Option Pricing Research Articles

A study on the fractional Black–Scholes option pricing model of the financial market via the Yang-Abdel-Aty-Cattani operator

PurposeFinancial mathematics is one of the most rapidly evolving fields in today’s banking and cooperative industries. In the current study, a new fractional differentiation operator with a nonsingular kernel based on the Robotnov fractional exponential function (RFEF) is considered for the Black–Scholes model, which is the most important model in finance. For simulations, homotopy perturbation and the Laplace transform are used and the obtained solutions are expressed in terms of the generalized Mittag-Leffler function (MLF).Design/methodology/approachThe homotopy perturbation method (HPM) with the help of the Laplace transform is presented here to check the behaviours of the solutions of the Black–Scholes model. HPM is well known for its accuracy and simplicity.FindingsIn this attempt, the exact solutions to a famous financial market problem, namely, the BS option pricing model, are obtained using homotopy perturbation and the LT method, where the fractional derivative is taken in a new YAC sense. We obtained solutions for each financial market problem in terms of the generalized Mittag-Leffler function.Originality/valueThe Black–Scholes model is presented using a new kind of operator, the Yang-Abdel-Aty-Cattani (YAC) operator. That is a new concept. The revised model is solved using a well-known semi-analytic technique, the homotopy perturbation method (HPM), with the help of the Laplace transform. Also, the obtained solutions are compared with the exact solutions to prove the effectiveness of the proposed work. The different characteristics of the solutions are investigated for different values of fractional-order derivatives.

Estimating Probability Weighting Functions through Option Pricing Bounds

Abstract This paper proposes a novel approach to estimating the probability weighting function (PWF) of investors in the option market. We match observed option prices to the option pricing bounds under stochastic dominance rules. Using 1-month S&P 500 index option data, we find that investors could subjectively employ an inverse S-shaped probability weighting function, which increases the weights on extreme returns and asymmetrically assigns greater weights to extremely low returns than to extremely high returns. Our findings suggest that the inverse S-shaped nature of the PWFs is robust across various estimation specifications, such as adopting an alternative methodology to construct the return distribution, and employing option data with different times to maturity. (JEL G12)

Total variation regularization analysis for inverse volatility option pricing problem

We investigate in this paper an inverse problem of recovering the space-dependent volatility in option pricing. To enhance precision across the domain, we transform the original problem into an inverse source problem of a bounded degenerate parabolic equation, utilizing linearization and variable substitution. Unlike classical methods, we apply a total variation regularization combined with a novel generalized finite integration technique. This approach accommodates volatility jumps in an overnight rate scenario. Leveraging an optimal control framework, we demonstrate that the inverse problem can be reformulated as an optimal control problem whose existence, necessary conditions, local uniqueness and stability for the minimizer of control functional are obtained. For numerical verification, we derive the Euler equation and design a discretization algorithm with generalized finite integration technique. Numerical examples showcase the robustness of our approach, highlighting advantages in accuracy and effectiveness over other strategies.

Monotonic transformation and recovering the implied stock price process

AbstractThe paper demonstrates the construction of a stock price stochastic process that aligns with observed option prices, drawing inspiration from a manuscript by Professor Erio Castagnoli that employs the concept of a non-linear monotonic transformation of a standard Brownian motion. The paper includes a detailed numerical example that illustrates the implementation of this straightforward and ingenious idea.

Pricing exchange options under stochastic correlation

In this paper we study the pricing of exchange options when the underlying assets have stochastic volatility and stochastic correlation. An approximated closed-form formula based on a Taylor expansion of the conditional Margrabe price is proposed. The problem is illustrated within the framework of the exchange between two different types of oil commodities.

Applying Theorem of Ross to Transition Probability Matrix in Energy Options Pricing after Political Tensions and Russia-Ukraine Conflict

Probability Matrix, it is necessary to obtain energy options pricing. The paper found the true probabilities of transition from the current state of the market to other states. The energy options pricing including effects of Political tensions is proposed. The methods are based on true probabilities of transition to the next state of the energy market. It can be used to predict the movement of the underlying asset of energy options (oil and gas derivatives pricing). For example, one strategy might be to compare the energy sources at the beginning 2022 (before 24.02.2024) with the current state of the market. The results show that this matrix can be implemented in energy options pricing, which represents the true probabilities of transition from the current state of the market to modern state. Practical implementation is an investment strategy in oil and gas futures that should include the median of a given distribution with the current state of the market after beginning of Russia-Ukraine conflict. The absence of arbitrage opportunities is a fundamental principle underlying modern financial pricing theory. This concept is peculiar to the statement of the fundamental theorem of asset pricing. The results prove idea that at a fixed point in time is the information about some market quotations of American Call options can be determined. The results have a set of quotes that are available in both directions: both for strikes and for periods to maturity.

