This thesis proposes an optimization formulation to ensure accuracy and stability in the local volatility function calibration. The unknown local volatility function is represented by kernel splines. The proposed optimization formulation minimizes calibration error and an L1 norm of the vector of coefficients for the kernel splines. The L1 norm regularization forces some coefficients to be zero at the termination of optimization. The complexity of local volatility function model is determined by the number of nonzero coefficients. Thus by using a regularization parameter, the proposed formulation balances the calibration accuracy with the model complexity. In the context of the support vector regression for function based on finite observations, this corresponds to balance the generalization error with the number of support vectors. In this thesis we also propose a trust region method to determine the coefficient vector in the proposed optimization formulation. In this algorithm, the main computation of each iteration is reduced to solving a standard trust region subproblem. To deal with the non-differentiable L1 norm in the formulation, a line search technique which allows crossing nondifferentiable hyperplanes is introduced to find the minimum objective value along a direction within a trust region. With the trust region algorithm, we numerically illustrate the ability of proposed approach to reconstruct the local volatility in a synthetic local volatility market. Based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is smooth and resembles in shape the observed implied volatility surface. Stability is illustrated by considering calibration using market option data from nearby dates.

This thesis proposes an optimization formulation to ensure accuracy and stability in the local volatility function calibration. The unknown local volatility function is represented by kernel splines. The proposed optimization formulation minimizes calibration error and an L1 norm of the vector of coefficients for the kernel splines. The L1 norm regularization forces some coefficients to be zero at the termination of optimization. The complexity of local volatility function model is determined by the number of nonzero coefficients. Thus by using a regularization parameter, the proposed formulation balances the calibration accuracy with the model complexity. In the context of the support vector regression for function based on finite observations, this corresponds to balance the generalization error with the number of support vectors. In this thesis we also propose a trust region method to determine the coefficient vector in the proposed optimization formulation. In this algorithm, the main computation of each iteration is reduced to solving a standard trust region subproblem. To deal with the non-differentiable L1 norm in the formulation, a line search technique which allows crossing nondifferentiable hyperplanes is introduced to find the minimum objective value along a direction within a trust region. With the trust region algorithm, we numerically illustrate the ability of proposed approach to reconstruct the local volatility in a synthetic local volatility market. Based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is smooth and resembles in shape the observed implied volatility surface. Stability is illustrated by considering calibration using market option data from nearby dates.

Under a class of one dimensional local volatility models, this thesis establishes closed form small time asymptotic formulae for the gradient of the implied volatility, whether or not the options are at the money, and for the at the money Hessian of the implied volatility. Along the way it also partially verifies the statement by Berestycki, Busca and Florent (2004) that the implied volatility admits higher order Taylor series expansions in time near expiry. Both as a prelude to the presentation of these main results and as a highlight of the importance of the no arbitrage condition, this thesis shows in its beginning a Cox-Ingersoll-Ross type stock model where an equivalent martingale measure does not always exist.

A parameter used in the Black-Scholes equation, volatility, is a measure for variation of the price of a financial instrument over time. Determining volatility is a fundamental issue in the valuation of financial instruments. This gives rise to an inverse problem known as the calibration problem for option pricing. This problem is shown to be ill-posed. We propose a regularization method and reformulate our calibration problem as a problem of finding the local volatility in a reproducing kernel Hilbert space. We defined a new volatility function which allows us to embrace both the financial and time factors of the options. We discuss the existence of the minimizer by using regu- larized reproducing kernel method and show that the regularizer resolves the numerical instability of the calibration problem. Finally, we apply our studied method to data sets of index options by simulation tests and discuss the empirical results obtained.

