Efficiently pricing multi-asset options is a challenging problem in computational finance. Although classical Fourier methods are extremely fast in pricing single asset options, maintaining the tractability of Fourier techniques for multi-asset option pricing is still an area of active research. Fourier methods rely on explicit knowledge of the characteristic function of the suitably stochastic price process, allowing for calculation of the option price by evaluation of multidimensional integral in the Fourier domain. The high smoothness of the integrand in the Fourier space motivates the exploration of deterministic quadrature methods that are highly efficient under certain regularity assumptions, such as, adaptive sparse grids quadrature (ASGQ), and Randomized Quasi-Monte Carlo (RQMC). However, when designing a numerical quadrature method for most of the existing Fourier pricing approaches, two key factors affecting the complexity should be carefully controlled, (i) the choice of the vector of damping parameters that ensure the Fourier-integrability and control the regularity class of the integrand, (ii) the high-dimensionality of the integration problem. To address these challenges, in the first part of this thesis we propose a rule for choosing the damping parameters, resulting in smoother integrands. Moreover, we explore the effect of sparsification and dimension-adaptivity in alleviating the curse of dimensionality. Despite the efficiency of ASGQ, the error estimates are very hard to compute. In cases where error quantification is of high priority, in the second part of this thesis, we design an RQMC-based method for the (inverse) Fourier integral computation. RQMC integration is known to be highly efficient for high-dimensional integration problems of sufficiently regular integrands, and it further allows for computation of probabilistic estimates. Nonetheless, using RQMC requires an appropriate domain transformation of the unbounded integration domain to the hypercube, which may originate in a transformed integrand with singularities at the boundaries, and consequently deteriorate the rate of convergence. To preserve the nice properties of the transformed integrand,we propose a model-dependent domain transformation to avoid these corner singularities and retain the optimal efficiency of RQMC. The effectiveness of the proposed optimal damping rule, the designed domain transformation procedure, and their combination with ASGQ and RQMC are demonstrated via several numerical experiments and computational comparisons to the MC approach and the COS method.

This thesis investigated Stochastic Runge–Kutta Lawson (SRKL) schemes and their application to the Heston model. Two distinct SRKL discretization methods were used to simulate a single asset’s dynamics under the Heston model, notably the Euler–Maruyama and Midpoint schemes. Additionally, standard Monte Carlo and variance reduction techniques were implemented. European and Asian option prices were estimated and compared with a benchmark value regarding accuracy, effectiveness, and computational complexity. Findings showed that the SRKL Euler–Maruyama schemes exhibited promise in enhancing the price for simple and path-dependent options. Consequently, integrating SRKL numerical methods into option valuation provides notable advantages by addressing challenges posed by the Heston model’s SDEs. Given the limited scope of this research topic, it is imperative to conduct further studies to understand the use of SRKL schemes within other models.

This document is a Master Thesis report in financial mathematics for KTH. This Master thesis is the product of an internship conducted at Nexialog Consulting, in Paris. This document is about the innovative use of Constrained Gaussian process regression in order to build an arbitrage free swaption cube. The methodology introduced in the document is used on a data set of European Swaptions Out of the Money. / Det här dokumentet är en magisteruppsats i finansiel matematik på KTH. Detta examensarbete är resultatet av en praktik som ufördes på Nexialog Consulting i Paris.Detta dokument handlar om den innovativa användningen av regression för gaussiska processer med bivillkor för att bygga en arbitragefri swaption kub. Den metodik som introduceras i dokumentet används på en datamängd av europeiska swaptions som är "Out of the Money".

In this thesis, we study the quadratic rough Heston model and the corresponding simulation methods. We calibrate the model using real-world market data. We compare and implement the three commonly used schemes (Hybrid, Multifactor, and Multifactor hybrid). We calibrate the model using real-world market SPX data. To speed up calibration, we apply quasi-Monte Carlo methods. We study the effect of the various calibration parameters on the volatility smile.

