The use of forward-looking information from option prices attracted a lot of attention after the 2008 financial crisis, which highlighting the difficulty of using historical data to predict extreme events. Although a considerable number of papers investigate extraction of forward-information from cross-sectional option prices, Figlewski (2008) argues that it is still an open question and none of the techniques is clearly superior. This thesis focuses on getting information from option prices and investigates two broad topics: applying machine learning in extracting state price density and recovering natural probability from option prices. The estimation of state price density (often described as risk-neutral density in the option pricing litera- ture) is of considerable importance since it contains valuable information about investors' expectations and risk preferences. However, this is a non-trivial task due to data limitation and complex arbitrage-free constraints. In this thesis, I develop a more efficient linear programming support vector machine (L1-SVM) estimator for state price density which incorporates no-arbitrage restrictions and bid-ask spread. This method does not depend on a particular approximation function and framework and is, therefore, universally applicable. In a parallel empirical study, I apply the method to options on the S&P 500, showing it to be comparatively accurate and smooth. In addition, since the existing literature has no consensus about what information is recovered from The Recovery Theorem, I empirically examine this recovery problem in a continuous diffusion setting. Using the market data of S&P 500 index option and synthetic data generated by Ornstein-Uhlenbeck (OU) process, I show that the recovered probability is not the real-world probability. Finally, to further explain why The Recovery Theorem fails and show the existence of associated martingale component, I demonstrate a example bivariate recovery.

Yes / This paper compares several widely-used and recently-developed methods to extract risk-neutral densities (RND) from option prices in terms of estimation accuracy. It shows that positive convolution approximation method consistently yields the most accurate RND estimates, and is insensitive to the discreteness of option prices. RND methods are less likely to produce accurate RND estimates when the underlying process incorporates jumps and when estimations are performed on sparse data, especially for short time-to-maturities, though sensitivity to the discreteness of the data differs across different methods.

The aim of the thesis is to present a robust conceptual framework for risk management of non-financial companies in order to improve decision making in the area of hedging with derivative instruments. Application of modern quantitative methods.

In this thesis, we study the estimation of the nonlinear conditionalrisk-neutral density function (RND) in discrete time. Specifically, weevaluate the extent to which the estimated nonlinear conditional RNDvaluable insights to answer relevant economic questions regarding to optionpricing, the measurement of invertors' preferences and portfolio choice.We make use of large dataset of options contracts written on the S&P 500index from 1996 to 2011, to estimate the parameters of the conditional RNDfunctions by minimizing the squared option pricing errors delivered by thenonlinear models studied in the thesis.In the first essay, we show that a semi-nonparametric option pricing modelwith GARCH variance outperforms several benchmarks models in-sample andout-of-sample. In the second essay, we show that a simple two-state regimeswitching model in volatility is not able to fully account for the pricingkernel and the risk aversion puzzle; however, it provides a reasonablecharacterisation of the time-series properties of the estimated riskaversion.In the third essay, we evaluate linear stochastic discount factormodels using an out-of-sample financial metric. We find that multifactormodels outperform the CAPM when this metric is used, and that modelsproducing the best fit in-sample are also those exhibiting the bestperformance out-of-sample.

