Many phenomena in our world can be described as differential equations in high dimensions. However, they are notoriously challenging to solve numerically due to the exponential growth in computational cost with increasing dimensions. This thesis explores an algorithm, known as deep BSDE, for solving high-dimensional partial differential equations and applies it to finance, namely European option pricing. In addition, an implementation of the method is provided that seemingly shortens the runtime by a factor of two, compared with the results in previous studies. From the results, we can conclude that the deep BSDE method does handle high-dimensional problems well. Lastly, the thesis gives the relevant prerequisites required to be able to digest the theory from an undergraduate level.

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.

The growth of private equity, as well as consolidation trends across other industries, have produced a strong and vibrant mergers and acquisitions market. A challenge during these acquisitions is information asymmetry, which makes agreeing on the transaction price a challenge. An increasingly popular instrument to get around this problem is to use earnout contracts, which puts the difference between what the buyer is willing to pay and what the seller is willing to accept as contingent on future performance of the company. This thesis focuses on testing four different models for estimating the expected pay-out of earnout contracts. The investigated models were geometric Brownian motion, autoregressive integrated moving average, artificial neural network and a hybrid model to forecast the underlying metrics which were used with Monte Carlo methods to compute the expected pay-out of the earnout contract. Furthermore, a bankruptcy adjusted and a model using implied market volatility were evaluated. The results were that the hybrid model showed the most promising predictions when estimating the expected pay-out. The bankruptcy adjustment was not successful since the model failed to reach sufficient accuracy. Using implied market volatility showed inconclusive results. / Tillväxten för riskkapital-industrin och konsolideringstrender inom andra industrier har resulterat i en aktiv marknad för bolagsförvärv. En tydlig utmaning under ett förvärv är informationsasymmetri, vilket gör det svårt att komma överens om bolagets värdering. En alltmer vanlig metod för att lösa detta problem är att använda en tilläggsköpeskilling. Ett sådant kontrakt placerar skillnaden mellan vad köparen är villig att betala och vad säljaren är villig att acceptera som en option baserad på bolagets framtida prestation. Detta examensarbete fokuserade på att testa fyra olika modeller för att skatta den framtida utbetalningen från tilläggsköpeskillingar. De utvärderade modellerna var baserade på geometrisk brownsk rörelse, autoregressive integrated moving average, artificiellt neuralt nätverk och en hybridmodell vilka användes för att generera prediktioner för optionernas underliggande mått. Dessa användes sedan för att med hjälp av Monte Carlo simulering skatta den förväntade utbetalningen från tilläggsköpeskillingen. Utöver detta testades en modell med justering av konkursrisk samt en modell baserad på implicerad volatilitet från börsnoterade optioner. Resultaten visade att hybridmodellen gav bäst prediktioner av den förväntade utbetalningen. Den konkursjusterade modellen påvisade inga signifikanta resultat då den ej nådde tillräckligt hög prediktionsförmåga. Användningen av implicerad marknadsvolatilitet gav ingen tydlig och statistiskt signifikant förbättring.

The study investigates whether the main capital market theories and pricing models provide a reasonably accurate description of the working and efficiency of capital markets, of the pricing of shares and options and the effect the risk/return relationship has on investor behaviour. The capital market theories and pricing models included in the study are Portfolio Theory, the Efficient Market Hypothesis (EMH), the Capital Asset Pricing Model (CAPM), the Arbitrage Pricing Theory (APT), Options Theory and the BlackScholes (8-S) Option Pricing Model. The main conclusion of the study is that the main capital market theories and pricing models, as reviewed in the study, do provide a reasonably accurate description of reality, but a number of anomalies and controversial issues still need to be resolved. The main recommendation of the study is that research into these theories and models should continue unabated, while the specific recommendations in a South African context are the following: ( 1) the benefits of global diversification for South African investors should continue to be investigated; (2) the level and degree of efficiency of the JSE Securities Exchange SA (JSE) should continue to be monitored, and it should be established whether alternative theories to the EMH provide complementary or better descriptions of the efficiency of the South African market; (3) both the CAPM and the APT should continue to be tested, both individually and jointly, in order to better understand the pricing mechanism of, and risk/return relationship on the JSE; (4) much South African research still needs to be conducted on the efficiency of the relatively new options market and the application of the B-S Option Pricing Model under South African conditions. / Financial Accounting / M. Com. (Accounting)