During the last few years, financial derivatives have been growing in trading volume. There seem to be a high demand and supply of derivatives on the market and one common derivative is the option contract. The option contract is frequently the subject of studies and many different pricing models have been created for options. One popular model is the Black-Scholes model, that prices European call options. This project will look closer at the Black-Scholes model and its assumption that volatility remains constant. The project will try to establish what predictive power the implied volatility has for the sign of the price changes in the option’s daily closing price. The implied volatility is defined as the value of volatility that can be used in an option pricing formula to yield the current market price and goes against the assumption of constant volatility. The model consists of a dependent variable that is the binary variable for the sign of the price changes, 1 if positive and 0 if negative. The independent variables are implied volatility, volume, price of the underlying, and VIX. The models used for testing are logistic regression, CART, random forest and XGBoost. The research question is: Can the sign of option price jumps be predicted with the implied volatility? The answer to the research question is that there are indications for the implied volatility having predictive power when predicting the sign of the price changes in the option, even though the results are not conclusive across all models. / Under de senaste åren har finansiella derivat ökat i handelsvolym. Det verkar finnas en hög efterfrågan och tillgång på derivat generellt på marknaden och ett vanligt sådant derivat är optionskontraktet. Optioner är ofta föremål för forskning och många olika prissättningsmodeller har skapats för optioner. En populär modell är Black-Scholes modellen som prissätter europeiska köpoptioner. Detta projekt kommer att titta närmare på Black-Scholes modellen och dess antagande om att volatiliteten förblir konstant. Projektet kommer att försöka fastställa vilken prediktiv kraft den implicita volatiliteten har för tecknet på prisförändringarna i optionens dagliga stängningskurs. Den implicita volatiliteten definieras som värdet av volatiliteten som kan användas i en optionsprissättningsformel för att ge det aktuella marknadspriset och går emot antagandet om konstant volatilitet. Modellen består av en beroende variabel som är en binär variabel för tecknet på prisförändringarna, 1 om positivt och 0 om negativt. De oberoende variablerna är implicit volatilitet, volym, pris på det underliggande instrumentet och VIX. Modellerna som används för att genomföra testen är logistisk regression, CART, random forest och XGBoost. Projektets frågeställning är: Kan tecknet på en options prisförändringar förutsägas med den implicita volatiliteten? Svaret som projektet kommit fram till är att det finns indikationer på att den implicita volatiliteten har prediktiv kraft när det gäller att förutsäga tecknet på prisförändringarna i optionen, även om resultaten inte är helt överensstämmande för alla modeller.

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.

In this project, we are aiming to solve option pricing and hedging problems numerically via Backward Stochastic Differential Equations (BSDEs). We use Markovian BSDEs to formulate nonlinear pricing and hedging problems of both European and American option types. This method of formulation is crucial for pricing financial instruments since it enables consideration of market imperfections and computations in high dimensions. We conduct numerical experiments of the pricing and hedging problems, where there is a higher interest rate for borrowing than lending, using the least squares Monte Carlo and deep neural network methods. Moreover, based on the experiment results, we point out which method to chooseover the other depending on the the problem at hand.

