Constant Elasticity Of Variance Research Articles

A Model-Free Fourier Cosine Method for Estimating the Risk-Neutral Density

In this article, we present a new nonparametric method to extract the risk-neutral density from market-observed options prices. The method is based on novelly combining the Fourier cosine series method and the Carr-Madan spanning formula. In contrast to the seminal Breeden-Litzenberger formula, which is based on twice differentiating the options prices with respect to the strikes, our method is based on integrating the options prices with respect to available strikes at a given maturity. We employ the Black-Scholes model, constant elasticity of variance model, and the Heston model as data-generating processes in the numerical experiments, and real market data in the empirical experiments. These models demonstrate that the proposed method is accurate, is highly efficient to evaluate, and compares favorably with existing methods in the literature.

A hybrid stochastic differential reinsurance and investment game with bounded memory

This paper investigates a hybrid stochastic differential reinsurance and investment game between one reinsurer and two insurers, including a stochastic Stackelberg differential subgame and a non-zero-sum stochastic differential subgame. The reinsurer, as the leader of the Stackelberg game, can price reinsurance premium and invest her wealth in a financial market that contains a risk-free asset and a risky asset which is described by the constant elasticity of variance (CEV) model. The two insurers, as the followers of the Stackelberg game, can purchase proportional reinsurance from the reinsurer and invest in the same financial market. The competitive relationship between two insurers is modeled by the non-zero-sum game, and their decision-making will consider the relative performance measured by the difference in their terminal wealth. The bounded memory feature is characterized by the wealth process with delay. The purpose of the reinsurer is to maximize the expected utility of her own terminal wealth with delay. The two insurers aim to maximize the expected utility of the combination of her terminal wealth and the relative performance with delay. By using the idea of backward induction and the dynamic programming approach, we derive the equilibrium strategy and value functions explicitly. Then, we provide the corresponding verification theorem. Finally, some numerical examples and sensitivity analysis are presented to demonstrate the effects of model parameters on the equilibrium strategy. We find the delay factor discourages or stimulates investment depending on the length of delay. Moreover, competitive factors between two insurers make their optimal reinsurance-investment strategy interact, and reduce reinsurance demand and reinsurance premium price.

Pricing of Adverse Development Covers Using Option Pricing Methods

The market for Adverse Development Covers and Loss Portfolio Transfers has been growing in the past few years. Despite this growth, reinsurers are still struggling to define a standard method for pricing such covers. In this context, this article aims at providing an innovative method for pricing such contracts. The proposed method is based on the famous Mack model and fits a Constant Elasticity of Variance (CEV) model to the Mack results (expected value and standard deviation) for each future development year of each accident/underwriting year. Having fitted the CEV model, it is possible to estimate the value of the Adverse Development Covers for each accident/underwriting year using standard European option pricing techniques and to compare this valuation with usual non-life insurance valuation techniques.

The equivalent constant-elasticity-of-variance (CEV) volatility of the stochastic-alpha-beta-rho (SABR) model

This study presents new analytic approximations of the stochastic-alpha-beta-rho (SABR) model. Unlike existing studies that focus on the equivalent Black–Scholes (BS) volatility, we instead derive the equivalent constant-elasticity-of-variance (CEV) volatility. Our approach effectively reduces the approximation error in a way similar to the control variate method because the CEV model is the zero vol-of-vol limit of the SABR model. Moreover, the CEV volatility approximation yields a finite value at a zero strike and thus conveniently leads to a small-time asymptotics for the mass at zero. The numerical results compare favorably with the BS volatility approximations in terms of the approximation accuracy, small-strike volatility asymptotics, and no-arbitrage region.

Pricing and calibration of the futures options market: A unified approximation

AbstractThe constant elasticity of variance (CEV) model is widely used in modeling commodity futures prices, but it may not perform well in calibrating corresponding futures options. We consider two variations of the CEV model, that is, CEV with jumps and CEV with regime switching, and compare their performance in calibrating the Chinese futures options market. In particular, we propose a unified framework for pricing American futures options by combining the continuous‐time Markov chain approximation and the dynamic programming method. Results show that the inverse leverage effect in the soybean meal options market can be better described by the CEV regime‐switching model.

