Master-Arbeit Univ. St. Gallen, 2008.

This thesis consists of three essays. The first two essays present empirical studies in which option market features related to information flows are examined. The third essay introduces a theoretical model to explain predictable dynamics in option pricing through the agents' learning process. In the first essay, I investigate the previously unexplored effects of asymmetric information on the adoption process of new equity options introduced into the market. I use a microstructure model to estimate measures of informational asymmetries. I discover that high informational asymmetries in the year prior to option listings produce larger levels of option adoption. Additionally, I find that option introductions induce reductions in asymmetric information. I also report that option bid-ask spreads start from low initial levels with a tendency to increase over time, which is unexpected since the introduced options are initially illiquid; however, this can be explained by the low level of initial activity by informed agents.The second essay examines whether the dynamics of the implied volatility surface of equity options contain exploitable predictability patterns. The option pricing predictability is expected due to the learning behaviour of agents in option markets. In particular, I explore the possibility that the dynamics of the implied volatility surface of individual equity options may be associated with subsequent movements in the volatility surface implicit in S&P 500 index options. I present evidence of strong relationships in the cross-section and the dynamics between implied volatility surfaces of equity options and S&P 500 index options. Moreover, I show that the predictability patterns of equity options are better characterized by the incorporation of information from the recent dynamics in the implied volatility surface of S&P 500 index options. Additionally, I analyse the economic value of the equity option predictability through trading strategies using straddle and delta-hedged portfolios, which produce abnormal risk-adjusted returns.Finally in the third essay, I introduce an equilibrium model to explain predictability patterns in option pricing through the learning process followed by investors. In this model the unknown fundamental dividend growth rate is subject to breaks, where the time periods between breaks follow a memoryless stochastic process. Immediately after a break there is insufficient information to price option contracts accurately. Therefore, a representative Bayesian agent has to learn step by step as new information arrives regarding the new fundamental value. I show that learning makes beliefs time-varying, which produces dynamic biases in option prices and implied volatilities. In addition, I find that learning generates different dynamic impacts on option contracts across moneyness and time-to-maturity; and hence it induces dynamics on the implied volatility surface. Furthermore, similarly to the predictability features observable in option market data, learning mechanisms make the option pricing dynamics predictable.

No description available.

Trois chapitres constituent cette thèse.Le premier traite des performances des modèles de valorisation traditionnels. Au travers une littérature détaillée, il met en exergue les facteurs qui impactent la structure financière ainsi que des ajustements théoriques en vue d’améliorer les différentes méthodes de valorisation. Puis, il aborde la création de valeur issue d’opérations de contrôle et expose les méthodes à privilégier en fonction de contextes déterminés. Il présente ensuite des études statistiques visant à attester de la fiabilité et de la pertinence des méthodes traditionnelles.Le deuxième est dédié à l’évaluation de la décision d’investissement par l’approche des options réelles. Tout d’abord, un cadre définit leur modélisation et leur niveau d’utilisation actuelle par les praticiens. Ensuite, la littérature étudiée développe les interactions des différentes catégories d’options présentes au sein d’un même projet d’investissement. Elle dévoile, notamment, les fondements des modèles de l’option d’attente, qui permet de déterminer le moment opportun pour investir, de l’option de désinvestissement, y compris au sein de contextes particuliers, et de l’option de croissance, qui affecte les choix de diversification et de stratégies d’acquisition. Ces différents modèles font l’objet d’applications pratiques.Enfin, le troisième s’attache à mettre en lumière l’évaluation de la structure du passif financier par l’approche des options réelles. Les modèles optionnels décrits dans la littérature proposent une nouvelle répartition de la valeur d’entreprise entre une valeur économique des capitaux propres et de la dette nette. Puis, les articles étudiés évoquent l’intégration des problèmes d’agence et du refinancement de la dette à partir de modèles optionnels. Enfin, trois études statistiques ont pour objectif de comparer des valorisations de sociétés depuis des méthodes traditionnelles et depuis celle des options réelles. Il s’agit de déterminer si la méthode des options octroie un surplus de valeur aux capitaux propres, de par la prise en compte d’une dette nette économique. Par ailleurs, les analyses réalisées visent à attester de la pertinence et de la fiabilité de la méthode des options réelles par rapport à aux méthodes traditionnelles. / This thesis is organized around three chapters.The first one deals with performances of traditional valuation methods. A detailed literature review highlights the factors that affect the financial structure and theoretical adjustments to improve the different valuation methods. Furthermore, the chapter is dedicated to value creation from control operations and outlines the preferred methods according to specific contexts. Finally, it presents statistical studies to demonstrate the reliability and relevance of traditional methods.The second chapter focuses on the assessment of the investment decision by the real options approach. First, their modeling framework is defined as well as their level of current use by practitioners. Then, the studied literature develops the interactions of different categories of options present within the same investment project. It reveals, in particular, the foundation for models of standby option, which determines the appropriate time to invest, the disinvestment option, including in particular contexts, and the growth option, which affects the choices of diversification and acquisition strategies. These different models are subject to practical applications.The third chapter aims to highlight the assessment of the financial liability structure by the real options approach. Optional models described in the literature suggest a new division of the enterprise value between economic value of equity and net debt. Moreover, the articles studied focus on the integration of agency and debt refinancing problems when using optional templates. Finally, three statistical studies aim to compare the valuation of companies based on traditional and real options methods in order to determine whether the options method gives a surplus value to equity by the inclusion of an economic net det. Furthermore, the purpose of the analyses is to attest to the relevance and reliability of the real options method compared to traditional methods.