Esta tese promove uma integração entre Finanças e Teoria de Informação para criação de um ambiente alternativo para a determinação do preço racional da opção européia simples ordinária sobre ação e ativo de renda fixa (bond). Uma das características deste novo ambiente de determinação de preço racional é poder continuar utilizando o cálculo newtoniano em vez do estocástico. Cria uma notação matemática precisa e completa para a Teoria da Informação e a integra com a teoria de Finanças em condições de incerteza. Integra as abordagens entrópicas de determinação do preço racional da opção européia simples ordinária de Gulko (1998 e 1998a) e de Yang (1997). Define precisamente o mundo com preço da incerteza neutralizado (risk-neutral world), o mundo martingale, o mundo informacionalmente eficiente e o mundo entrópico e suas implicações para a Ciência do Investimento e, mais especificamente, para a determinação do preço racional de ativos básicos e derivativos. Demonstra detalhadamente a fórmula do preço racional da opção européia simples ordinária de Black-Scholes-Merton, melhorando a notação matemática, simplificando (eliminando a abordagem martingale) e complementando a demonstração feita por Baxter & Rennie (1998). Interrompe uma sucessão de trabalhos que estabelecem uma forma equivocada de calcular o preço da opção européia simples ordinária. Esse erro teve sua origem, muito provavelmente, numa edição de Brealey & Myers, que equivocadamente utilizou um resultado de Cox & Rubinstein (1985); esse resultado facilitava o cálculo do preço racional da opção européia simples ordinária por meio de uma tabela que evita o uso direto da fórmula de Black-Scholes-Merton. Brealey & Myers (desde a quarta edição de 1991), Luehrman (nos seus dois artigos da HBR de 1998 e um caso de 1995 pela HBS) e Edleson (caso publicado em 1994 pela HBS) ensinam que o valor percentual encontrado nessa tabela deve ser multiplicado pelo preço do valor mobiliário, quando deveria ser multiplicado pelo valor presente do preço de exercício. Os resultados mais importantes desta tese para Finanças são: (i) desenvolvimento de um método alternativo, robusto e parcimonioso, baseado no princípio da máxima entropia da Teoria da Informação e do Sistema de Distribuições de Pearson para obtenção de uma única medida de probabilidade neutralizadora do preço da incerteza (risk-neutral probability), (ii) obtenção de fórmula prática para a determinação do preço racional da opção européia simples ordinária para ação, (iii) validação da fórmula de Black-Scholes-Merton para ação, (iv) obtenção de uma fórmula adequada para a determinação do preço racional da opção européia simples ordinária sobre um título de renda fixa (bond), (v) estimação da volatilidade implícita entrópica do preço do valor mobiliário e (vi) definição e estimação do valor em risco (value at risk) entrópico. Há ainda dois resultados importantes para a Teoria da Informação e Economia: (i) distinção mais precisa entre incerteza e risco e (ii) desenvolvimento da medida de ganho informacional da previsão aprimorando o resultado de Theil (1967) e Benish (1999) pela utilização do conceito de divergência de Kullback-Leibler. / This thesis integrates Finance and Information Theory in order to create an alternative environment to the calculation of the rational price of the simple ordinary European option over stocks and bonds. One of the features of this new environment is to allow us to continue using the Newtonian calculus instead of the stochastic one. It creates a precise and complete mathematical notation for the Information Theory and integrates it with the Finance Theory under uncertainty conditions. It integrates Gulko’s (1998 and 1998a) and Yang’s (1997) entropic approaches to the calculation of the rational price of the simple ordinary European option. It precisely defines the uncertainty-price-neutral world (risk-neutral world), the martingale world, the informationally efficient world and the entropic world and their implications to the Investment Science and, more specifically, to the calculation of the rational price of ordinary assets and derivatives. It demonstrates with details the Black-Scholes-Merton formula of the rational price of the simple ordinary European option, improves the mathematical notation, simplifies it (by eliminating the martingale approach) and completes the demonstration done by Baxter & Rennie (1998). It breaks a succession of works that established a mistaken way to calculate the price of the simple ordinary European option. This mistake had its origin, much probably, in an edition of Brealey & Myers, who erroneously used a result from Cox & Rubinstein (1985). This result facilitates the calculation of the rational price of the simple ordinary European option by using a table that avoids the direct usage of the Black-Scholes-Merton formula. Brealey & Myers (since the 1991 fourth edition), Luehrman (in his two 1998 articles in HBR and in a 1995 case in HBS) and Edleson (1994 case published in HBS) teach that the percentage value found in this table must be multiplied by the price of the asset, when in reality it should have been multiplied by the present value of the strike price. The most important results of this thesis for Finance are: (i) development of a robust and economic alternative method, based on the maximum-entropy principle of the Information Theory and on Pearson’s Distribution System, to the calculation of a unique uncertainty-price-neutral probability measure (risk-neutral probability), (ii) achievement of a practical formula to the calculation of the rational price of the simple ordinary European option on stocks, (iii) validation of the Black-Scholes-Merton formula on stocks, (iv) achievement of an adequate formula to the calculation of the rational price of the simple ordinary European option on bonds, (v) estimation of the implied entropic volatility of the price of an asset and (vi) definition and estimation of the entropic value-at-risk. There are still two important results to the Information Theory and to Economics: (i) a more precise distinction between uncertainty and risk and (ii) development of the forecast informational gain, an enhancement of the result of Theil (1967) and Benish (1999) by using the Kullback-Leibler divergence concept.

