Pricing American call options using the Black–Scholes equation with a nonlinear volatility function

2020 ◽  
Author(s):  
Maria do Rosario Grossinho ◽  
Yaser Fagan Kord ◽  
Daniel Sevcovic
Keyword(s):  
Call Options ◽  
Black Scholes ◽  
Volatility Function

scholarly journals OPTIONS WRITTEN ON STOCKS WITH KNOWN DIVIDENDS

2004 ◽  
Vol 07 (07) ◽  
pp. 901-907
Author(s):  
ERIK EKSTRÖM ◽  
JOHAN TYSK
Keyword(s):  
Stock Price ◽  
Option Price ◽  
Option Prices ◽  
Strike Price ◽  
Call Options ◽  
Alternative Approach ◽  
Black Scholes ◽  
Market Practice ◽  
The Difference ◽  
Risk Free Rate

There are two common methods for pricing European call options on a stock with known dividends. The market practice is to use the Black–Scholes formula with the stock price reduced by the present value of the dividends. An alternative approach is to increase the strike price with the dividends compounded to expiry at the risk-free rate. These methods correspond to different stock price models and thus in general give different option prices. In the present paper we generalize these methods to time- and level-dependent volatilities and to arbitrary contract functions. We show, for convex contract functions and under very general conditions on the volatility, that the method which is market practice gives the lower option price. For call options and some other common contracts we find bounds for the difference between the two prices in the case of constant volatility.

scholarly journals Accurate radial basis functions technique for competitive and efficient solutions of non-linear Black-Scholes equations

2020 ◽  
Vol 20 (4) ◽  
pp. 60-83
Author(s):  
Vinícius Magalhães Pinto Marques ◽  
Gisele Tessari Santos ◽  
Mauri Fortes
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Efficient Solutions ◽  
Basis Functions ◽  
Adaptive Scheme ◽  
Call Options ◽  
Black Scholes ◽  
Non Linear ◽  
Radial Basis ◽  
Complex Models

ABSTRACTObjective: This article aims to solve the non-linear Black Scholes (BS) equation for European call options using Radial Basis Function (RBF) Multi-Quadratic (MQ) Method.Methodology / Approach: This work uses the MQ RBF method applied to the solution of two complex models of nonlinear BS equation for prices of European call options with modified volatility. Linear BS models are also solved to visualize the effects of modified volatility.  Additionally, an adaptive scheme is implemented in time based on the Runge-Kutta-Fehlberg (RKF) method.

scholarly journals PENENTUAN HARGA KONTRAK OPSI KOMODITAS EMAS MENGGUNAKAN METODE POHON BINOMIAL

2017 ◽  
Vol 6 (2) ◽  
pp. 99
Author(s):  
I GEDE RENDIAWAN ADI BRATHA ◽  
KOMANG DHARMAWAN ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Option Prices ◽  
European Option ◽  
Binomial Tree ◽  
Call Options ◽  
Option Contracts ◽  
Put Option ◽  
Black Scholes ◽  
Tree Method

Holding option contracts are considered as a new way to invest. In pricing the option contracts, an investor can apply the binomial tree method. The aim of this paper is to present how the European option contracts are calculated using binomial tree method with some different choices of strike prices. Then, the results are compared with the Black-Scholes method. The results obtained show the prices of call options contracts of European type calculated by the binomial tree method tends to be cheaper compared with the price of that calculated by the Black-Scholes method. In contrast to the put option prices, the prices calculated by the binomial tree method are slightly more expensive.

scholarly journals Pricing Perpetual Put Options by the Black–Scholes Equation with a Nonlinear Volatility Function

2017 ◽  
Vol 24 (4) ◽  
pp. 291-308
Author(s):  
Maria do Rosário Grossinho ◽  
Yaser Kord Faghan ◽  
Daniel Ševčovič
Keyword(s):  
Put Options ◽  
Black Scholes ◽  
Volatility Function

Algorithm for Determining the Volatility Function in the Black–Scholes Model

2019 ◽  
Vol 59 (10) ◽  
pp. 1753-1758 ◽  
Author(s):  
V. M. Isakov ◽  
S. I. Kabanikhin ◽  
A. A. Shananin ◽  
M. A. Shishlenin ◽  
S. Zhang
Keyword(s):  
Black Scholes Model ◽  
Black Scholes ◽  
Volatility Function

scholarly journals Valuation of European Call Options Using Wavelet-Based Pricing Model and Black-Scholes Pricing Model

2019 ◽  
Vol 5 (5) ◽  
pp. 92
Author(s):  
Sigei Sheila Chepkorir ◽  
Anthony Gichuhi Waititu ◽  
Jane Aduda Akinyi
Keyword(s):  
Pricing Model ◽  
Call Options ◽  
Black Scholes

scholarly journals Pricing energy contracts under regime switching time-changed Levy Processes