This dissertation is centered around two comprehensive themes: the extraction of information embedded in equity index option prices, and how to use this information in order to be able to make optimal decisions in the equity index option markets. These problems are important for decision makers in the equity index options markets, since they are continuously faced with making decisions under uncertainty given observed market prices. The methods developed in this dissertation provide robust tools that can be used by practitioners in order to improve the quality of the decisions that they make. In order to be able to extract information embedded in option prices, the dissertation develops two different methods for estimation of stable option implied surfaces which are consistent with observed market prices. This is a difficult and ill-posed inverse problem which is complicated by the fact that observed option prices contain a large amount of noise stemming from market micro structure effects. Producing estimated surfaces that are stable over time is important since otherwise risk measurement of derivatives portfolios, pricing of exotic options and calculation of hedge parameters will be prone to include significant errors. The first method that we develop leads to an optimization problem which is formulated as a convex quadratic program with linear constraints which can be solved very efficiently. The second estimation method that we develop in the dissertation makes it possible to produce local volatility surfaces of high quality, which are consistent with market prices and stable over time. The high quality of the surfaces estimated with the second method is the crucial input to the research which has resulted in the last three papers of the dissertation. The stability of the estimated local volatility surfaces makes it possible to build a realistic dynamic model for the equity index derivatives market. This model forms the basis for the stochastic programming (SP) model for option hedging that we develop in the dissertation. We show that the SP model, which uses generated scenarios for the squared local volatility surface as input,  outperforms the traditional hedging methods that are described in the literature. Apart from having an accurate view of the variance of relevant risk factors, it is when building a dynamic model also important to have a good estimate of the expected values, and thereby risk premia, of those factors. We use a result from recently published research which lets us recover the real-world density from only a cross-section of observed option prices via a local volatility model. The recovered real-world densities are then used in order to identify and estimate liquidity premia that are embedded in option prices. We also use the recovered real-world densities in order to test how well the option market predicts the realized statistical characteristics of the underlying index. We compare the results with the performance of commonly used models for the underlying index. The results show that option prices contain a premium in the tails of the distribution. By removing the estimated premia from the tails, the resulting density predicts future realizations of the underlying index very well.

In this master thesis we have examined the possibility of pricing multiple American options, on an underlying asset with discrete dividends, with a finite difference method. We have found a good and stable way to price one American option by solving the BSM PDE backwards, while also calculating the Greeks of the option with automatic differentiation. The list of Greeks for an option is quite extensive since we have been using a local volatility surface. We have also tried to find a way to price several American options simultaneously by solving a forward PDE. Unfortunately, we haven't found any previous work that we could use with our local volatility surface, while still keeping down the computational time. The closest we got was to calculate the value of a compound option in a forward mode, but in order to use this to value an American option, we needed to go through an iterative process which calculated a forward or backward European PDE in every step.

In this thesis we develop and test a new method for interpolating and extrapolating prices of European options. The theoretical base originates from the local variance gamma model developed by Carr (2008), in which the local volatility model by Dupire (1994) is combined with the variance gamma model by Madan and Seneta (1990). By solving a simplied version of the Dupire equation under the assumption of a continuous ve parameter di usion term, we derive a parameterization dened for strikes in an interval of arbitrary size. The parameterization produces positive option prices which satisfy both conditions for absence of arbitrage in a one maturity setting, i.e. all adjacent vertical spreads and buttery spreads are priced non-negatively. The method is implemented and tested in the FX-option market. We suggest two sub-models, one with three and one with ve degrees of freedom. By using a least-square approach, we calibrate the two sub-models against 416 Reuters quoted volatility smiles. Both sub-models succeeds in generating prices within the bid-ask spread for all options in the sample. Compared to the three parameter model, the model with ve parameters calibrates more exactly to market quoted mids but has a longer calibration time. The three parameter model calibrates remarkably quickly; in a MATLAB implementation using a Levenberg-Marquardt algorithm the average calibration time is approximately 1 ms. Both sub-models produce volatility smiles which are C2 and well-behaving. Further, we suggest a technique allowing for arbitrage-free interpolation of calibrated option price functions in the maturity dimension. The interpolation is performed in parameter space, where every set of parameters uniquely determines an option price function. Furthermore, we produce sucient conditions to ensure absence of calendar spread arbitrage when calibrating the proposed model to several maturities. We use this technique to produce implied volatility surfaces which are suciently smooth, satisfy all conditions for absence of arbitrage and fit market quoted volatility surfaces within the bid-ask spread. In the final chapter we use the results for producing Dupire local volatility surfaces and for pricing variance swaps.