Ce travail étudie des modèles financiers pour les prix d'options, les taux d'intérêts et le risque de crédit, avec des processus stochastiques à mémoire et comportant des discontinuités. Ces modèles sont formulés en termes du mouvement Brownien fractionnaire, du processus de Lévy fractionnaire ou filtré (et doublement stochastique) et de leurs approximations par des semimartingales. Leur calcul stochastique est traité au sens de Malliavin, et des formules d'Itô sont déduites. Nous caractérisons les probabilités risque neutre en termes de ces processus pour des modèles d'évaluation d'options de type de Black-Scholes avec sauts. Nous étudions également des modèles de taux d'intérêts, en particulier les modèles de Vasicek, de Cox-Ingersoll-Ross et de Heath-Jarrow-Morton. Finalement nous étudions la modélisation du risque de crédit / This work investigates financial models for option pricing, interest rates and credit risk with stochastic processes that have memory and discontinuities. These models are formulated in terms of the fractional Brownian motion, the fractional or filtered Lévy process (also doubly stochastic) and their approximations by semimartingales. Their stochastic calculus is treated in the sense of Malliavin and Itô formulas are derived. We characterize the risk-neutral probability measures in terms of these processes for options pricing models of Black-Scholes type with jumps. We also study models of interest rates, in particular the models of Vasicek, Cox-Ingersoll-Ross and Heath-Jarrow-Morton. Finally we study credit risk models

Este trabalho analisa três estratégias de hedge de opção, buscando identificar a importância da escolha da estratégia para a obtenção de um bom desempenho do hedge. O conceito de hedge é analisado de forma retrospectiva e uma teoria geral de hedge é apresentada. Em seguida são descritos alguns estudos comparativos de desempenho de estratégias de hedge de opção e suas metodologias de implementação. Para esta análise comparativa são selecionadas três estratégias de hedge de opção de compra do tipo européia: a primeira utiliza o modelo Black-Scholes-Merton de precificação de opções, a segunda utiliza uma solução de programação dinâmica para hedge dinâmico multiperiódico e a terceira utiliza um modelo GARCH para precificação de opções. As estratégias são comentadas e comparadas do ponto de vista de suas premissas teóricas e por meio de testes comparativos de desempenho. O desempenho das estratégias é comparado sob uma perspectiva dinâmicamente ajustada, multiperiódica e autofinanciável. Os dados para comparação de desempenho são gerados por simulação e o desempenho é avaliado pelos erros absolutos médios e erros quadráticos médios, resultantes na carteira de hedge. São feitas ainda considerações a respeito de alternativas de estimação e suas implicações no desempenho das estratégias. / This work analyzes three option hedging strategies, to identify the importance of choosing a strategy in order to achieve a good hedging performance. A retrospective analysis of the concept of hedging is conducted and a general hedging theory is presented. Following, some comparative papers of hedging performance and their implementation methodologies are described. For the present comparative analysis, three hedging strategies for European options have been selected: the first one based on the Black-Scholes-Merton model for option pricing, the second one based on a dynamic programming solution for dynamic multiperiod hedging and the third one based on a GARCH model for option pricing. The strategies are compared under their theoric premisses and through comparative performance testes. The performances of the strategies are compared under a dynamically adjusted multiperiodic and self-financing perspective. Data for performance comparison are generated by simulation and performance is evaluated by mean absolute errors and mean squared errors resulting on the hedging portfolio. An analysis is also done regarding estimation approaches and their implications over the performance of the strategies.

傳統人壽保險契約之分析利用等價原則(principal of equivalience) 來對商品評價。即保險人所收保費之現值等於保險人未來責任(保險金額給付)之現值。然而對於權益連結壽險商品而言，其結合傳統商品之風險(如利率風險、死亡率風險等)與財務風險，故更增加其評價困難性。過去研究中在假設預定利率為常數與死亡率為給定的情況下，利用Black-Scholes (1973)評價公式推導出公式解。然而Black-Scholes評價公式是建構在完全市場上，對於權益連結壽險商品而言其已不符合完全市場之假設，因此本文放寬完全市場之假設來對此商品重新評價與避險。 在財務市場上，對於不完全市場(incomplete markets)下請求權(contingent claims)之評價與避險，已發展出數個不同評價方法。本文利用均數變異避險(mean-variance hedging)方法(Follmer&Sondermann ，1986)所衍生之風險極小化(risk-minimization)觀念來對此保險衍生性金融商品評價與避險，並找到一風險衡量測度(Moller , 1996、1998a、2000)來評估發行此商品保險人需承受多少風險。 / In this study, actuarial equivalent principle and no-arbitrage pricing theory are used in pricing and valuation for unit-linked life insurance policies.　Since their market values cannot be replicated through the self-finance strategies due to market incompleteness, the theoretical setup in Black and Scholes (1973) and Follmer and Sondermann (1986) are adopted to develop the pricing and hedging strategies. Counting process is employed to characterize the transition pattern of the policyholder and the linked assets are modeled through the geometric Brownian motions. Equivalent martingale measures are adapted to derive the pricing formulas. Since the benefit payments depend on the performance of the underlying portfolios and the health status of the policyholder, mean-variance minimization criterion is employed to evaluate the financial risk. Finally pricing and hedging issues are examined through the numerical illustrations. Monte Carlo method is implemented to approximate the market premiums according to the payoff structures of the policies. In this paper, we show that the risk-minimization criterion can be used to determine the hedging strategies and access the minimal intrinsic risks for the insurers.

