Study on Quantum Finance Algorithm:Quantum Monte Carlo Algorithm based on European Option Pricing

As one of the major methods for the simulation of option pricing,Monte Carlo method assumes random fluctuations in the distribution of asset prices.Under certain uncertainties process,different evolution paths could be simulated so as to finally yield the expectation value of the asset price,which requires a lot of simulations to ensure the accuracy based on huge and expensive calculations.In order to solve the above computational problem,quantum Monte Carlo(QMC)has been established and applied in the relevant systems such as European call options.In this work,both MC and QM methods are adopted to simulate European call options.Based on the preparation of quantum states in QMC algorithm and the construction of quantum circuits by simulating a quantum hardware environment on a traditional computer,the amplitude estimation(AE)algorithm is found to play a secondary role in accelerating the pricing of European options.More importantly,the payoff function and the time required for the simulation in QMC method show some improvements than those in MC method.

Vulnerable European Call Option Pricing Based on Uncertain Fractional Differential Equation

This paper presents two new versions of uncertain market models for valuing vulnerable European call option.The dynamics of underlying asset,counterparty asset,and corporate liability are formulated on the basis of uncertain differential equations and uncertain fractional differential equations of Caputo type,respectively,and the solution to an uncertain fractional differential equation of Caputo type is presented by employing the Mittag-Leffler function andα-path.Then,the pricing formulas of vulnerable European call option based on the proposed models are investigated as well as some algorithms.Some numerical experiments are performed to verify the effectiveness of the results.

Parallelization and Acceleration of Dynamic Option Pricing Models on GPU-CPU Heterogeneous Systems

In this paper,stochastic global optimization algorithms,specifically,genetic algorithm and simulated annealing are used for the problem of calibrating the dynamic option pricing model under stochastic volatility to market prices by adopting a hybrid programming approach.The performance of this dynamic option pricing model under the obtained optimal parameters is also discussed.To enhance the model throughput and reduce latency,a heterogeneous hybrid programming approach on GPU was adopted which emphasized a data-parallel implementation of the dynamic option pricing model on a GPU-based system.Kernel offloading to the GPU of the compute-intensive segments of the pricing algorithms was done in OpenCL.The GPU approach was found to significantly reduce latency by an optimum of 541 times faster than a parallel implementation approach on the CPU,reducing the computation time from 46.24 minutes to 5.12 seconds.

Efficient Simulations of Option Pricing and Greeks Under Three-Factor Model by Conditional Monte Carlo Method

This paper proposes a hybrid Monte Carlo simulation method for pricing European options under the stochastic volatility model and three-factor model.First,the European options are expressed as a conditional expectation formula,which can be used not only for reducing variance of simulations,but also for calculating the value of Greeks easily,due to the elimination of the weak singularity for the payoff of the option.Then,in order to reduce variance further,the authors also construct a new explicit regression based control variate under Heston model and three-factor model respectively.Numerical results of experiments show that the proposed method can greatly reduce the variance of simulation for pricing European option,and is easy to complement for the calculation of Greeks.

Rough Heston Models with Variable Vol-of-Vol and Option Pricing

In this paper,a rough Heston model with variable volatility of volatility(vol-of-vol)is derived by modifying the generalized nonlinear Hawkes process and extending the scaling techniques.Then the nonlinear fractional Ric-cati equation for the characteristic function of the asset log-price is derived.The existence,uniqueness and regularity of the solution to the nonlinear fractional Riccati equation are proved and the equation is solved by the Adams methods.Finally the Fourier-cosine methods are combined with the Adams methods to price the options.

Pricing European Options Based on a Logarithmic Truncated t-Distribution

The t-distribution has a “fat tail” feature, which is more suitable than the normal probability density function to describe the distribution characteristics of return on assets. The difficulty of using t-distribution to price European options is that a fat tail can lead to a deviation in one integral required for option pricing. We use a distribution called logarithmic truncated t-distribution to price European options. A risk neutral valuation method was used to obtain a European option pricing model with logarithmic truncated t-distribution.

Option Pricing under the Double Exponential Jump-Diffusion Model with Stochastic Volatility and Interest Rate

This paper proposes an efficient option pricing model that incorporates stochastic interest rate(SIR),stochastic volatility(SV),and double exponential jump into the jump-diffusion settings.The model comprehensively considers the leptokurtosis and heteroscedasticity of the underlying asset’s returns,rare events,and an SIR.Using the model,we deduce the pricing characteristic function and pricing formula of a European option.Then,we develop the Markov chain Monte Carlo method with latent variable to solve the problem of parameter estimation under the double exponential jump-diffusion model with SIR and SV.For verification purposes,we conduct time efficiency analysis,goodness of fit analysis,and jump/drift term analysis of the proposed model.In addition,we compare the pricing accuracy of the proposed model with those of the Black-Scholes and the Kou(2002)models.The empirical results show that the proposed option pricing model has high time efficiency,and the goodness of fit and pricing accuracy are significantly higher than those of the other two models.

