Pricing options quickly and accurately is a well known problem in finance. Quantum computing is being researched with the hope that quantum computers will be able to price options more efficiently than classical computers. This research extends\nthe quantum binomial option pricing model proposed by Zeqian Chen to European\nput options and to Barrier options and develops a quantum algorithm to price them.\nThis research produced three key results. First, when Maxwell-Boltzmann statistics\nare assumed, the quantum binomial model option prices are equivalent to the classical binomial model. Second, options can be priced efficiently on a quantum computer after the circuit has been built. The time complexity is O((N − τ)log(N − τ)) and it is in the BQP quantum computational complexity class. Finally, challenges extending the quantum binomial model to American, Asian and Bermudan options exist as the quantum binomial model does not take early exercise into account.
Pricing options quickly and accurately is a well known problem in finance. Quantum computing is being researched with the hope that quantum computers will be able to price options more efficiently than classical computers. This research extends\nthe quantum binomial option pricing model proposed by Zeqian Chen to European\nput options and to Barrier options and develops a quantum algorithm to price them.\nThis research produced three key results. First, when Maxwell-Boltzmann statistics\nare assumed, the quantum binomial model option prices are equivalent to the classical binomial model. Second, options can be priced efficiently on a quantum computer after the circuit has been built. The time complexity is O((N − τ)log(N − τ)) and it is in the BQP quantum computational complexity class. Finally, challenges extending the quantum binomial model to American, Asian and Bermudan options exist as the quantum binomial model does not take early exercise into account.
The valuation of American options and the determination of optimal stopping times remain central challenges in option pricing theory and derivatives valuation. Classical approaches to multidimensional problems rely on the dynamic programming principle, which is notoriously difficult to parallelize. Moreover, the need to store continuation values for all simulated paths in each dynamic programming step imposes severe memory constraints on classical computers, limiting the scale of feasible simulations. Quantum computing offers a promising way to address these limitations. In this paper, the authors introduce a novel algorithm for pricing American options on quantum hardware. To date, only one method has been proposed for this purpose. The authors' approach combines the Quantum Binomial Tree with Quantum Machine Learning to enable direct valuation of American options on a quantum computer. By incorporating quantum amplitude estimation, it could achieve a quadratic speed-up over classical Monte Carlo methods. Furthermore, it exploits the exponential growth of the quantum state vector, overcoming the memory bottlenecks that restrict classical approaches.
Abstract Pricing of financial derivatives, in particular early exercisable options such as Bermudan options, is an important but heavy numerical task in financial institutions, and its speed-up will provide a large business impact. Recently, applications of quantum computing to financial problems have been started to be investigated. In this paper, we first propose a quantum algorithm for Bermudan option pricing. This method performs the approximation of the continuation value, which is a crucial part of Bermudan option pricing, by Chebyshev interpolation, using the values at interpolation nodes estimated by quantum amplitude estimation. In this method, the number of calls to the oracle to generate underlying asset price paths scales as O(ϵ−1) O ˜ ( ϵ − 1 ) , where ϵ is the error tolerance of the option price. This means the quadratic speed-up compared with classical Monte Carlo-based methods such as least-squares Monte Carlo, in which the oracle call number is O(ϵ−2) O ˜ ( ϵ − 2 ) .
The Quantum Binomial Options Pricing Model is a novel approach to pricingderivatives. It serves as a quantum analogue to the classical binomial optionspricing model, using concepts from quantum mechanics like the Pauli matricesand Bloch sphere to represent the derivatives market. Through usage of a den-sity matrix and mixed states, the model can potentially price derivatives moreaccurately. However, at this time there exist no clear formulae and methodsfor hedging derivatives through this model. This paper numerically tests theperformance of the hedging framework corresponding to the model as comparedto the classical model using a year of historical S&P 500 data. The simulationreveals that the Quantum Binomial Options Pricing model is, at least in itscurrent state, not viable for usage in actually hedging derivatives. This is dueto numerical instabilities, particularly in the Quantum Theta and QuantumVega, the former of which ends up negative, betraying fundamental financialpostulates, and the latter of which is blown out of scale due to the short termnature of the simulation. We conclude that the Quantum Binomial OptionsPricing Model is currently unsuitable for practical applications and that ourfindings reveal the necessity of rigorously testing new financial models againstbenchmark models like the Classical Binomial Options Pricing Model.
