrage-free condition. Furthermore, we derive formulae of risky bonds prices by assuming the logarithm of the odds ratio of an underlying company's bankruptcy probability satisfies an AR(1) or MA(1) processes. Empirical data of Rebar, Chinarebar, Ceon are studied, time series models are established for logarithm of odds ratios. In most cases, we find that the log odds ratios can be well fitted by AR(1) models.

The random walk is a powerful model. Chemistry, Physics, and Finance are just a few of the disciplines that model with the random walk. It is clear from its varied uses that despite its simplicity, the simple random walk it very flexible. There is one major drawback, however, to the simple random walk and the geometric random walk. The limiting distribution is either normal, lognormal, or a levy process with infinite variance. This thesis introduces an new random walk aimed at overcoming this drawback. Because the simple random walk and the geometric random walk are special cases of the proposed walk, it is called a generalized random walk. Several properties of the generalized random walk are considered. First, the limiting distribution of the generalized random walk is shown to include a large class of distributions. Second and in conjunction with the first, the generalized random walk is compared to the geometric random walk. It is shown that when parametrized properly, the generalized random walk does converge to the lognormal distribution. Third, and perhaps most interesting, is one of the limiting properties of the generalized random walk. In the limit, generalized random walks are closely connected with a u function. The u function is the key link between generalized random walks and its difference equation. Last, we apply the generalized random walk to option pricing.

"This project is an investigation and implementation of pricing derivative securities using the forward measure. It will explain the methodology of building a modified discrete Ho-Lee interest rate model to do so, along with the extraction of historical yield and interest rates to calibrate the model. "

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This dissertation research focuses on modeling and evaluating multivariate uncertainties and the dependency between the uncertainties. Managing risk and making strategic decisions under uncertainty is critically important for both individual and corporate success. In this dissertation research, we present two new methodologies, the implied binomial tree approach and the dependent decision tree approach, to modeling multivariate decision making problems with practical applications in real options valuation. First, we present the implied binomial tree approach to consolidate the representation of multiple sources of uncertainty into univariate uncertainty, while capturing the impact of these uncertainties on the project’s cash flows. This approach provides a nonparametric extension of the approaches in the literature by allowing the project value to follow a generalized diffusion process in which the volatility may vary with time and with the asset prices, therefore offering more modeling flexibility. This approach was motivated by the Implied Binomial Tree (IBT) approach that is widely used to value complex financial options. By constructing the implied recombining binomial tree in a way so as to be consistent with the simulated market information, we extended the finance-based IBT method for real options valuation — when the options are contingent on the value of one or more market related uncertainties that are not traded assets. Further, we present a general framework based on copulas for modeling dependent multivariate uncertainties through the use of a decision tree. The proposed dependent decision tree model allows multiple dependent uncertainties with arbitrary marginal distributions to be represented in a decision tree with a sequence of conditional probability distributions. This general framework could be naturally applied in decision analysis and real options valuations, as well as in more general applications of dependent probability trees. While this approach to modeling dependencies can be based on several popular copula families as we illustrate, we focus on the use of the normal copula and present an efficient computational method for multivariate decision and risk analysis that can be standardized for convenient application. / text

Barrier options are the most popular and traded derivatives in the financial market because of their lower prices. Many studies have been conducted to develop the methods of pricing barrier options. Barrier option prices can be calculated using the classical binomial tree method, but it is time-consuming when we have a large number of time periods. Muroi and Yamada have developed a new fast algorithm to obtain the prices of barrier options by using the spectral expansion approach. We implement and check this algorithm by doing more extensive numerical experimental studies and showing that the same prices calculated using the binomial tree method can also be obtained using the spectral binomial tree approach with a higher computational speed.

