Submitted by Cristina Tessari (tinatessari@gmail.com) on 2016-06-09T13:51:42Z No. of bitstreams: 1 DissertationEPGE_CristinaTessari2016.pdf: 1264081 bytes, checksum: 14e65157457bfe8deea5353bb192a0af (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2016-06-29T14:03:25Z (GMT) No. of bitstreams: 1 DissertationEPGE_CristinaTessari2016.pdf: 1264081 bytes, checksum: 14e65157457bfe8deea5353bb192a0af (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2016-06-29T14:06:59Z (GMT) No. of bitstreams: 1 DissertationEPGE_CristinaTessari2016.pdf: 1264081 bytes, checksum: 14e65157457bfe8deea5353bb192a0af (MD5) / Made available in DSpace on 2016-06-29T14:07:23Z (GMT). No. of bitstreams: 1 DissertationEPGE_CristinaTessari2016.pdf: 1264081 bytes, checksum: 14e65157457bfe8deea5353bb192a0af (MD5) Previous issue date: 2016-03-22 / We use Brazilian data to compute monthly idiosyncratic moments (expected skewness, realized skewness, and realized volatility) for equity returns and assess whether they are informative for the cross-section of future stock returns. Since there is evidence that lagged skewness alone does not adequately forecast skewness, we estimate a cross-sectional model of expected skewness that uses additional predictive variables. Then, we sort stocks each month according to their idiosyncratic moments, forming quintile portfolios. We find a negative relationship between higher idiosyncratic moments and next-month stock returns. The trading strategy that sells stocks in the top quintile of expected skewness and buys stocks in the bottom quintile generates a significant monthly return of about 120 basis points. Our results are robust across sample periods, portfolio weightings, and to Fama and French (1993)’s risk adjustment factors. Finally, we identify a return reversal of stocks with high idiosyncratic skewness. Specifically, stocks with high idiosyncratic skewness have high contemporaneous returns. That tends to reverse, resulting in negative abnormal returns in the following month. / In the first chapter, we test some stochastic volatility models using options on the S&P 500 index. First, we demonstrate the presence of a short time-scale, on the order of days, and a long time-scale, on the order of months, in the S&P 500 volatility process using the empirical structure function, or variogram. This result is consistent with findings of previous studies. The main contribution of our paper is to estimate the two time-scales in the volatility process simultaneously by using nonlinear weighted least-squares technique. To test the statistical significance of the rates of mean-reversion, we bootstrap pairs of residuals using the circular block bootstrap of Politis and Romano (1992). We choose the block-length according to the automatic procedure of Politis and White (2004). After that, we calculate a first-order correction to the Black-Scholes prices using three different first-order corrections: (i) a fast time scale correction; (ii) a slow time scale correction; and (iii) a multiscale (fast and slow) correction. To test the ability of our model to price options, we simulate options prices using five different specifications for the rates or mean-reversion. We did not find any evidence that these asymptotic models perform better, in terms of RMSE, than the Black-Scholes model. In the second chapter, we use Brazilian data to compute monthly idiosyncratic moments (expected skewness, realized skewness, and realized volatility) for equity returns and assess whether they are informative for the cross-section of future stock returns. Since there is evidence that lagged skewness alone does not adequately forecast skewness, we estimate a cross-sectional model of expected skewness that uses additional predictive variables. Then, we sort stocks each month according to their idiosyncratic moments, forming quintile portfolios. We find a negative relationship between higher idiosyncratic moments and next-month stock returns. The trading strategy that sells stocks in the top quintile of expected skewness and buys stocks in the bottom quintile generates a significant monthly return of about 120 basis points. Our results are robust across sample periods, portfolio weightings, and to Fama and French (1993)’s risk adjustment factors. Finally, we identify a return reversal of stocks with high idiosyncratic skewness. Specifically, stocks with high idiosyncratic skewness have high contemporaneous returns. That tends to reverse, resulting in negative abnormal returns in the following month.