Numerical pricing based on fractional Black–Scholes equation with time-dependent parameters under the CEV model: Double barrier options

The empirical observations show that the constant elasticity of variance (CEV) model is a practical approach to capture the implied volatility smile phenomenon. Also, due to the appearance of long-range dependence (or long memory effect) in stock returns or volatility, the fractional Black–Scholes models became particularly important in finance. We will generalize the CEV model to a fractional-CEV model with Jumarie’s fractional model (Jumarie, 2008) to capture the long memory effect in addition to show the negative relationship between stock price and its return volatility. The asset price dynamics of this model follows from a fractional stochastic differential equation, and its volatility is a function of the underlying asset price. We derive the fractional Black–Scholes equation by using the Itoˆ Lemma and fractional Taylor’s series. Previously, Jumarie’s model was used to determine the value of European and American options. In this study, given the importance of the barrier option, we use the proposed model for pricing a European double barrier option. Since most fractional PDEs do not have closed-form analytical solutions, we solve the option pricing problem numerically. Then, we investigate the stability and convergence of the proposed scheme by applying the Fourier analysis. Finally, some numerical results are given in the last section by computing the European double barrier option.

A compact difference scheme for time-fractional Black-Scholes equation with time-dependent parameters under the CEV model: American options

‎The Black-Scholes equation is one of the most important mathematical models in option pricing theory, but this model is far from market realities and cannot show memory effect in the financial market. This paper investigates an American option based on a time-fractional Black-Scholes equation under the constant elasticity of variance (CEV) model, which parameters of interest rate and dividend yield supposed as deterministic functions of time, and the price change of the underlying asset follows a fractal transmission system. This model does not have a closed-form solution; hence, we numerically price the American option by using a compact difference scheme. Also, we compare the time-fractional Black-Scholes equation under the CEV model with its generalized Black-Scholes model as α = 1 and β = 0. Moreover, we demonstrate that the introduced difference scheme is unconditionally stable and convergent using Fourier analysis. The numerical examples illustrate the efficiency and accuracy of the introduced difference scheme.

The sub-fractional CEV model

The sub-fractional Brownian motion (sfBm) is a stochastic process, characterized by non-stationarity in their increments and long-range dependence, considered as an intermediate step between the standard Brownian motion (Bm) and the fractional Brownian motion (fBm). The mixed process, a linear combination between a Bm and an independent sfBm, called mixed sub-fractional Brownian motion (msfBm), keeps the features of the sfBm adding the semi-martingale property for H>3∕4, is a suitable candidate to use in price fluctuation modeling, in particular for option pricing. In this note, we arrive at the European Call price under the Constant Elasticity of Variance (CEV) model driven by a mixed sub-fractional Brownian motion. Empirical tests show the capacity of the proposed model to capture the temporal structure of option prices across different maturities.

Pricing discretely monitored barrier options: When Malliavin calculus expansions meet Hilbert transforms

This paper proposes a novel approach that combines Malliavin calculus based expansions with Hilbert transform techniques to pricing various types of discretely monitored barrier options under jump diffusion models with multifactor stochastic volatility. Malliavin calculus based expansions are often used to deal with multidimensional models but are hard to apply directly in pricing discretely monitored barrier options, while, on the contrary, Hilbert transform techniques proposed by Feng and Linetsky (2008) (Mathematical Finance, 18(3), 337–384) have proved to be particularly useful for pricing discretely monitored barrier options but are difficult to apply directly to multidimensional models. By innovatively making these two methods complement each other, our approach takes advantage of both of them and overcomes their respective limitations. Numerical results suggest that the resulting recursive expansion pricing method is accurate and efficient under a broad range of prevalent option pricing models, including not only affine models such as the Heston-SV model and the Bates-SVJ model but also non-affine models such as the CEV model and the GARCH-SV model with/without jumps.