This thesis treats aspects of two fundamental problems in applied financial mathematics: calibration of a given stochastic process to observed marketprices on financial instruments (which is the topic of the first paper) and strategies for hedging options in financial markets that are possibly incomplete (which is the topic of the second paper). Calibration in finance means choosing the parameters in a stochastic process so as to make the prices on financial instruments generated by the process replicate observed market prices. We deal with the so called local volatility model which is one of the most widely used models in option pricing across all asset classes. The calibration of a local volatility surface to option marketprices is an ill-posed inverse problem as a result of the relatively small number of observable market prices and the unsmooth nature of these prices in strike and maturity. We adopt the practice advanced by some authors to formulate this inverse problem as a least squares optimization under the constraint that option prices follow Dupire’s partial differential equation. We develop two algorithms for performing the optimization: one based on techniques from optimal control theory and another in which a numerical quasi-Newton algorithmis directly applied to the objective function. Regularization of the problem enters easily in both problem formulations. The methods are tested on three months of daily option market quotes on two major equity indices.The resulting local volatility surfaces from both methods yield excellent replications of the observed market prices. Hedging is the practice of offsetting the risk in a financial instrument by taking positions in one or several other tradable assets. Quadratic hedging is a well developed theory for hedging contingent claims in incomplete markets by minimizing the replication error in a suitable L2-norm. This theory, though, is not widely used among market practitioners and relatively few scientific papers evaluate how well quadratic hedging works on real marketdata. We construct a framework for comparing hedging strategies, and use it to empirically test the performance of quadratic hedging of European call options on the Euro Stoxx 50 index modeled with an affine stochastic volatility model with and without jumps. As comparison, we use hedging in the standard Black-Scholes model. We show that quadratic hedging strategies significantly outperform hedging in the Black-Scholes model for out of the money options and options near the money of short maturity when only spot is used in the hedge. When in addition another option is used for hedging, quadratic hedging outperforms Black-Scholes hedging also for medium dated options near the money. / Den här avhandlingen behandlar aspekter av två fundamentala problem i tillämpad finansiell matematik: kalibrering av en given stokastisk process till observerade marknadspriser på finansiella instrument (vilket är ämnet för den första artikeln) och strategier för hedging av optioner i finansiella marknader som är inkompletta (vilket är ämnet för den andra artikeln). Kalibrering i finans innebär att välja parametrarna i en stokastisk process så att de priser på finansiella instrument som processen genererar replikerar observerade marknadspriser. Vi behandlar den så kallade lokala volatilitets modellen som är en av de mest utbrett använda modellerna inom options prissättning för alla tillgångsklasser. Kalibrering av en lokal volatilitetsyta till marknadspriser på optioner är ett illa ställt inverst problem som en följd av att antalet observerbara marknadspriser är relativt litet och att priserna inte är släta i lösenpris och löptid. Liksom i vissa tidigare publikationer formulerar vi detta inversa problem som en minsta kvadratoptimering under bivillkoret att optionspriser följer Dupires partiella differentialekvation. Vi utvecklar två algoritmer för att utföra optimeringen: en baserad på tekniker från optimal kontrollteori och en annan där en numerisk kvasi-Newton metod direkt appliceras på målfunktionen. Regularisering av problemet kan enkelt införlivas i båda problemformuleringarna. Metoderna testas på tre månaders data med marknadspriser på optioner på två stora aktieindex. De resulterade lokala volatilitetsytorna från båda metoderna ger priser som överensstämmer mycket väl med observerade marknadspriser. Hedging inom finans innebär att uppväga risken i ett finansiellt instrument genom att ta positioner i en eller flera andra handlade tillgångar. Kvadratisk hedging är en väl utvecklad teori för hedging av betingade kontrakt i inkompletta marknader genom att minimera replikeringsfelet i en passande L2-norm. Denna teori används emellertid inte i någon högre utsträckning av marknadsaktörer och relativt få vetenskapliga artiklar utvärderar hur väl kvadratisk hedging fungerar på verklig marknadsdata. Vi utvecklar ett ramverk för att jämföra hedgingstrategier och använder det för att empiriskt pröva hur väl kvadratisk hedging fungerar för europeiska köpoptioner på aktieindexet Euro Stoxx 50 när det modelleras med en affin stokastisk volatilitetsmodell med och utan hopp. Som jämförelse använder vi hedging i Black-Scholes modell.Vi visar att kvadratiska hedgingstrategier är signifikant bättre än hedging i Black-Scholes modell för optioner utanför pengarna och optioner nära pengarna med kort löptid när endast spot används i hedgen. När en annan option används i hedgen utöver spot är kvadratiska hedgingstrategier bättre än hedging i Black-Scholes modell även för optioner nära pengarna medmedellång löptid. / <p>QC 20141121</p>