Esta tese promove uma integração entre Finanças e Teoria de Informação para criação de um ambiente alternativo para a determinação do preço racional da opção européia simples ordinária sobre ação e ativo de renda fixa (bond). Uma das características deste novo ambiente de determinação de preço racional é poder continuar utilizando o cálculo newtoniano em vez do estocástico. Cria uma notação matemática precisa e completa para a Teoria da Informação e a integra com a teoria de Finanças em condições de incerteza. Integra as abordagens entrópicas de determinação do preço racional da opção européia simples ordinária de Gulko (1998 e 1998a) e de Yang (1997). Define precisamente o mundo com preço da incerteza neutralizado (risk-neutral world), o mundo martingale, o mundo informacionalmente eficiente e o mundo entrópico e suas implicações para a Ciência do Investimento e, mais especificamente, para a determinação do preço racional de ativos básicos e derivativos. Demonstra detalhadamente a fórmula do preço racional da opção européia simples ordinária de Black-Scholes-Merton, melhorando a notação matemática, simplificando (eliminando a abordagem martingale) e complementando a demonstração feita por Baxter & Rennie (1998). Interrompe uma sucessão de trabalhos que estabelecem uma forma equivocada de calcular o preço da opção européia simples ordinária. Esse erro teve sua origem, muito provavelmente, numa edição de Brealey & Myers, que equivocadamente utilizou um resultado de Cox & Rubinstein (1985); esse resultado facilitava o cálculo do preço racional da opção européia simples ordinária por meio de uma tabela que evita o uso direto da fórmula de Black-Scholes-Merton. Brealey & Myers (desde a quarta edição de 1991), Luehrman (nos seus dois artigos da HBR de 1998 e um caso de 1995 pela HBS) e Edleson (caso publicado em 1994 pela HBS) ensinam que o valor percentual encontrado nessa tabela deve ser multiplicado pelo preço do valor mobiliário, quando deveria ser multiplicado pelo valor presente do preço de exercício. Os resultados mais importantes desta tese para Finanças são: (i) desenvolvimento de um método alternativo, robusto e parcimonioso, baseado no princípio da máxima entropia da Teoria da Informação e do Sistema de Distribuições de Pearson para obtenção de uma única medida de probabilidade neutralizadora do preço da incerteza (risk-neutral probability), (ii) obtenção de fórmula prática para a determinação do preço racional da opção européia simples ordinária para ação, (iii) validação da fórmula de Black-Scholes-Merton para ação, (iv) obtenção de uma fórmula adequada para a determinação do preço racional da opção européia simples ordinária sobre um título de renda fixa (bond), (v) estimação da volatilidade implícita entrópica do preço do valor mobiliário e (vi) definição e estimação do valor em risco (value at risk) entrópico. Há ainda dois resultados importantes para a Teoria da Informação e Economia: (i) distinção mais precisa entre incerteza e risco e (ii) desenvolvimento da medida de ganho informacional da previsão aprimorando o resultado de Theil (1967) e Benish (1999) pela utilização do conceito de divergência de Kullback-Leibler. / This thesis integrates Finance and Information Theory in order to create an alternative environment to the calculation of the rational price of the simple ordinary European option over stocks and bonds. One of the features of this new environment is to allow us to continue using the Newtonian calculus instead of the stochastic one. It creates a precise and complete mathematical notation for the Information Theory and integrates it with the Finance Theory under uncertainty conditions. It integrates Gulko’s (1998 and 1998a) and Yang’s (1997) entropic approaches to the calculation of the rational price of the simple ordinary European option. It precisely defines the uncertainty-price-neutral world (risk-neutral world), the martingale world, the informationally efficient world and the entropic world and their implications to the Investment Science and, more specifically, to the calculation of the rational price of ordinary assets and derivatives. It demonstrates with details the Black-Scholes-Merton formula of the rational price of the simple ordinary European option, improves the mathematical notation, simplifies it (by eliminating the martingale approach) and completes the demonstration done by Baxter & Rennie (1998). It breaks a succession of works