Option pricing under jump diffusion model

We provide an European option pricing formula written in the form of an infinite series of Black–Scholes-type terms under double Lévy jumps model, where both the interest rate and underlying price are driven by Lévy processes. The series solution converges with a radius of convergence, and it is complemented by some numerical experiments to demonstrate its speed of convergence.

American put options with regime-switching volatility

We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.

Primal‐dual active set algorithm for valuating American options under regime switching

AbstractThis paper focuses on numerical algorithms to value American options under regime switching. The prices of such options satisfy a set of complementary parabolic problems on an unbounded domain. Based on our previous experience, the pricing model could be truncated into a linear complementarity problem (LCP) over a bounded domain. In addition, we transform the resulting LCP into an equivalent variational problem (VP), and discretize the VP by an Euler‐finite element method. Since the variational matrix in the discretized system is P‐matrix, a primal‐dual active set (PDAS) algorithm is proposed to evaluate the option prices efficiently. As a specialty of PDAS, the optimal exercise boundaries in all regimes are obtained without further computation cost. Finally, numerical simulations are carried out to test the performance of our proposed algorithm and compare it to existing methods.

VIX option pricing through nonaffine GARCH dynamics and semianalytical formula

AbstractThis paper develops analytical approximations for volatility index (VIX) option pricing under nonaffine generalized autoregressive conditional heteroskedasticity (GARCH) models as advocated by Christoffersen et al. We obtain the approximation formulae for pricing VIX options and then evaluate their performance with three expansions under four empirically well‐tested models. Our numerical experiments find that the weighted expansion generated by the fat‐tailed weighting kernel can significantly reduce approximation error over the Gram‐Charlier expansion; the Taylor expansion of conditional moments can lead to divergence for parameters with certain high persistence in the affine GARCH, nonlinear asymmetric GARCH, and Glosten‐Jagannathan‐Runkle GARCH models, while surviving during high persistence in the exponential GARCH.

Option pricing with dynamic conditional skewness

AbstractIn this paper, we develop a discrete‐time affine option‐pricing model that explicitly incorporates the dynamics of conditional skewness. The new proposed model features different dynamics for conditional skewness and variance. To stress the difference in information, we use alternative realized measures constructed from high‐frequency historical returns to update skewness and variance dynamics. By Fourier inversion, we derive closed‐form option valuation formulas. Empirically, the flexibility that the model offers for conditional skewness as well as high‐frequency information from the underlying asset contribute to superior performance upon benchmark models using S&P 500 index options. Overall, the joint modeling of dynamic conditional skewness and realized measures leads to an out‐of‐sample gain of 12.25% in pricing accuracy. The improvements are more pronounced for deep in‐the‐money calls, options with shorter maturities, and during highly volatile periods.

Advertising investments on television: real option estimation through Markov chains

We propose the valuation of a real option in the telecommunications industry. According to the probabilistic present worth approach, we estimate the value of a contract between a television network and a company willing to advertise its business on this network. We assume that the value of the contract depends on a time-dependent variable, i.e., the number of viewers tuned into the network, which behaves like a Markov process. After discretizing and converting this number into a monetary value through a specific function, we compute the nth-order moment of the total discounted earnings. The knowledge of the moments, and the application of the maximum-entropy approach, allows to find the probability distribution of the payoff function and the consequential pricing of the real option. Finally, we apply the proposed model to the real television audience data.

A critical analysis of the Weighted Least Squares Monte Carlo method for pricing American options

Least-squares Monte Carlo generates regression-based continuation value estimators that are heteroscedastic. Fabozzi et al. (2017) propose weighted least-squares regression to correct for this. We show that heteroscedastic-corrected estimators are more accurate than uncorrected estimators far from the exercise boundary and where the exercise decision is obvious. However, the corrected estimators do not translate into improved exercise decisions and hence correcting has little effect on option price estimates. This holds when using alternative specifications for the correction and when implementing an iterative method. We conclude that correcting for heteroscedasticity does not result in more efficient prices and generally should be avoided.

Exact simulation of the Hull and White stochastic volatility model

We show how to simulate exactly the asset price and the variance under the Hull and White stochastic volatility model. We derive analytical formulas for the Laplace transform of the time integral of volatility conditional on the variance level at the endpoint of the time interval and the Laplace transform of integrated variance conditional on both integrated volatility and variance. Based on these results, we simulate the model through a nested-conditional factorization approach, where Laplace transforms are inverted through the (conditional) Fourier-cosine (COS) method. Under this model, our approach can be used to generate unbiased estimates for the price of derivatives instruments. We propose some variants of the exact simulation scheme for computing unbiased estimates of option prices and sensitivities, a difficult task in the Hull and White model. These variants also allow for a significant reduction in the Monte Carlo simulation estimator's variance (around 93-98%) and the computing time (around 22%) when pricing options. The performances of the proposed algorithms are compared with various benchmarks. Numerical results demonstrate the faster convergence rate of the error in our method, which achieves an O(s−1/2) convergence rate, where s is the total computational budget, largely outperforming the benchmark.