A note on the option price and ‘Mass at zero in the uncorrelated SABR model and implied volatility asymptotics’

Gulisashvili et al. [Quant. Finance, 2018, 18(10), 1753–1765] provide short term asymptotics for the mass at zero under the uncorrelated stochastic-alpha-beta-rho (SABR) model by approximating the integrated variance with a moment-matched lognormal distribution. We improve the accuracy of the numerical integration by using Gauss–Hermite quadrature. We further obtain the option price by similarly integrating the constant elasticity of variance (CEV) option prices without resorting to the small-strike volatility smile asymptotics of De Marco et al. [SIAM J. Financ. Math., 2017, 8(1), 709–737]. For the uncorrelated SABR model, the new option pricing method is accurate and arbitrage-free across all strike prices.

Optimal consumption and portfolios with the hyperbolic absolute risk aversion preference under the CEV model

This paper studies the optimal investment-consumption decision under the constant elasticity of variance (CEV) model for an individual seeking to maximize the expected utility from cumulative consumption plus the expected utility from terminal wealth. Due to the fact that different individuals may have different risk preferences, we assume that the risk preference of an individual satisfies a hyperbolic absolute risk aversion (HARA) utility function. Generally speaking, power utility function, logarithmic utility function and exponential utility function widely used in investment theory are usually special cases of HARA utility function. By using the principle of dynamic programming and Legendre transform-dual technique, we obtain the explicit expression of the optimal investment-consumption decision. In addition, we derive the results under other utility functions as well and analyze some characteristics of the optimal portfolios and the optimal consumption decisions. A numerical simulation is presented to illustrate our results. Research results suggest that the optimal investment decisions between with consumption behavior and without it have considerable differences.

Robust deep hedging

We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black–Scholes model as well as the constant elasticity of variance (CEV) model as special cases. Based on a general dynamic programming principle, we are able to link the associated nonlinear expectation to a variational form of the Kolmogorov equation which opens the door for fast numerical pricing in the robust framework. The main novelty of the paper is that we propose a deep hedging approach which efficiently solves the hedging problem under parameter uncertainty. We numerically evaluate this method on simulated and real data and show that the robust deep hedging outperforms existing hedging approaches in highly volatile periods.

Foreign exchange rate volatility smiles and smirks

AbstractWe study the implied volatilities of three foreign exchange (FX) option markets: EUD/USD, GBP/USD, and AUD/USD. We find that they are distinct from each other. The implied volatilities of the EUD/USD market tend to be more U‐shaped than those of other markets. Local volatility models such as the constant elasticity of variance (CEV) model and stochastic volatility models, such as the Heston model, may fail to capture this type of convexity. We choose a stochastic‐local volatility model to obtain an implied volatility formula for the corresponding FX options. The formula is given by the CEV formula with additional terms reflecting the (pure) stochastic volatility nature of FX rates. Based on this result, we show that the stochastic‐local volatility model is a suitable universal choice for the pricing of these FX options.

On The Closed Form Strategies of an Investor under the CEV and CIR Processes

In this paper, the explicit solutions of the optimal investment plans of an investor with exponential utility function exhibiting constant absolute risk aversion (CARA) under constant elasticity of variance (CEV) and stochastic interest rate is studied. A portfolio comprising of a risk-free asset modelled by the Cox-Ingersoll-Ross (CIR) process and two risky assets modelled by the CEV process is considered, where the instantaneous volatilities of the two risky assets form a 2 x 2 matrix n = {np,q}2x2 such that nnT is positive definite. Using the power transformation and change of variable approach with asymptotic expansion technique, explicit solutions of the optimal investment plans are found. Moreover, numerical simulations are used to study the effects of the interest rate, elasticity parameter, correlation coefficient and the risk averse coefficient on the optimal investment plans.