本篇論文旨在分析神經網路學習績效，並提出一套學習演算法，結合倒傳遞網路(BP)與理解神經網路(RN)，命名為RNBP，這套學習演算法將與傳統的BP做比較，以兩個不同的財務金融領域的應用，一個是選擇權上Black-Scholes訂價模式的模擬，一個是金融交換上利率的預測。主要績效的評估準則是以學習的效率與模擬、預測的準確度為依據。 此外，本論文的另一個重點是提出一套對於神經網路系統進一步分析的方法與工具，敏感度分析(Sensitivity Analysis)與滯留區(Dead Region)分析，藉以瞭解神經網路系統是否具有效地良好學習或被一般化的能力，從神經網路的角度來說，這也是BP與RNBP的另一個績效比較標準。本研究的結果顯示RNBP在預測準確度上較BP為優良，但是在學習效率與預測能力的穩定性上並沒有呈現一致性的結論；此外，敏感度分析與滯留區分析的結果也幫助神經網路在應用領域上有更深入的瞭解。 在過去，神經網路的應用者往往忽略了進一步瞭解神經網路的重要性與可行性，本論文的貢獻在於藉由分析神經網路所學習的知識，幫助應用者進一步瞭解神經網路表達的訊息在應用領域上所隱含的實質意義。 / The study attempts to analyze the learning performance of neural networks in applications, and propose a new learning procedure for the layered feedforward neural network systems, named KNBP, which binds RN and BP learning algorithms. Two artificial neural networks, BP and KNBP, here are both applied to two financial fields, the simulation of Black-Scholes pricing model for the call options and the midrates forecasting in financial swaps. The explicit performance comparison between the two artificial neural network systems is mainly based on two criteria, which are learning efficiency and forecasting effectiveness. Then we propound a mathematical methodology of sensitivity analysis and the dead regions to deeply explore inside the network structures to see whether the models of ANNS are actually well trained or valid, and thus setup an alternative comparable criterion. The results from this study show that RNBP performs better than BP in forecasting effectiveness, but RNBP obtains neither a consistent learning efficiency in cases nor a stable forecasting ability. Furthermore, the sensitivity analysis and the dead region analysis provide a deeper view of the ANNs in the applied fields. In the past, most studies applying neural networks ignored the importance that it is feasible and advantageous to obtain more useful information via analyzing neural networks. The purpose of the research is to help further understanding to the information discovery resulted from neural networks in practical applications.