that established a mistaken way to calculate the price of the simple ordinary European option. This mistake had its origin, much probably, in an edition of Brealey & Myers, who erroneously used a result from Cox & Rubinstein (1985). This result facilitates the calculation of the rational price of the simple ordinary European option by using a table that avoids the direct usage of the Black-Scholes-Merton formula. Brealey & Myers (since the 1991 fourth edition), Luehrman (in his two 1998 articles in HBR and in a 1995 case in HBS) and Edleson (1994 case published in HBS) teach that the percentage value found in this table must be multiplied by the price of the asset, when in reality it should have been multiplied by the present value of the strike price. The most important results of this thesis for Finance are: (i) development of a robust and economic alternative method, based on the maximum-entropy principle of the Information Theory and on Pearson’s Distribution System, to the calculation of a unique uncertainty-price-neutral probability measure (risk-neutral probability), (ii) achievement of a practical formula to the calculation of the rational price of the simple ordinary European option on stocks, (iii) validation of the Black-Scholes-Merton formula on stocks, (iv) achievement of an adequate formula to the calculation of the rational price of the simple ordinary European option on bonds, (v) estimation of the implied entropic volatility of the price of an asset and (vi) definition and estimation of the entropic value-at-risk. There are still two important results to the Information Theory and to Economics: (i) a more precise distinction between uncertainty and risk and (ii) development of the forecast informational gain, an enhancement of the result of Theil (1967) and Benish (1999) by using the Kullback-Leibler divergence concept.

Ce mémoire vise à estimer la densité neutre au risque (Risk neutral density (RND) en anglais) par une approche non paramétrique tout en tenant compte de l’endogénéité. Les prix transversaux des options européennes sont utilisés pour l’estimation. Le modèle principal considéré est la régression linéaire fonctionnelle. Nous montrons comment utiliser des variables instrumentales dans ce modèle pour corriger l’endogénéité. En outre, nous avons intégré des variables instrumentales dans le modèle approximant le RND par l’utilisation des fonctions d’Hermite à des fins de comparaison des résultats. Pour garantir un estimateur stable, nous utilisons la technique de régularisation de Tikhonov. Ensuite, nous effectuons des simulations de Monte-Carlo pour étudier l’impact des différents types de distribution RND sur les résultats obtenus. Plus précisément, nous analysons une distribution de mélange lognormale et une distribution de smile de Black-Scholes. Les résultats des simulations démontrent que l’estimateur utilisant des variables instrumentales pour corriger l’endogénéité est plus performant que l’alternative qui ne les utilise pas. En outre, les résultats de la distribution de smile de Black-Scholes sont plus performants que ceux de la distribution de mélange log-normale. Enfin, S&P 500 options sont utilisées pour une application de l’estimateur. / This thesis aims to estimate the risk-neutral density (RND) through a non-parametric approach while accounting for endogeneity. The cross-sectional prices of European options are used for the estimation. The primary model under consideration is functional linear regression. We have demonstrated the use of instrumental variables in this model to address endogeneity. Additionally, we have integrated instrumental variables into the model approximating RND through the use of Hermite functions for the purpose of result comparison. To ensure a stable estimator, we employ the Tikhonov regularization technique. Following this, we conduct Monte- Carlo simulations to investigate the impact of different RND distribution types on the obtained results. Specifically, we analyze a lognormal mixture distribution and a Black-Scholes smile distribution. The simulation results demonstrate that the estimator utilizing instrumental variables to adjust for endogeneity outperforms the non-adjusted alternative. Additionally, outcomes from the Black-Scholes smile distribution exhibit superior performance compared to those from the log-normal mixture distribution. Finally, S&P 500 options are used for an application of the estimator.