O estudo apresenta um modelo de precificação de derivativos financeiros baseado em processos de mistura de movimentos brownianos. A partir de uma modelagem probabilística, são apresentados ajustes ao modelo tradicional de Black-Scholes-Merton para contemplar situações em que o retorno do ativo-objeto não segue uma distribuição normal. O trabalho discute ainda um mecanismo de estimação de parâmetros da mistura de normais. O resultado da pesquisa possibilita a análise de preço de opções em situações mais gerais. / The study presents a model for pricing financial derivatives based on a mixture of Brownian motion processes. From a probabilistic modeling, the research focuses on adjustments to the traditional Black- Scholes- Merton model to address situations where the return of the underlying asset does not follow a normal distribution. The paper also discusses a mechanism to estimate parameters of a mixture of normal distributions. The result of the study allows an analysis of option price in more general situations.

O valor extrínseco é uma das variáveis que mais carregam informações sobre o valor de uma opção; características como a probabilidade de exercício e a trajetória até o vencimento dizem muito sobre como o mercado está influenciando pelo menos a ação-objeto. Como o valor extrínseco é uma variável bastante sensível, é importante que sua mensuração seja a mais detalhada o possível. Este trabalho buscou captar a geração instantânea de valor intrínseco avaliando as cotações \"tick by tick\" das ações e opções de compra da VALE5, PETR4, OGXP3, BVMF3 e ITUB4 durante os anos de 2011 e 2012. Foi possível demonstrar que mesmo uma mínima diferença de valor extrínseco captado possibilitou uma maior capacidade de explicação em diferentes modelos testados, comparando com a captação de final de dia (EOD). / The intrinsic value is one of the variables that most carry information on the value of a stock option. Characteristics such as exercise probability and path to maturity summarize how the market is working at least for the stock. As extrinsic value is very sensitive, it is important that its measurements happen in the most possible detailed way. This study focus on capture the instantaneous extrinsic value generation, analyzing \"tick by tick\" quotes of VALE5, PETR4, OGXP3, BVMF3 e ITUB4 stocks and related call options in the years of 2011 and 2012. It was possible to demonstrate that even a minimal difference of extrinsic value captured resulted in a better explanation capacity based on different tested models, in comparison with the extrinsic value captured by end of day data.

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 present thesis addresses both theoretical as well as numerical aspects of the treatment of nonlinear inverse problems. The first part considers Tikhonov regularization for nonlinear ill-posed operator equations in Hilbert scales with oversmoothing penalties. Sufficient as well as necessary conditions to establish convergence are introduced and convergence rate results are given for various parameter choice rules under a two sided nonlinearity constraint. Ultimately, both a posteriori as well as certain a priori parameter choice rules lead to identical converce rates. The theoretical results are supported and augmented by extensive numerical case studies. In particular it is shown, that the localization of the above mentioned nonlinearity constraint is not trivial. Incorrect localization will prevent convergence of the regularized to the exact solution. The second part of the thesis considers two open problems in inverse option pricing and electrical impedance tomography. While regularization through discretization is sufficient to overcome ill-posedness of the latter, the first requires a more sophisticated approach. It is shown, that the recovery of time dependent volatility and interest rate functions from observed option prices is everywhere locally ill-posed. This motivates Tikhonov-type or variational regularization with two parameters and penalty terms to simultaneously recover these functions. Two parameter choice rules using the L-hypersurface as well as a combination of L-curve and quasi-optimality are introduced. The results are again supported by extensive numerical case studies.

Financial engineering problems are of great importance in the academic community and BlackScholes equation is a revolutionary concept in the modern financial theory. Financial instruments such as stocks and derivatives can be evaluated using this model. Option evaluation, is extremely important to trade in the stocks. The numerical solutions of the Black-Scholes equation are used to simulate these options. In this thesis, the explicit and the implicit Euler methods are used for the approximation of Black-scholes partial differential equation and a second order finite difference scheme is used for the spatial derivatives. These temporal and spatial discretizations are used to gain an insight about the stability properties of the explicit and the implicit methods in general. The numerical results show that the explicit methods have some constraints on the stability, whereas, the implicit Euler method is unconditionally stable. It is also demostrated that both the explicit and the implicit Euler methods are only first order convergent in time and this implies too small step-sizes to achieve a good accuracy.