Pricing Stochastic Barrier Options under Hull-White Interest Rate Model

A barrier option valuation model with stochastic barrier which was regarded as the main feature of the model was developed under the Hull-White interest rate model.The purpose of this study was to deal with the stochastic barrier by means of partial differential equation methods and then derive the exact analytical solutions of the barrier options.Furthermore,a numerical example was given to show how to apply this model to pricing one structured product in realistic market.Therefore,this model can provide new insight for future research on structured products involving barrier options.

American Barrier Option Pricing Formulas for Currency Model in Uncertain Environment

Option pricing problem is one of the central issue in the theory of modern finance.Uncertain currency model has been put forward under the foundation of uncertainty theory as a tool to portray the foreign exchange rate in uncertain finance market.This paper uses uncertain differential equation involved by Liu process to dispose of the foreign exchange rate.Then an American barrier option of currency model in uncertain environment is investigated.Most important of all,the authors deduce the formulas to price four types of American barrier options for this currency model in uncertain environment by rigorous derivation.

Quasi-Monte Carlo simulation of Brownian sheet with application to option pricing

Monte Carlo and quasi-Monte Carlomethods arewidely used in scientific studies.As quasi-Monte Carlo simulations have advantage over ordinaryMonte Carlomethods,this paper proposes a new quasi-Monte Carlo method to simulate Brownian sheet via its Karhunen–Loéve expansion.The proposed new approach allocates quasi-random sequences for the simulation of random components of the Karhunen–Loéve expansion by maximum reducing its variability.We apply the quasi-MonteCarlo approach to an option pricing problem for a class of interest rate models whose instantaneous forward rate driven by a different stochastic shock through Brownian sheet andwe demonstrate the application with an empirical problem.

Early exercise premium method for pricing American options under the J-model

Background:This study develops a new model called J-am for pricing American options and for determining the related early exercise boundary(EEB).This model is based on a closed-form solution J-formula for pricing European options,defined in the study by Jerbi(Quantitative Finance,15:2041-2052,2015).The J-am pricing formula is a solution of the Black&Scholes(BS)PDE with an additional function called f as a second member and with limit conditions adapted to the American option context.The aforesaid function f represents the cash flows resulting from an early exercise of the option.Methods:This study develops the theoretical formulas of the early exercise premium value related to three American option pricing models called J-am,BS-am,and Heston-am models.These three models are based on the J-formula by Jerbi(Quantitative Finance,15:2041-2052,2015),BS model,and Heston(Rev Financ Stud,6:327-343,1993)model,respectively.This study performs a general algorithm leading to the EEB and to the American option price for the three models.Results:After implementing the algorithms,we compare the three aforesaid models in terms of pricing and the EEB curve.In particular,we examine the equivalence between J-am and Heston-am as an extension of the equivalence studied by Jerbi(Quantitative Finance,15:2041-2052,2015).This equivalence is interesting since it can reduce a bi-dimensional model to an equivalent uni-dimensional model.Conclusions:We deduce that our model J-am exactly fits the Heston-am one for certain parameters values to be optimized and that all the theoretical results conform with the empirical studies.The required CPU time to compute the solution is significantly less in the case of the J-am model compared with to the Heston-am model.

Efficient Option Pricing Methods Based on Fourier Series Expansions

A novel option pricing method based on Fourier-cosine series expansion was proposed by Fang and Oosterlee.Developing their idea,three new option pricing methods based on Fourier,Fourier-cosine and Fourier-sine series expansions are presented in this paper,which are more efficient when the option prices are calculated with many strike prices.A series of numerical experiments under different exp-Lévy models are also given to compare these new methods with the Fang and Oosterlee's method and other methods.

Asset Pricing and Simulation Analysis Based on the New Mixture Gaussian Processes

European compound option pricing model is established by using the mixed bifractional Brownian motion. Firstly, using the principle of risk-neutral pricing, the European option pricing formulas and the parity formulas are obtained. Secondly, with the Delta hedging strategy, the corresponding compound option pricing formulas and the parity formulas are got. Finally, using the daily closing price data of “Lingang B shares” and “Yitai B shares” respectively, the results show that the mixed model is closer to the true value than the previous model.

An ETD Method for American Options under the Heston Model

A numerical method for American options pricing on assets under the Heston stochastic volatility model is developed.A preliminary transformation is applied to remove the mixed derivative term avoiding known numerical drawbacks and reducing computational costs.Free boundary is treated by the penalty method.Transformed nonlinear partial differential equation is solved numerically by using the method of lines.For full discretization the exponential time differencing method is used.Numerical analysis establishes the stability and positivity of the proposed method.The numerical convergence behaviour and effectiveness are investigated in extensive numerical experiments.

