Simplified Option Selection Method

مجلة رؤى اقتصادية, 2018

The options pricing on financial assets represents a subject of great interest to academics and practitioners in the financial markets; it is also a phenomenon that has preoccupied specialists in financial and mathematics domains. The Black-Sholes model (1973) is the benchmark model of many models; this model provides us with a basic tool for the pricing option contracts traded in markets. Moreover, it was based on many assumptions that are the stability of volatility, the risk free rate, and the normal distribution. The main objective of the present study is the exhibition and the interpretation methods that are related to the assessment and pricing of options on financial stocks market through the comparison between the theoretical options prices under Black-Scholes model, Monte Carlo Simulation method, and the current option prices on market; in addition to the validity test of both models in order to predict the market prices by an empirical study for the period from 26 December 2013 to 08 May 2014, with daily data using R software and its packages. Finally, It was found that the Black-Sholes model does not perform when the volatility is higher in both periods 6 and 9 months, but for one year the B-S model proved its ability to predict the current prices with a positive relationship. Other findings highlighted the outperformance of the Monte Carlo Simulation Method to predict the current price only when the volatility is lower for both periods 6 and 9 months.

Journal of Banking and Financial Economics

Investment behaviour, techniques and choices have evolved in the options markets since the launch of options trading in 1973. Today, we are entering the field of Big Data and the explosion of information, which has become the main feature of science, impacts investors' decisions and their trading position, particularly in the financial markets. Our paper aims to testing the effectiveness of the most popular options pricing models , which are the Monte Carlo simulation method, the Binomial model, and the benchmark model; the Black-Scholes model, when we ignore/take on account the Moneyness categories and different time to maturities; five months, one year, and two years, in addition to comparing these models, we will then test the effect of each model on the prediction of the current options prices, using the regression analysis, and the Nifty50 option index during the period of 25/07/2014 to 30/06/2016. The result shows that all models are overpriced in all Moneyness categories with a high level of volatility in In-the money category, other finding concludes that the Monte Carlo Simulation method is outperforming when the volatility is lower, while the Black-Sholes model and the Binomial model are outperforming in the entire sample with ignoring the Moneyness.

GUB Journal of Science and Engineering, 2021

Our main objective of this paper is to introduce four individual techniques of pricing options; the techniques are Binomial method, Trinomial method, Monte Carlo simulation and Black-Scholes-Merton model. Because they play a significant role in option valuation of stock price dynamics, risk managements as well as stock market. In this paper, we briefly discuss all these four methods with their properties and behavior. We also focused on numerical technique for the higher accuracy of option pricing and compare them graphically. We use the Computer Algebra System (CAS) Python (Edition 2019.3.1) for this purpose. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 1-7

This paper analyses the Black-Scholes' and Heston Option Pricing Model. We discuss the concept of historical volatility in the two Models. We compare the two models for the parameter-'Volatility'. A mathematical tool, UMBRAE (Unscaled Mean Bounded Relative Absolute Error) is used to compare the two models for historical volatility while pricing European call options. Real data from NSE (National Stock Exchange) is considered for three different sectors like-Banking, Automobiles and, Pharmaceuticals for comparison through Moneyness (which is defined as the percentage difference of stock price and strike price) and Time-To-Maturity. Mathematical software-Matlab is used for all mathematical calculations.

Journal of Computational Finance, 2001

The author describes a modi®ed binomial method that provides a simple and uni®ed framework for the valuation of various kinds of Asian options (American or European, arithmetic or geometric, ®xed or¯oating strike, discrete or continuous sampling and dividends, and partial Asians). The greeks can also be calculated accurately and stably. The method is a re®nement of that of , where at each node of a standard binomial tree one also considers a table of possible values of the average. To avoid the exponential explosion of the size of this table in the arithmetic average case, one considers a smaller set of representative values for the average, interpolates when necessary, and otherwise uses standard backward recursion to value the option. In this paper, an ef®cient implementation of this idea is presented. In particular, option values are insured to be smooth as a function of the number N of binomial time periods, so that Richardson extrapolation can be applied to eliminate 1=N (and sometimes higherorder) corrections, dramatically increasing the speed of the method. Detailed checks and illustrations are provided, showing that this approach can achieve any desired level of accuracy for convection-or diffusion-dominated regimes and for long or short maturities. It is typically much faster than standard PDE and Monte Carlo approaches.

investigaciones económicas, 2005

This paper presents a comparison of alternative option pricing models based neither on jump-di usion nor stochastic volatility data generating processes. We assume either a smooth volatility function of some previously defined explanatory variables or a model in which discrete-based observations can be employed to estimate both path-dependence volatility and the negative correlation between volatility and underlying returns. Moreover, we also allow for liquidity frictions to recognize that underlying markets may not be fully integrated. The simplest models tend to present a superior out-of sample performance and a better hedging ability, although the model with liquidity costs seems to display better in-sample behavior. However, none of the models seems to be able to capture the rapidly changing distribution of the underlying index return or the net buying pressure characterizing option markets.

Majority of the traders lose money whilst trading in options owing to market speculation, emotional trading and lack of risk management. The purpose of this research paper is to introduce an automated way of trading in options in order to tackle these problems. To achieve this, we have adopted some basic option strategies namely Covered Call, Protective Put and Covered Strangle. These strategies have been tested over a period of 5 years (2015-2019) for a set of stocks in the U.S options market.

Research Papers in Economics, 2008

In this contribution, we consider options written on stocks which pay cash dividends. Dividend payments have an effect on the value of options: high dividends imply lower call premia and higher put premia. While exact solutions to problems of evaluating both European and American call options and European put options are available in the literature, for American-style put options early exercise may be optimal at any time prior to expiration even in the absence of dividends. In this case numerical techniques, such as lattice approaches, are required. Discrete dividends produce a shift in the tree; as a result, the tree is no longer reconnecting beyond any dividend date. Methods based on non-recombining trees give consistent results, but they are computationally expensive. We analyze binomial algorithms and performed some empirical experiments.

European Journal of Operational Research, 2005

This paper implements a model setup in Muzzioli and Torricelli [Int. J. Intell. Syst. 17 (6) -594] for deriving implied trees and pricing options when the put-call parity is not fulfilled. The model basically extends Derman and KaniÕs [Risk 7 (2) (1994) 32-39], whereby call (put) prices are also used in the lower (upper) part of the tree thus exploiting the information content of both call and put prices. The DAX-index option market is chosen for this application because it is a relatively new European market where short-selling restrictions may induce put-call parity violations and the nature of the option (European) and of the underlying (dividends reinvested in the index) avoid some estimation problems. In order to test the pricing fit of the model, a non-linear optimisation procedure is proposed to estimate a unique implied tree which allows a comparison between the model prices, Derman and KaniÕs and market prices. The results suggest that the MT model improves the pricing.

Physica D: Nonlinear Phenomena, 2011

Non-parametric option pricing models, such as artificial neural networks, are often found to outperform their parametric counterparts in empirical option pricing exercises. In this context, non-parametric models are viewed as more flexible and amenable to adaptive learning. However, the main drawback of non-parametric approaches is their lack of stability, which is detrimental to out-of-sample performance. This is the key reason why one may prefer a parsimonious parametric model. This paper proposes a parametric Takagi-Sugeno-Kang (TSK) fuzzy rule-based option pricing model that requires only a small number of rules to describe highly complex non-linear functions. The findings for this data-driven approach indicate that the TSK model presents a robust option pricing tool that is superior to an array of well-known parametric models from the literature. In addition, its predictive performance is consistently no worse than that of a non-parametric feedforward neural network model.