The fast Fourier transform (FFT), even though it has been widely applicable in Physics and Engineering, it has become attractive in Finance as well for it’s enhancement of computational speed. Carr and Madan succeeded in implementing the FFT for pricing of an option. This project, inspired by Carr and Madan’s paper, attempts to elaborate and connect the various mathematical and theoretical concepts that are helpful in understanding of the derivation. Further, we derive the characteristic function of the risk neutral probability for the logarithmic terminal stock price. The Black-Scholes-Merton (BSM) model is also revised including derivation of the partial deferential equation and the formula. Finally, comparison of the BSM numerical implementation with and without the FFT method is done using MATLAB.

We deal with locally ill-posed nonlinear operator equations F(x) = y in L^2(0,1), where the Fréchet derivatives A = F'(x_0) of the nonlinear forward operator F are compact linear integral operators A = M ◦ J with a multiplication operator M with integrable multiplier function m and with the simple integration operator J. In particular, we give examples of nonlinear inverse problems in natural sciences and stochastic finance that can be written in such a form with linearizations that contain multiplication operators. Moreover, we consider the corresponding ill-posed linear operator equations Ax = y and their degree of ill-posedness. In particular, we discuss the fact that the noncompact multiplication operator M has only a restricted influence on this degree of ill-posedness even if m has essential zeros of various order.

Derivatives have a large and significant role on the financial markets today and the popularity of options has increased. This has also increased the demand of finding a suitable option pricing model, since the ground-breaking model developed by Black & Scholes (1973) have poor pricing performance. Practitioners and academics have over the years developed different models with the assumption of non-constant volatility, without reaching any conclusions regarding which model is more suitable to use. This thesis examines four different models, the first model is the Practitioners Black & Scholes model proposed by Christoffersen & Jacobs (2004b). The second model is the Heston´s (1993) continuous time stochastic volatility model, a modification of the model is also included, which is called the Strike Vector Computation suggested by Kilin (2011). The last model is the Heston & Nandi (2000) Generalized Autoregressive Conditional Heteroscedasticity type discrete model. From a practical point of view the models are evaluated, with the goal of finding the model with the best pricing performance and the most practical usage. The model´s robustness is also tested to see how the models perform in out-of-sample during a high respectively low implied volatility market. All the models are effected in the robustness test, the out-sample ability is negatively affected by a high implied volatility market. The results show that both of the stochastic volatility models have superior performances in the in-sample and out-sample analysis. The Generalized Autoregressive Conditional Heteroscedasticity type discrete model shows surprisingly poor results both in the in-sample and out-sample analysis. The results indicate that option data should be used instead of historical return data to estimate the model’s parameters. This thesis also provides an insight on why overnight-index-swap (OIS) rates should be used instead of LIBOR rates as a proxy for the risk-free rate.

According to production process theory, customization is directly related to cost and inversely related to volume, efficiency, and productivity. However, customers generally desire products that are individually tailored to their wants and needs. For this reason, as residential contractors grow, they struggle to meet customers' demands for flexibility. This struggle to increase customization is not unique to the construction industry and many other industries have studied this problem in depth. While the inverse relationship between customization and cost is generally true, mass customization can enable increased customization with limited or no increased cost. The residential construction process employs many mass customization enabling principles, including modularity and product family design. However, the preconstruction process fails to employ these same principles. The purpose of this study was to explore how mass customization principles can simplify customization in the residential preconstruction process. Two rounds of interviews were conducted with residential construction industry preconstruction experts. Using their input, a process for implementing mass customization was developed. The results demonstrate that implementing mass customization principles can greatly simplify the purchasing, estimating, and option pricing processes for residential contractors. However, mass customization also significantly affects company structure, cost control strategies, trade relationships, and leanness. This research is enlightening to residential contractors struggling to manage customization. It also provides direction for software developers targeting the residential construction processes.