Symmetry-based optimal portfolio for a DC pension plan under a CEV model with power utility

In this article, explicit representation of solution for the Hamilton–Jacobi–Bellman (HJB) equation associated with the portfolio optimization problem for an investor who seeks to maximize the expected power (CRRA) utility of the terminal wealth in a defined-contribution pension plan under a constant elasticity of variance model is derived based on the application of the Lie symmetry method to the partial differential equation and its associated terminal condition. Compared with the ingenious ansatz techniques used before, here we present a group theoretical analysis of the terminal value problem for the solution following the algorithmic procedure of the Lie symmetry analysis. It shows that the interesting properties of the group structures of the original HJB equation and its successive similarity reduced equations lead to an elegant resolution of the problem. Moreover, we identify the meaningful range of risk aversion coefficient which is ignored in the previous work. At last, the properties and sensitivity analysis of the derived optimal strategy are demonstrated by numerical simulations and several figures. The method used here is quite general and can be applied to other equations obtained in financial mathematics.

Robust optimal excess-of-loss reinsurance and investment problem with <i>p</i>-thinning dependent risks under CEV model

This paper is devoted to study a robust optimal excess-of-loss reinsurance and investment problem with p -thinning dependent risks for an ambiguity-averse insurer (AAI). Assume that the AAI's wealth process consists of two p -thinning dependent classes of insurance business. The AAI is allowed to purchase excess-of-loss reinsurance and invest in a financial market consisting of one risk-free asset and one risky asset, where risky asset's price follows CEV model. Under the criterion of maximizing the expected exponential utility of AAI's terminal wealth, the explicit expressions of the optimal excess-of-loss reinsurance and investment strategy are derived by employing techniques of stochastic control theory. Moreover, we provide the verification theorem and present some numerical examples to analyze the impacts of parameters on our optimal control strategies.

Robust Deep Hedging

We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black-Scholes model as well as the constant elasticity of variance (CEV) model as special cases. Based on a general dynamic programming principle, we are able to link the associated nonlinear expectation to a variational form of the Kolmogorov equation which opens the door for fast numerical pricing in the robust framework. The main novelty of the paper is that we propose a deep hedging approach which efficiently solves the hedging problem under parameter uncertainty. We numerically evaluate this method on simulated and real data and show that the robust deep hedging outperforms existing hedging approaches, in particular in highly volatile periods.

CEV model equipped with the long-memory

In this paper, we define the mixed fractional Constant Elasticity of Variance (CEV) process exploiting a transfer equation. This transformation enables us to shift non-linearity from the volatility coefficient into the drift one and accordingly, the problem can be more easily studied. We confirm the existence of a unique positive solution to the transfer equation and verify the conditions under which the probability that, on any fixed finite interval, the mixed fractional CEV process does not hit zero tends to 1 (by some simulation, we illustrate more). Next, some parameters of the process are estimated and finally, we examine the ability of this process to model a financial market.

An efficient numerical method for pricing American put options under the CEV model

The constant elasticity of variance (CEV) model is a practical approach to option pricing by fitting to the implied volatility smile. However, pricing American options is computationally intensive because no analytical formulas are available. In this paper, we present numerical methods to find the optimal exercise boundary with respect to an American put option under the CEV model. This problem corresponds to the free boundary (also called an optimal exercise boundary) problem for a partial differential equation. We use a transformed function that has Lipschitz character near the optimal exercise boundary to determine the optimal exercise boundary. After determining the optimal exercise boundary, we calculate American put option values under the CEV model using the finite-difference method. Finally, we use the proposed numerical method to obtain several results that are then compared with the results of other methods.

A Simple Closed-Form Approximation for Constant Elasticity of Variance Spread Options

By applying the Lie-Trotter operator splitting method and the idea of the WKB method, we have developed a simple, accurate and efficient analytical approximation for pricing the constant elasticity of variance (CEV) spread options. The derived option price formula bears a striking resemblance to Kirk's formula of the Black-Scholes spread options. Illustrative numerical examples show that the proposed approximation is not only extremely fast and robust, but also it is remarkably accurate for typical volatilities and maturities of up to two years.