We consider the N step binomial tree model of stocks. Call options and put options of European and American type are computed explicitly. With appropriate scaling in time and jumps,  convergence of the stock prices and the option prices are obtained as N-> infinite. The obtained convergence is the Black-Scholes model and, for the particular case of European call option, the Black-Scholes formula is obtained. Furthermore, the Black-Scholes partial differential equation is obtained as a limit from the N step binomial tree model. Pricing of American put option under the Black-Scholes model is obtained as a limit from the N step binomial tree model. With this thesis, option pricing under the Black-Scholes model is achieved not by advanced stochastic analysis but by elementary, easily understandable probability computation. Results which in elementary books on finance are mentioned briefly are here derived in more details. Some important Java codes for N step binomial tree option prices are constructed by the author of the thesis.

The interest rate dynamics is an important fundamental for valuation more complex structures of interest rate derivatives. The goal of this diploma thesis is to describe the use of models of interest rate for interest rate option pricing. The paper could be logically divided into two parts, the theoretical one and practical one. In the first part the essentials for pricing theory are introduced as risk neutrality, martingales, stochastic differential calculus, and theory of arbitrage. On their basis four basic yield curve models are derived, Vasicek model, model Cox-Ingersoll-Ross , Black-Derman-Toy and two factor Heath-Jarrow-Morton model. Second part provides the analysis of yields of U.S. Treasury bonds with different maturity. At the end CIR model and BDT binomial tree are used for valuation of option on 10 years yield.

傳統上描述利率期間結構，不外乎藉由瞬間短期利率的隨機過程（如：Hull and White模型），或瞬間遠期利率的隨機過程（如：HJM模型）。應用這些方式理論上雖然可行，但是市場上並無法觀察得知這些瞬間利率。 Brace-Gatarek-Musiela利率模型（簡稱BGM模型）是將HJM模型間斷化，直接推導市場上可觀察得到之LIBOR利率的隨機過程，用它來描述市場利率期間結構，並利用數學的技巧，推導出符合對數常態的型式，方便使用Black公式來求解，且同時考慮LIBOR利率之波動程度，透過與市場資料的校準，符合市場上的利率期間結構及利率波動結構，有助於利率衍生性商品的訂價與避險。 由於市場上有愈來愈多的利率衍生性商品，不是由單純的cap、swaption來組成，例如：路徑相依選擇權、美式選擇權、回顧型選擇權…等，這些新奇選擇權要求出評價公式很難，所以通常使用數值方法來評價。常用的數值方法有蒙地卡羅模擬法及樹狀圖評價法，由於使用蒙地卡羅模擬法處理起來較耗時，而且評價美式選擇權比較麻煩，而樹狀圖評價法較省時，且應用較廣。因此，本文除了詳細推導BGM利率模型，並建構出BGM利率模型下的利率樹，來對這些新奇選擇權做評價。 最後做一實證分析，以市場上的所發行的利率連動債券為例，對於匯豐銀行美元護本109利率連動債券的設計、評價、損益分析及其相關議題做詳盡的探討。

O objetivo desta dissertação é fornecer primeiramente o arcabouço necessário para o entendimento do derivativo opções, muito utilizado nos mercados financeiros mundiais, e posteriormente executar precificações de opções americanas a partir dos modelos dos mínimos quadrados de Monte Carlo (LSM), o modelo de árvore binomial com extrapolação de Richardson e a aproximação analítica de Bjerksund e Stensland (B&S), aplicando duas plataformas de processamento paralelo computacional, a TPL (Task Parallel Library) nativa no .NET framework 4.5 e a plataforma CUDA (Compute Unified Device Architecture), demonstrando o comparativo dos resultados obtidos a cada modelo diante de cada plataforma. / The objective of this dissertation is to provide first the necessary framework for the understanding of the derivative options, widely used in the world financial markets, and later to execute the American option pricing from Monte Carlo least squares models (LSM), the binomial tree model with Richardson extrapolation and the Bjerksund and Stensland analytic approach (BJS) by applying two parallel computational processing platforms, the native TPL (Task Parallel Library) in the .NET framework 4.5 and the CUDA platform (Compute Unified Device Architecture), demonstrating the comparison of the obtained results to each model before each platform.