How Load Aggregators Avoid Risks in Spot Electricity Market:In the Framework of Power Consumption Right Option Contracts

There is uncertainty in the electricity price of spot electricity market,which makes load aggregators undertake price risks for their agent users.In order to allow load aggregators to reduce the spot market price risk,scholars have proposed many solutions,such as improving the declaration decision-making model,signing power mutual insurance contracts,and adding energy storage and mobilizing demand-side resources to respond.In terms of demand side,calling flexible demand-side resources can be considered as a key solution.The user’s power consumption rights(PCRs)are core contents of the demand-side resources.However,there have been few studies on the pricing of PCR contracts and transaction decisions to solve the problem of price forecast deviation and to manage the uncertainty of spot market prices.In addition,in traditional PCR contracts,PCRs are mostly priced using a single price mechanism,that is,the power user is compensated for part of the electricity that was interrupted or reduced in power supply.However,some power users might engage in speculative behaviours under this mechanism.Further,for load aggregators,their price risk avoidance ability has not substantially improved.As a financial derivative,options can solve the above problems.In this article,firstly,the option method is used to build an option pricing optimization model for power consumption right contracts that can calculate the optimal option premium and strike price of option contracts of power consumption rights.Secondly,from the perspective of power users and load aggregators,a simulation model of power consumption right transaction decision-making is constructed.The results of calculation examples show that(1)Under the model in this article,the pricing of option contracts for power consumption rights with better risk aversion capabilities than traditional compensation contracts can be obtained.(2)The decision to sell or purchase the power consumption rights will converge at respective highvalue periods,and option contracts will expedite the process.(3)Option contracts can significantly reduce the loss caused by the uncertainty of spot electricity prices for load aggregators without reducing users’willingness to sell power consumption rights.

A Comparative Study of Support Vector Machine and Artificial Neural Network for Option Price Prediction

Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices.

Modified Differential Transform Method for Solving Black-Scholes Pricing Model of European Option Valuation Paying Continuous Dividends

.Option pricing is a major problem in quantitative finance.The Black-Scholes model proves to be an effective model for the pricing of options.In this paper a com-putational method known as the modified differential transform method has been em-ployed to obtain the series solution of Black-Scholes equation with boundary condi-tions for European call and put options paying continuous dividends.The proposed method does not need discretization to find out the solution and thus the computa-tional work is reduced considerably.The results are plotted graphically to establish the accuracy and efficacy of the proposed method.

On the Convergence of a Crank-Nicolson Fitted Finite Volume Method for Pricing European Options under Regime-Switching Kou’s Jump-Diffusion Models

In this paper,we construct and analyze a Crank-Nicolson fitted finite volume scheme for pricing European options under regime-switching Kou’s jumpdiffusion model which is governed by a system of partial integro-differential equations(PIDEs).We show that this scheme is consistent,stable and monotone as the mesh sizes in space and time approach zero,hence it ensures the convergence to the solution of continuous problem.Finally,numerical experiments are performed to demonstrate the efficiency,accuracy and robustness of the proposed method.

No-Arbitrage in Financial Economics: Solution of the Mystery of Implied Volatility and S&P 500 Volatility Index

We have shown that classic works of Modigliani and Miller, Black and Scholes, Merton, Black and Cox, and Leland making the foundation of the modern asset pricing theory, are wrong due to misinterpretation of no arbitrage as the martingale no-arbitrage principle. This error explains appearance of the geometric Brownian model (GBM) for description of the firm value and other long-term assets considering the firm and its assets as self-financing portfolios with symmetric return distributions. It contradicts the empirical observations that returns on firms, stocks, and bonds are skewed. On the other side, the settings of the asset valuation problems, taking into account the default line and business securing expenses, BSEs, generate skewed return distributions for the firm and its securities. The Extended Merton model (EMM), taking into account BSEs and the default line, shows that the no-arbitrage principle should be understood as the non-martingale no arbitrage, when for sufficiently long periods both the predictable part of returns and the mean of the stochastic part of returns occur negative, and the value of the return deficit depends on time and the states of the firm and market. The EMM findings explain the problems with the S&P 500 VIX, the strange behavior of variance and skewness of stock returns before and after the crisis of 1987, etc.

马尔可夫交换Lévy过程模型下的期权定价及其对冲（英文）

In this paper, we consider a Markov switching Lévy process model in which the underlying risky assets are driven by the stochastic exponential of Markov switching Lévy process and then apply the model to option pricing and hedging. In this model, the market interest rate, the volatility of the underlying risky assets and the N-state compensator,depend on unobservable states of the economy which are modeled by a continuous-time Hidden Markov process. We use the MEMM(minimal entropy martingale measure) as the equivalent martingale measure. The option price using this model is obtained by the Fourier transform method. We obtain a closed-form solution for the hedge ratio by applying the local risk minimizing hedging.