Considering momentum spillover effects via graph neural network in option pricing

AbstractTraditional options pricing relies on underlying asset volatility and contract properties. However, asset volatility is affected by the “lead–lag effects,” known as the “momentum spillover effect.” To address this, we propose a proxy measuring correlated options' influence based on maturity date. Findings indicate that 1‐day‐lagged proxy indicators positively impact option returns. Furthermore, to capture the dynamic effects of correlated options, we introduce a deep graph neural network‐based model (GNN‐MS). Empirical results on Shanghai Stock Exchange 50 exchange‐traded fund options reveal GNN‐MS significantly outperforms classics, enhancing root‐mean‐square error by at least 8.81%. This study provides novel insights into option pricing considering momentum spillover effects.

Estimating time-varying factors’ variance in the string-term structure model with stochastic volatility

This paper presents a novel term structure of interest rate (TSIR) model with stochastic volatility and jumps (SVJ) that combines the market framework proposed by Brace et al. (1997) with the string-shock framework of Santa-Clara and Sornette (2001). In this model, the factors’ variance is estimated through the eigendecomposition of a variance–covariance matrix obtained with a measure of market volatility derived from out-of-the-money option prices and historical correlations of interest rates traded in the futures market. The stochastic evolution of the factors’ variance is governed by the 4/2 model developed by Grasselli (2017), including jumps. A novel method is employed to estimate the parameters of the SVJ model that minimizes the distance between the sample moments and the moments of a gamma distribution. The empirical application of the model in the Brazilian derivatives market demonstrates its effectiveness in accurately capturing the volatility smile of interest rate options.

Financial Innovation and Risk Management in Japan

Purpose: The aim of the study was to investigate the Financial Innovation and Risk Management. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Japan has seen a surge in financial innovation driven by fintech startups and digitalization efforts by traditional banks. However, this innovation comes with regulatory challenges and risks that must be managed. Regulatory frameworks are being updated to address new risks like cybersecurity threats. There's also a growing focus on corporate governance, risk culture, and sustainability integration. Unique Contribution to Theory, Practice and Policy: Option pricing theory, capital asset pricing model (CAPM) & modern portfolio theory (MPT) may be used to anchor future studies on the financial innovation and risk management. Foster a culture of innovation within financial institutions by promoting collaboration between risk management professionals and product development teams. Establish regulatory sandboxes and innovation hubs to facilitate responsible experimentation with financial innovations while maintaining regulatory oversight.

Real option pricing under the regime-switching model with jumps on a finite time horizon

We consider an irreversible investment decision problem on a finite time horizon where an instantaneous cash flow process of a firm follows a regime-switching jump-diffusion (RSJD) model. The value of a project can be derived from a partial integro-differential equation (PIDE) and then we obtain a closed-form solution of the PIDE. It is proved that the value of the project converges to the solution on an infinite time horizon problem as the lifetime of the project tends to infinity. Moreover, the value function of a real option with a finite expiration date can be evaluated by solving a linear complementarity problem (LCP) under the RSJD model. We apply a 2-step backward differentiation formula (BDF2 method) combined with an operator splitting method to solve the LCP and then it has the second-order convergence rate in the discrete ℓ2-norm with respect to the time and spatial variables. The stability of the proposed method to solve the LCP is also proved in the discrete ℓ2-norm. Finally, a variety of numerical experiments are carried out to determine the optimal investment time and the numerical results on the finite time horizon are analyzed.

Accelerated numerical solutions for discretized Black–Scholes equations

Abstract Accepted by: Aris Syntetos This study thoroughly investigates the efficiency of advanced numerical extrapolation methods aimed at enhancing the convergence of vector sequences in the realm of mathematical finance. Our focus lies in the application of polynomial extrapolation techniques to calculate finite difference solutions for the Black–Scholes (BS) equation—an indispensable model in options pricing. The performance of our algorithms undergoes rigorous evaluation through a comprehensive analysis involving both simulated and real-world data. Notably, our experiments uncover that a stochastic scheme, incorporating two extrapolation strategies and a random relaxation parameter, outperforms other proposed methods, excelling in both convergence and stability metrics. Our findings underscore the potential of this numerical extrapolation method to enhance the efficiency of financial calculations, particularly in the realm of option pricing. This innovation holds promise for refining financial models and addressing specific challenges within the field of mathematical programming, providing effective solutions to the primary computational bottlenecks commonly encountered in financial decision-making.

European Option Pricing Based on FSDE Driven by Fractional Brownian Motion

In the actual financial market, the classical Black-Scholes (B-S) model cant perfectly describe the process of stock price. Besides, memory effect is an important phenomenon in financial systems. Thus, in this paper, we establish a fractional order stochastic differential equations (FSDE) which is driven by fractional Brownian motion (fBm) to describe the effect of noise memory and trend memory in financial pricing. Finally, we derive a European option pricing formula based on the established model. After conducting an empirical analysis based on the SSE 50ETF, we find that the established model performs better than the traditional one.