The article provides a literature review and analysis of quantum computing methods in the context of option pricing, comparing them against a strict classical benchmark and recommending a viable way forward to quantum computing implementation. We assess the capabilities and the weaknesses of Windows-choice models closed-form legacy formulae of vanillas, lattice/PDE true hockeygoal models of early exercise, and Monte Carlo models of flexibleness and point out where classical run attracted and precision restraints are shown to apply in the new generation(s): complexity of variables, in this path dependence, in a portfolio composing. We proceed to describe quantum algorithms that are based on quantum Monte Carlo which is centered on amplitude-estimation-based algorithms to cut down on contact with sampling; avoiding the scale of classical Monte Carlo and are particularly appealing to complex derivatives; we describe circuit components to prepare states, encode payoffs, and approximate expectations. The study establishes research priorities that achieve scale implies: depth frequently used to design loaders in, and variants of payoff circuits, workflow standardizations that exchange the exchange of error-resilient quantum modules and technological risk engines in the NISQ age. In general, the paper offers a single source of information on classical-quantum option pricing, clean insertion architecture to deploy hybrids and a roadmap between demonstrations and reliable production.
Options are financial instruments whose payoffs are determined by the value of an underlying asset. These underlying assets are typically stocks, interest rates, or exchange rates, but can also include commodities such as energy or agricultural products. Options serve diverse purposes, including hedging against price volatility and speculating on market movements. Additionally, they play a critical role in assessing certain positions on the balance sheets of insurance companies, particularly in the context of solvency capital requirement calculations. As an option’s payoff lies in the future and is stochastic in nature, finding a fair price today is a non-trivial task. The focus of this work is finding quantum algorithms for calculating these prices and compare them to algorithms designed for classical computers. This work follows two different directions on how to improve on classical methods for option pricing. The first direction is improving on Monte-Carlo (MC) methods and the second one is adapting Fourier based pricing algorithms. The Amplitude Estimation (AE) algorithm promises a quadratic speedup compared to classical MC. This fact makes this algorithm an interesting candidate for improvements by quantum algorithms. It was shown in the literature, that simple options with very crude discretization of the underlying stochastic models, can be implemented on a quantum device and evaluated with AE. One big challenge is the efficient implementation of the option’s payoff profile. To solve this problem colleagues and I have introduced a multi-objective genetic algorithm to automatically find well performing and efficient circuits to implement the payoff of non-path-dependent European options on one or more underlyings. Another strategy for pricing options is based on the fact, that in many models the characteristic function of the underlying is numerically more tractable than its distribution function. There are methods that make use of this fact and the performance of the Fast Fourier Transform (FFT) to efficiently calculate prices for European call options for many strikes in one go. The quantum version of the FFT, the Quantum Fourier Transform (QFT), is in some sense, even faster than its classical counterpart. We have adapted the existing classical method to make use of QFT, to benefit from its performance. Finally, the different strategies are benchmarked and compared regarding their accuracy, their potential for scaling and their adaptability to different styles of derivatives and models. The result of this comparison are requirements on the quantum hardware for the methods to be viable as well as use cases where each method has its benefits over the others.
The accurate valuation of financial derivatives plays a pivotal role in the finance industry. Although closed formulas for pricing are available for certain models and option types, exemplified by the European Call and Put options in the Black-Scholes Model, the use of either more complex models or more sophisticated options precludes the existence of such formulas, thereby requiring alternative approaches. The Monte Carlo simulation, an alternative approach effective in nearly all scenarios, has already been challenged by quantum computing techniques that leverage Amplitude Estimation. Despite its theoretical promise, this approach currently faces limitations due to the constraints of hardware in the Noisy Intermediate-Scale Quantum (NISQ) era. In this study, we introduce and analyze a quantum algorithm for pricing European call options across a broad spectrum of asset models. This method transforms a classical approach, which utilizes the Fast Fourier Transform (FFT), into a quantum algorithm, leveraging the efficiency of the Quantum Fourier Transform (QFT). Furthermore, we compare this novel algorithm with existing quantum algorithms for option pricing.