Theaim of this master-thesis is to study the impact of uncertainty in the local-and implied volatility surfaces when pricing certain structured products suchas capital protected notes and autocalls. Due to their long maturities, limitedavailability of data and liquidity issue, the uncertainty may have a crucialimpact on the choice of valuation model. The degree of sensitivity andreliability of two different valuation models are studied. The valuation models chosen for this thesis are the local volatility model of Dupire and the implied volatility model of Black&amp;Scholes. The two models are stress tested with varying volatilities within an uncertainty interval chosen to be the volatilities obtained from Bid and Ask market prices. The volatility surface of the Mid market prices is set as the relative reference and then successively scaled up and down to measure the uncertainty.The results indicates that the uncertainty in the chosen interval for theDupire model is of higher order than in the Black&amp;Scholes model, i.e. thelocal volatility model is more sensitive to volatility changes. Also, the pricederived in the Black&amp;Scholes modelis closer to the market price of the issued CPN and the Dupire price is closer tothe issued Autocall. This might be an indication of uncertainty in thecalibration method, the size of the chosen uncertainty interval or the constantextrapolation assumption.A further notice is that the prices derived from the Black&amp;Scholes model areoverall higher than the prices from the Dupire model. Another observation ofinterest is that the uncertainty between the models is significantly greaterthan within each model itself. / Syftet med dettaexamensarbete är att studera inverkan av osäkerhet, i prissättningen av struktureradeprodukter, som uppkommer på grund av förändringar i volatilitetsytan. I dennastudie värderas olika slags autocall- och kapitalskyddade struktureradeprodukter. Strukturerade produkter har typiskt långa löptider vilket medförosäkerhet i värderingen då mängden data är begränsad och man behöver ta tillextrapolations metoder för att komplettera. En annan faktor som avgörstorleksordningen på osäkerheten är illikviditeten, vilken mäts som spreadenmellan listade Bid och Ask priset. Dessa orsaker ligger bakom intresset attstudera osäkerheten för långa löptider över alla lösenpriser och dess inverkanpå två olika värderingsmodeller.Värderingsmodellerna som används i denna studie är Dupires lokala volatilitetsmodell samt Black&amp;Scholes implicita volatilitets modell. Dessa ställs motvarandra i en jämförelse gällande stabilitet och förmåga att fånga uppvolatilitets ändringar. Man utgår från Mid volatilitetsytan som referens ochuppmäter prisändringar i intervallet från Bid upp till Ask volatilitetsytornagenom att skala Mid ytan. Resultaten indikerar på större prisskillnader inom Dupires modell i jämförelsemot Black&amp;Scholes. Detta kan tolkas som att Dupires modell är mer känslig isammanhanget och har en starkare förmåga att fånga upp förändringar isvansarna. Vidare notering är att priserna beräknade i Dupire är relativtbilligare än motsvarande från Black&amp;Scholes modellen. En ytterligareobservation är att osäkerheten mellan värderingsmodellerna är av högre ordningän inom var modell för sig. Ett annat resultat visar att CPN priset beräknat iBlack&amp;Scholes modell ligger närmast marknadspriset medans marknadsprisetför Autocallen ligger närmare Dupires. Detta kan vara en indikation påosäkerheten i kalibreringsmetoden eventuellt det valda osäkerhetsintervalletoch konstanta extrapolations antagandet.

The master thesis is focused on how a local volatility surfaces can be extracted by optimization with respectto smoothness and price error. The pricing is based on utility based pricing, and developed to be set in arisk neutral pricing setting. The pricing is done in a discrete multinomial recombining tree, where the timeand price increments optionally can be equidistant. An interpolation algorithm is used if the option that shallbe priced is not matched in the tree discretization. Power utility functions are utilized, where the log-utilitypreference is especially studied, which coincides with the (Kelly) portfolio that systematically outperforms anyother portfolio. A fine resolution of the discretization is generally a property that is sought after, thus a seriesof derivations for the implementation are done to restrict the computational encumbrance and thus allow finer discretization. The thesis is mainly focused on the derivation of the method rather than finding optimal parameters thatgenerate the local volatility surfaces. The method has shown that smooth surfaces can be extracted, whichconsider market prices. However, due to lacking available interest and dividend data, the pricing error increasessymmetrically for longer option maturities. However, the method shows exponential convergence and robustnessto different initial (flat) volatilities for the optimization initiation. Given an optimal smooth local volatility surface, an arbitrary payoff function can then be used to price thecorresponding option, which could be path-dependent, such as barrier options. However, only vanilla optionswill be considered in this thesis. Finally, we find that the developed