2021 ◽  
Author(s):  
Konrad Gajewski
Keyword(s):  
Markov Chain ◽  
Characteristic Function ◽  
Continuous Time ◽  
Regime Switching ◽  
Switching Time ◽  
Continuous Time Markov Chain ◽  
Energy Data ◽  
Varying Parameters ◽  
Call Options ◽  
Black Scholes

The failures of the popular Black-Scholes-Merton (BSM) model led to an interest in new, robust models which could more accurately model the behavior of historical prices. We consider one such model, the regime switching time-changed Levy process, which builds upon the BSM model by incorporating jumps through a random clock, as well as randomly varying parameters according to a continuous-time Markov chain. We develop the characteristic function as well as two methods for pricing European call options. Finally, we estimate the parameters of the model by incorporating historic energy data and option quotes using a variety of methods.

Modeling of implied volatility surfaces of nifty index options

2019 ◽  
Vol 06 (03) ◽  
pp. 1950028 ◽  
Author(s):  
Mihir Dash
Keyword(s):  
Implied Volatility ◽  
Strike Price ◽  
Index Options ◽  
Put Options ◽  
Call Options ◽  
Merton Model ◽  
Implied Volatility Surface ◽  
Black Scholes ◽  
Volatility Surface ◽  
Implied Volatility Surfaces

The implied volatility of an option contract is the value of the volatility of the underlying instrument which equates the theoretical option value from an option pricing model (typically, the Black–Scholes[Formula: see text]Merton model) to the current market price of the option. The concept of implied volatility has gained in importance over historical volatility as a forward-looking measure, reflecting expectations of volatility (Dumas et al., 1998). Several studies have shown that the volatilities implied by observed market prices exhibit a pattern very different from that assumed by the Black–Scholes[Formula: see text]Merton model, varying with strike price and time to expiration. This variation of implied volatilities across strike price and time to expiration is referred to as the volatility surface. Empirically, volatility surfaces for global indices have been characterized by the volatility skew. For a given expiration date, options far out-of-the-money are found to have higher implied volatility than those with an exercise price at-the-money. For short-dated expirations, the cross-section of implied volatilities as a function of strike is roughly V-shaped, but has a rounded vertex and is slightly tilted. Generally, this V-shape softens and becomes flatter for longer dated expirations, but the vertex itself may rise or fall depending on whether the term structure of at-the-money volatility is upward or downward sloping. The objective of this study is to model the implied volatility surfaces of index options on the National Stock Exchange (NSE), India. The study employs the parametric models presented in Dumas et al. (1998); Peña et al. (1999), and several subsequent studies to model the volatility surfaces across moneyness and time to expiration. The present study contributes to the literature by studying the nature of the stationary point of the implied volatility surface and by separating the in-the-money and out-of-the-money components of the implied volatility surface. The results of the study suggest that an important difference between the implied volatility surface of index call and put options: the implied volatility surface of index call options was found to have a minimum point, while that of index put options was found to have a saddlepoint. The results of the study also indicate the presence of a “volatility smile” across strike prices, with a minimum point in the range of 2.3–9.0% in-the-money for index call options and of 10.7–29.3% in-the-money for index put options; further, there was a jump in implied volatility in the transition from out-of-the-moneyness to in-the-moneyness, by 10.0% for index call options and about 1.9% for index put options.

scholarly journals Reconstruction of the Time-Dependent Volatility Function Using the Black–Scholes Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Yuzi Jin ◽  
Jian Wang ◽  
Sangkwon Kim ◽  
Youngjin Heo ◽  
Changwoo Yoo ◽  
...  
Keyword(s):  
Cost Function ◽  
Piecewise Linear ◽  
Reconstruction Algorithm ◽  
Descent Method ◽  
Time Dependent ◽  
Steepest Descent Method ◽  
Market Values ◽  
Black Scholes ◽  
Volatility Function ◽  
The Cost

We propose a simple and robust numerical algorithm to estimate a time-dependent volatility function from a set of market observations, using the Black–Scholes (BS) model. We employ a fully implicit finite difference method to solve the BS equation numerically. To define the time-dependent volatility function, we define a cost function that is the sum of the squared errors between the market values and the theoretical values obtained by the BS model using the time-dependent volatility function. To minimize the cost function, we employ the steepest descent method. However, in general, volatility functions for minimizing the cost function are nonunique. To resolve this problem, we propose a predictor-corrector technique. As the first step, we construct the volatility function as a constant. Then, in the next step, our algorithm follows the prediction step and correction step at half-backward time level. The constructed volatility function is continuous and piecewise linear with respect to the time variable. We demonstrate the ability of the proposed algorithm to reconstruct time-dependent volatility functions using manufactured volatility functions. We also present some numerical results for real market data using the proposed volatility function reconstruction algorithm.

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