SOLVING THE IVANCEVIC OPTIONS PRICING MODEL WITH THE NUMERICAL METHOD SOME BLAISE-ABBO (SBA)

2021 ◽  
Vol 24 (2) ◽  
pp. 133-143
Author(s):  
Paré Daouda ◽  
Kassiénou Lamien ◽  
Blaise Somé ◽  
Youssouf Paré ◽  
Longin Somé
Keyword(s):  
Numerical Method ◽  
Options Pricing ◽  
Pricing Model

scholarly journals Assessing the Option to Abandon an Investment Project by the Binomial Options Pricing Model

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Salvador Cruz Rambaud ◽  
Ana María Sánchez Pérez
Keyword(s):  
Real Option ◽  
Net Present Value ◽  
Investment Project ◽  
Mathematical Expression ◽  
Options Pricing ◽  
Pricing Model ◽  
Operational Flexibility ◽  
Present Value ◽  
Project Appraisal ◽  
Financial Options

Usually, traditional methods for investment project appraisal such as the net present value (hereinafter NPV) do not incorporate in their values the operational flexibility offered by including a real option included in the project. In this paper, real options, and more specifically the option to abandon, are analysed as a complement to cash flow sequence which quantifies the project. In this way, by considering the existing analogy with financial options, a mathematical expression is derived by using the binomial options pricing model. This methodology provides the value of the option to abandon the project within one, two, and in general n periods. Therefore, this paper aims to be a useful tool in determining the value of the option to abandon according to its residual value, thus making easier the control of the uncertainty element within the project.

Cooperative Governance of Inter-provincial air pollution based on a Black–Scholes Options Pricing Model

2020 ◽  
Vol 277 ◽  
pp. 124031
Author(s):  
Jian Xue ◽  
Shengnan Zhao ◽  
Laijun Zhao ◽  
Di Zhu ◽  
Shuxin Mao
Keyword(s):  
Air Pollution ◽  
Options Pricing ◽  
Pricing Model ◽  
Black Scholes ◽  
Cooperative Governance

Ensuring More Is Better: On the Simultaneous Application of Stock and Options Data to Estimate the GARCH Options Pricing Model

2018 ◽  
Keyword(s):  
Option Pricing ◽  
Stock Prices ◽  
Options Pricing ◽  
Asymptotic Result ◽  
Option Prices ◽  
Options Market ◽  
Pricing Model ◽  
Simultaneous Application ◽  
Option Values ◽  
Option Pricing Model

The most common approach in fitting option pricing models to market data is first to make an assumption about the underlying asset’s returns process and then develop an option pricing model for that process that is tested against market option prices. The returns process is estimated from historical data, option values are computed, and then compared against a cross-section of prices from the options market. Unfortunately, this often does not work well, and plainly it is inefficient in its use of the data. However, efforts to combine returns data from the asset market and prices from the options market into a single estimation have also not had much success. In this article, Chang, Cheng, and Fuh propose a new procedure to combine data from both markets in the estimation, in which options are assumed to be subject to random pricing noise relative to model values. The additional slack gives the estimator better ability to match prices in both markets. The article contrasts the performance of the full model approach with an approach that only uses stock prices or options prices to fit an option pricing model based on an underlying GARCH process. The value of the combined approach is demonstrated both theoretically as an asymptotic result in the model and also in a Monte Carlo simulation.

UNCERTAINTY IN PRICING TRADABLE OPTIONS

2003 ◽  
Vol 06 (02) ◽  
pp. 103-117 ◽  
Author(s):  
JORGE R. SOBEHART ◽  
SEAN C. KEENAN
Keyword(s):  
Interest Rates ◽  
Market Equilibrium ◽  
Expected Value ◽  
Options Pricing ◽  
Options Market ◽  
Pricing Model ◽  
Random Delays ◽  
Black Scholes Model ◽  
Black Scholes ◽  
Random Fluctuations

In this paper we introduce an options pricing model consistent with the level of uncertainty observed in the options market. By assuming that the price at which an option can be traded is intrinsically uncertain, either because of the inability to hedge continuously or because of errors in the estimation of the security's volatility and interest rates, random delays in the execution of orders or information deficiencies, we show that the Black-Scholes model produces a biased estimate of the expected value of tradable options. Information deficiencies lead to a call-put relationship that reduces to the standard call-put expression on average but shows random fluctuations consistent with the concept of market equilibrium. The same information deficiencies can contribute to the volatility skew that affects the Black-Scholes model.

scholarly journals EFFECTIVE AND SIMPLE VWAP OPTIONS PRICING MODEL

2014 ◽  
Vol 17 (06) ◽  
pp. 1450036 ◽  
Author(s):  
ALEXANDER BURYAK ◽  
IVAN GUO
Keyword(s):  
Market Price ◽  
Weighted Average ◽  
Incomplete Market ◽  
Gamma Process ◽  
Options Pricing ◽  
Average Price ◽  
Pricing Model ◽  
Pricing Models ◽  
Gamma Distributions

Volume weighted average price (VWAP) options are a popular security type in many countries, but despite their popularity very few pricing models have been developed so far for VWAP options. This can be explained by the fact that the VWAP pricing problem is set in an incomplete market since there is no underlying with which to hedge the volume risk, and hence there is no uniquely defined price. Any price, which is obtained will include a market price of volume risk which must be determined from the corresponding volume statistics. Our analysis strongly supports the hypothesis that the empirical volume statistics of ASX equities can be described reasonably well by fitted gamma distributions. Based on this observation we suggest a simple gamma process-based model that allows for the exact analytic pricing of VWAP options in a rather straightforward way.

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