The purpose of this thesis is to develop an optimization model that chooses the optimal and price efficient combination of underlying assets for a equally weighted basket option. To obtain a price efficient combination of underlying assets a function that calculates the basket option price is needed, for further use in an optimization model. The closed-form basket option pricing is a great challenge, due to the lack of a distribution describing the augmented stochastic price process. Many types of approaches to price an basket option has been made. In this thesis, an analytical approximation of the basket option price has been used, where the analytical approximation aims to develop a method to describe the augmented price process. The approximation is done by moment matching, i.e. matching the first two moments of the real distribution of the basket option with an lognormal distribution. The obtained price function is adjusted and used as the objective function in the optimization model. Furthermore, since the goal is to obtain en equally weighted basket option, the appropriate class of optimization models to use are binary optimization problems. This kind of optimization model is in general hard to solve - especially for increasing dimensions. Three different continuous relaxations of the binary problem has been applied in order to obtain continuous problems, that are easier to solve. The results shows that the purpose of this thesis is fulfilled when formulating and solving the optimization problem - both as an binary and continuous nonlinear optimization model. Moreover, the results from a Monte Carlo simulation for correlated stochastic processes shows that the moment matching technique with a lognormal distribution is a good approximation for pricing a basket option. / Syftet med detta examensarbete är att utveckla ett optimeringsverktyg som väljer den optimala och priseffektiva kombinationen av underliggande tillgångar för en likaviktad aktiekorg. För att kunna hitta en priseffektiv kombination av underliggande tillgångar behöver man finna en passande funktion som bestämmer priset på en likaviktad aktiekorg. Prissättningen av dessa typer av optioner är en stor utmaning. Detta är på grund av bristen av en sannolikhetsfördelning som kan beskriva den utökade och korrelerade stokastiska prisprocess som uppstår för en aktiekorg. Många typer av prissättningar har undersökts och tillämpats. I detta arbete har en analytisk approximation använts för att kunna beskriva den underliggande pris processen approximativt. Uppskattningen görs genom att matcha de tvåförsta momenten av den verkliga fördelningen med motsvarande moment för en lognormal fördelning. Den erhållna prisfunktionen justeras och används som målfunktionen i optimeringsmodellen. Binära ickelinjära optimeringsproblem är i allmänhet svåra att lösa - särskilt för ökande dimensioner av variabler. Tre olika kontinuerliga omformuleringar av det binära optimeringsproblemet har gjorts för att erhålla kontinuerliga problem som är lättare att lösa. Resultaten visar att en optimal och priseffektiv kombination av underliggande aktier är möjlig att hitta genom att formulera ett optimeringsproblem - både som en binär och kontinuerlig ickelinjär optimeringsmodell. Dessutom visar resultaten från en Monte Carlo-simulering, i detta fall för korrelerade stokastiska processer, att moment matching metoden utförd med en lognormal fördelning är en god approximation för prissättningen av aktiekorgar.

The main goal of this dissertation was to obtain an understanding as to the true economic nature of employee share options and the problems surrounding the accounting thereof. The main conclusion of this study is that employee share options should be expensed in the income statement as and when the employee's services are performed. The reason is that employee share options are valuable financial instruments which the employer has used to compensate the employee for his services. It was also concluded that exercise date accounting and classification of outstanding employee share options as liabilities on the balance sheet is the most appropriate accounting treatment. Such accounting treatment trues up the accounting of employee share options with that of cash-settled share appreciation rights, which are economically equivalent transactions. The measurement of employee share options should be based on their fair value using an option-pricing model adapted for the specific features of employee share options. / Accounting / Thesis (M. Com. (Accounting Science))

本研究利用GARCH動態過程的優點捕捉匯率報酬率之異質變異與波動度叢聚性質，並以GARCH動態過程為基礎，考慮跳躍風險服從Lévy過程，再利用特徵函數與快速傅立葉轉換方法推導出GARCH-Lévy動態過程下的歐式匯率選擇權解析解。以日圓兌換美元(JPY/USD)之歐式匯率選擇權為實證資料，比較基準GARCH選擇權評價模型與GARCH-Lévy選擇權評價模型對市場真實價格的配適效果與預測能力。實證結果顯示，考慮跳躍風險為無限活躍之Lévy過程，即GARCH-VG與GARCH-NIG匯率選擇權評價模型，不論是樣本內的評價誤差或是在樣本外的避險誤差皆勝於考慮跳躍風險為有限活躍Lévy過程的GARCH-MJ匯率選擇權評價模型。整體而言，本研究發現進行匯率選擇權之評價時，GARCH-NIG匯率選擇權評價模型有較小的樣本內及樣本外評價誤差。 / In this thesis, we make use of GARCH dynamic to capture volatility clustering and heteroskedasticity in exchange rate. We consider a jump risk which follows Lévy process based on GARCH model. Furthermore, we use characteristic function and fast fourier transform to derive the currency option pricing formula under GARCH-Lévy process. We collect the JPY/USD exchange rate data for our empirical analysis and then compare the goodness of fit and prediction performance between GARCH benchmark and GARCH-Lévy currency option pricing model. The empirical results show that either in-sample pricing error or out-of-sample hedging performance, the infinite-activity Lévy process, GARCH-VG and GARCH-NIG option pricing model is better than finite-activity Lévy process, GARCH-MJ option pricing model. Overall, we find using GARCH-NIG currency option pricing model can achieve the lower in-sample and out-of sample pricing error.