Black-Scholes選擇權定價模型是各種選擇定價的開山始祖，無論在理論或實務上均獲致許多的便利及好評，美中不足的是，這種既定模型下結構化參數的估計問題，在真實體系的結構訊息未知或是不明朗時，或是模式錯誤，亦或政治結構或金融環境不知時，該模型在實證資料的評價上會面臨價格偏誤的窘境。是故，許多的數值演算法（numerical algorithms）便因應而生，這些方法一則源於對此基本模型的修正，一則是屬於逼近的數值解。 評價選擇權的方法雖不一而足，然所有的這些理論或模型可分為二大類即模型驅動的理論（model-drive approach）及資料驅動的理論（data-driven approach）。前者是建構在許多重要的假設，當這些假設成立時，則選擇權的價格可用如Black-Scholes偏微分方程來表示，而後再用數值解法求算出，許多的數值方法即屬於此類的範疇；而資料驅動的理論（data-driven approach），其理論的特色是它的有效性（validity）不像前者是依其假設，職是之故，他在處理現實世界的財務資料時更顯見其具有極大的彈性。這些以計算智慧（computation intelligence）為主的財務計量方法，如人工神經網路（ANNs），遺傳演算法（GAs），遺傳規劃（GP）已在財務工程（financial engineering）領域上萌芽，並有日趨蓬勃的態勢，而將機器學習技術（machine learning techniques）應用在衍生性商品的定價，應是目前財務應用上最複雜及困難，亦是最富挑戰性的問題。 本文除了對現有文獻的整理評析外，在人工神經網路方面，除用於S&P 500的實證外，並用於台灣剛推行不久的認購構證評價之實證研究；而遺傳規劃在計算智慧發展的領域中，算是較年輕的一員，但發展卻相當的快速，雖目前在經濟及財務上已有一些文獻，但就目前所知的二篇文獻選擇權定價理論的文獻中，仍是試圖學習Black-Scholes選擇權定價模型，而本文則提出修正模型，使之成為完全以資料驅動的模型，應用於S&P 500實證，亦證實可行。最後，本文結合計算智慧中的遺傳演算法（ genetic algorithms）及數學上的加權殘差法（weight-residual method）來建構一條除二項式定價模型，人工神經網路定價模型，遺傳規劃定價模型等資料驅動模型之外的另一種具適應性學習能力的選擇權定價模式。 / The option pricing development rapid in recent years. However, the recent rapid development of theory and the application can be traced to the pathbreaking paper by Fischer Black and Myron Scholes(1973). In that pioneer paper, they provided the first explicit general equilibrium solution to the option pricing problem for simple calls and puts and formed a basis for the contingent claim asset pricing and many subsequent academic studies. Although the Black-Scholes option pricing model has enjoyed tremendous success both in practice and research, Nevertheless, it produce biased price estimates. So, many numerical algorithms have advanced to modify the basic model. I classified these traditional numerical algorithms and computational intelligence methods into two categories. Namely, the model-driven approach and the data-driven approach. The model-driven approach is built on several major assumptions. When these assumption hold, the option price usually can be described as a partial differential equation such as the Black-Scholes formula and can be solved numerically. Several numerical methods can be regarded as a member of this category. There are the Galerkin method, finite-difference method, Monte-Carlo method, etc. Another is the data-driven approach. The validity of this approach does not rests on the assumptions usually made for the model-driven one, and hence has a great flexibility in handling real world financial data. Artificial neural networks, genetic algorithms and genetic programming are a member of this approach. In my dissertation, I take a literature review about option pricing. I use artificial neural networks in S & P 500 index option and Taiwan stock call warrant pricing empirical study. On the other hand, genetic programming development rapid in recent three years, I modified the past model and contruct a data-driven genetic programming model. andThen, I usd it to S & P 500 index option empirical study. In the last, I combined genetic algorithms and weight-residual method to develop a option pricing model.

Le modèle classique de la finance (Markowitz, Sharpe, Black, Scholes, Fama) a, dès le début, été remis en cause par le mathématicien Benoît Mandelbrot (1924-2010). Il démontre que la loi normale ne correspond pas à la réalité des marchés, parce qu’elle sous-estime les risques extrêmes. Il faut au contraire utiliser les lois de puissance, comme la loi de Pareto. Nous montrons ici toutes les implications de ce changement fondamental sur la finance, mais aus-si, ce qui est nouveau, en ce qui concerne la gestion des entreprises (à travers le calcul du coût des capitaux propres). Nous tentons de mettre à jour les raisons profondes de l’existence des lois de puissance en économie à travers la notion d’entropie. Nous présen-tons de nouveaux outils théoriques pour comprendre la formation des prix (la théorie de la proportion diagonale), des bulles (la notion de réflexivité), des crises (la notion de réseau), en apportant une réponse globale à la crise actuelle (un système monétaire diversifié). Toutes ces voies sont très peu, ou pas du tout exploitées. Elles sont surtout, pour la pre-mière fois, mises en cohérence autour de la notion de loi de puissance. C’est donc une nou-velle façon de comprendre les phénomènes économiques que nous présentons ici. / The classical model of finance (Markowitz, Sharpe, Black, Scholes, Fama) has, from the be-ginning, been challenged by the mathematician Benoit Mandelbrot (1924-2010). It shows that the normal distribution does not match the reality of the market, because it underesti-mates the extreme risks. Instead, we must use the power laws, such as the Pareto law. We show the implications of this fundamental change in the finance, but also in the manage-ment of companies (through the calculation of cost of capital). We try to update the underly-ing reasons for the existence of power laws in economics through the concept of entropy. We present new theoretical tools to understand price formation (the theory of diagonal proportion), bubbles (the notion of reflexivity), crisis (network concept), providing a com-prehensive response to the current crisis (a diversified monetary system). All these ways are very little or not at all exploited. They are mostly for the first time, made consistent around the notion of power law. This is a new way of understanding economic phenomena present-ed here.