Brigo與Mercurio提出了三種新的資產價格過程，分別是位移CEV過程、位移對數常態過程與混合對數常態過程。在這三種過程中，資產價格的波動度不再是一個固定的常數，而是時間與資產價格的明確函數。而由這三種過程所推導出來的歐式選擇權評價公式，將會導致隱含波動度曲線呈現傾斜曲線或是微笑曲線，且提供了參數讓我們能夠配適市場的波動度結構。本文利用台指買權來實證Brigo與Mercurio所提出的三種歐式選擇權評價公式，我們發現校準結果以混合對數常態過程優於位移CEV過程，而位移CEV過程則稍優於位移對數常態過程。因此，在實務校準時，我們建議以混合對數常態過程為台指買權的評價模型，以達到較佳的校準結果。 / Brigo and Mercurio proposed three types of asset-price dynamics which are shifted-CEV process, shifted-lognormal process and mixture-of-lognormals process respectively. In these three processes, the volatility of the asset price is no more a constant but a deterministic function of time and asset price. The European option pricing formulas derived from these three processes lead respectively to skew and smile in the term structure of implied volatilities. Also, the pricing formula provides several parameters for fitting the market volatility term structure. The thesis applies Taiwan’s call option to verifying these three pricing formulas proposed by Brigo and Mercurio. We find that the calibration result of mixture-of-lognormals process is better than the result of shifted-CEV process and the calibration result of shifted-CEV process is a little better than the result of shifted-lognormal process. Therefore, we recommend applying the pricing formula derived from mixture-of-lognormals process to getting a better calibration.

台灣特殊的兩黨對立政治環境及幾乎每年都會有的固定選舉，使得政治的不確定性深深的影響著國內的投資環境及投資人心態。本研究便是要探討，2002/1/1~2006/1/16 研究期間台灣的投資人在選舉前後的投資行為，是否真如大家所預期的，會受到台灣選舉事件的影響。 本研究首先利用適當的機率密度函數模型及選擇權市場資訊來導出隱含的風險中立密度值。再利用這些風險中立密度值，求出各個選舉事件相對應的機率分配圖形，並透過其機率分配圖形及波動率指數等統計值於投票日前後的變化來觀察某一選舉事件前後投資者的反應。 研究結果發現：1. 選舉事件的發生確實會影響投資者的心理，且投資者會透過選擇權市場有效率的反應預期的未來股價指數分佈情況。2. 越大型、越具爭議且全國性的選舉結果，其選舉期間機率分配圖形及波動率指數具有較高的波動性。3. 一般而言，選舉過後市場不確定因素降低，將使投資者對於股市的預期較為一致和樂觀。而若這個選舉結果使投資者感到意外，因而增加了市場的不確定性，則選後機率分配圖形及波動率指數的改變反而會更為明顯。4. 在此研究下對數常態混合法比傳統的 Black-Scholes 方法產生較低的誤差值，因此就實證的分析上能提供更好的配適。 / This research examines the behavior of investors during election periods from January 1st 2002 to January 6th 2006 in Taiwan. The research includes a few steps. First, we adopted a proper probability density function composed of stock index options data to construct the implied distribution. Then, when changing the whole shape of the risk-neutral implied distribution, the volatility indexes, and the statistics of the implied distribution, we observed investors' response around a specific election event. According to the empirical results, we found that: 1. An election event would influence investors’ behavior, and investors tend to reflect their expectation of future stock index in the option market in an efficient way. 2. The result of a large-scale and more disputed nationwide election will cause a higher fluctuation in both the implied distribution and the volatility index. 3. In general, the factor resulting from investors’ uncertainty of the market is likely to reduce after the election, which makes investors’ relatively unanimous and optimistic expectation of the stock market. However, if this election result surprises investors, their uncertainty of the market will increase, and thus the changes of the implied distribution and the volatility index become quite obvious. 4. The in-sample performance of the lognormal mixtures method employed in the research is considerably better than that of the traditional Black-Scholes model by having a lower root mean squared error.