<p>In this work we consider a problem of the</p><p>computation of the components of the hedging portfolio structure. In</p><p>literature often one can find valuations and estimations of the</p><p>fair price of American options. But the formulas for hedging portfolio</p><p>are interesting as well and are known for very particular cases</p><p>only. In our work we study different cases of American Put and Russian</p><p>options on finite and infinite horizon.</p>

In this work we consider a problem of the computation of the components of the hedging portfolio structure. In literature often one can find valuations and estimations of the fair price of American options. But the formulas for hedging portfolio are interesting as well and are known for very particular cases only. In our work we study different cases of American Put and Russian options on finite and infinite horizon.

The first option has been publicly traded for more than 30 years. With the progress of time, despite the European option is still the exchange-traded option. But evolved through the years, the European option has not meet people's needs, so exotic option was born. Similarly, the pricing model, from the traditional closed-form solution (under the Black-Scholes assumption), now commonly used binomial trees, finite difference, or by using the Monte Carlo simulation. The main impact of the following factors: the first, with the complexity of the option contract - from single asset to multi-assets, from the plain vanilla option to the path-dependent option, it is more difficult to find the closed-form solution of the option. Second, with the development of personal computers, making numerical computing is no longer a difficult task. It is precisely these two front reason, there will be the birth of this article. Himalaya option is also an exotic options. With the multi-assets and path dependent features, we want to find a closed-form solution is very difficult. Under multi-assets situation, the binomial tree and finite difference will be time-consuming calculation. Therefore, this paper is using Monte Carlo simulation of reasons. In this paper, we use Monte Carlo simulation to pricing Himalaya option, which includes several variance reduction techniques used to reduce sample variance. Finally, when pricing completed, we try to do a simple study to option hedging.

Thesis (M.B.A.)--University of Hong Kong, 1997.

Commodity trading firms work to remain competitive in the evolving agricultural industry. They work to become more efficient by increasing economies of size and scale, vertically and horizontally integrating, and diversifying geographically, or any combination of these avenues. Geographically diverse firms have access to multiple origins between which, spatial arbitrage opportunities can occur. When spatial arbitrage opportunities occur, firms take advantage of them to generate profit. Origin switching options are one way to take advantage of these opportunities. Origin switching option allow the seller of grain to fill a contract with any listed origin at the cost of the premium negotiated. This thesis helps to determine the value of these origin type switching options by developing a Monte Carlo simulation model with real option analysis. Soybean and corn markets are analyzed in the U.S. Gulf, Pacific Northwest, Brazil, Argentine, and origins with China and Japan as the respective destinations.

Master of Science in Engineering - Engineering / Artificial Neural Networks has seen tremendous growth in recent years. It has been applied to various sciences, including applied mathematics, chemistry, physics, and engineering and has also been implemented in various areas of finance. Many researchers have applied them to forecasting of stock prices and other fields of finance. In this study we focus on option pricing. An option is a contract giving the buyer of the contract the right but not the obligation to purchase stock on or before a certain expiration date. Options have become a multi-billion dollar industry in modern times, and there has been a lot of focus on pricing these option contracts. Option pricing data is highly non-linear and its pricing has its basis in stochastic calculus. Since neural networks have excellent non-linear modeling capabilities, it seems obvious to apply neural networks to option pricing. In this thesis, many different methodologies are developed to model the data. The multilayer perceptron and radial basis functions are used in the stand-alone neural networks. Then, the architectures of the stand-alone networks are optimized using particle swarm optimization, which leads to excellent results. Thereafter, a committee of neural networks is investigated. A committee network is an average of a combination of stand-alone neural networks. In contrast to stand-alone networks, a committee network has great generalization capabilities. Many different methods are developed for attaining optimal results from these committee networks. The methods included different forms of weighting the stand-alone networks, a non-linear combination of the committee members using another stand-alone neural network, a two layer committee network where the second layer was used for smoothing the output and a circular committee network. Lastly, genetic algorithm, with the Metropolis-Hastings algorithm, was used to optimize the committee of neural networks. Finally all these methods were analyzed.