Dans cette thèse, j’exploite le cadre d’analyse de données fonctionnelles et développe l’analyse d’inférence et de prédiction, avec une application à des sujets sur les marchés financiers. Cette thèse est organisée en trois chapitres. Le premier chapitre est un article co-écrit avec Marine Carrasco. Dans ce chapitre, nous considérons un modèle de régression linéaire fonctionnelle avec une variable prédictive fonctionnelle et une réponse scalaire. Nous effectuons une comparaison théorique des techniques d’analyse des composantes principales fonctionnelles (FPCA) et des moindres carrés partiels fonctionnels (FPLS). Nous déterminons la vitesse de convergence de l’erreur quadratique moyen d’estimation (MSE) pour ces méthodes. Aussi, nous montrons cette vitesse est sharp. Nous découvrons également que le biais de régularisation de la méthode FPLS est plus petit que celui de FPCA, tandis que son erreur d’estimation a tendance à être plus grande que celle de FPCA. De plus, nous montrons que le FPLS surpasse le FPCA en termes de prédiction avec moins de composantes. Le deuxième chapitre considère un modèle autorégressif entièrement fonctionnel (FAR) pour prèvoir toute la courbe de rendement du S&P 500 a la prochaine journée. Je mène une analyse comparative de quatre techniques de Big Data, dont la méthode de Tikhonov fonctionnelle (FT), la technique de Landweber-Fridman fonctionnelle (FLF), la coupure spectrale fonctionnelle (FSC) et les moindres carrés partiels fonctionnels (FPLS). La vitesse de convergence, la distribution asymptotique et une stratégie de test statistique pour sélectionner le nombre de retard sont fournis. Les simulations et les données réelles montrent que les méthode FPLS performe mieux les autres en terme d’estimation du paramètre tandis que toutes ces méthodes affichent des performances similaires en termes de prédiction. Le troisième chapitre propose d’estimer la densité de neutralité au risque (RND) dans le contexte de la tarification des options, à l’aide d’un modèle fonctionnel. L’avantage de cette approche est qu’elle exploite la théorie d’absence d’arbitrage et qu’il est possible d’éviter toute sorte de paramétrisation. L’estimation conduit à un problème d’inversibilité et la technique fonctionnelle de Landweber-Fridman (FLF) est utilisée pour le surmonter. / In this thesis, I exploit the functional data analysis framework and develop inference, prediction and forecasting analysis, with an application to topics in the financial market. This thesis is organized in three chapters. The first chapter is a paper co-authored with Marine Carrasco. In this chapter, we consider a functional linear regression model with a functional predictor variable and a scalar response. We develop a theoretical comparison of the Functional Principal Component Analysis (FPCA) and Functional Partial Least Squares (FPLS) techniques. We derive the convergence rate of the Mean Squared Error (MSE) for these methods. We show that this rate of convergence is sharp. We also find that the regularization bias of the FPLS method is smaller than the one of FPCA, while its estimation error tends to be larger than that of FPCA. Additionally, we show that FPLS outperforms FPCA in terms of prediction accuracy with a fewer number of components. The second chapter considers a fully functional autoregressive model (FAR) to forecast the next day’s return curve of the S&P 500. In contrast to the standard AR(1) model where each observation is a scalar, in this research each daily return curve is a collection of 390 points and is considered as one observation. I conduct a comparative analysis of four big data techniques including Functional Tikhonov method (FT), Functional Landweber-Fridman technique (FLF), Functional spectral-cut off (FSC), and Functional Partial Least Squares (FPLS). The convergence rate, asymptotic distribution, and a test-based strategy to select the lag number are provided. Simulations and real data show that FPLS method tends to outperform the other in terms of estimation accuracy while all the considered methods display almost the same predictive performance. The third chapter proposes to estimate the risk neutral density (RND) for options pricing with a functional linear model. The benefit of this approach is that it exploits directly the fundamental arbitrage-free equation and it is possible to avoid any additional density parametrization. The estimation problem leads to an inverse problem and the functional Landweber-Fridman (FLF) technique is used to overcome this issue.