Academic literature on the topic 'STOCK CALL OPTIONS'

Even though option pricing and its market activities are not new, in Ghana the idea of trading options is yet to be realized. One popular method in pricing options is known as Black-Scholes-Merton option pricing model. Even though option pricing activities are not currently happening on the Ghana Stock Exchange, authors looked at the possibilities and preparedness of the GES to start trading such financial instrument. The main objective of this study therefore was to know how Black-Scholes-Merton model could be used to help in appropriate option value and undertake a risk assessment of stocks on the exchange. This study basically used the black-Scholes formula in calculating the call and put option prices for 28 companies listed GES. The results showed that the price of call option for 18 out of 28 listed stocks showed a value of zero. Again, only seven (7) companies had a value for both call and put options. This means stocks of 21 companies cannot be an underlying asset for trading financial derivatives. Reason for this performance of stock is due to low volatility. The study recommends that policies to increase volatility on the stock market should be put in place in other to make option pricing possible.

AbstractThis study examines whether the gambling behavior of investors affects volume and volatility in financial markets. Focusing on the options market, we find that the ratio of call option volume relative to total option volume is greatest for stocks with return distributions that resemble lotteries. Consistent with the theoretical predictions of Stein (1987), we demonstrate that gambling-motivated trading in the options market influences future spot price volatility. These results not only identify a link between lottery preferences in the stock market and the options market, but they also suggest that lottery preferences can lead to destabilized stock prices.

AbstractDeviations from put-call parity contain information about future stock returns. Using the difference in implied volatility between pairs of call and put options to measure these deviations, we find that stocks with relatively expensive calls outperform stocks with relatively expensive puts by 50 basis points per week. We find both positive abnormal performance in stocks with relatively expensive calls and negative abnormal performance in stocks with relatively expensive puts, which cannot be explained by short sale constraints. Rebate rates from the stock lending market directly confirm that our findings are not driven by stocks that are hard to borrow. The degree of predictability is larger when option liquidity is high and stock liquidity low, while there is little predictability when the opposite is true. Controlling for size, option prices are more likely to deviate from strict put-call parity when underlying stocks face more information risk. The degree of predictability decreases over the sample period. Our results are consistent with mispricing during the earlier years of the study, with a gradual reduction of the mispricing over time.

The Option is widely applied in the financial sector. The Black-Scholes-Merton model is often used in calculating option prices on a stock price movement. The model uses geometric Brownian motion which assumes that the data is normally distributed. However, in reality, stock price movements can cause sharp spikes in data, resulting in nonnormal data distribution. So we need a stock price model that is not normally distributed. One of the fastest growing stock price models today is the process exponential model. The process has the ability to model data that has excess kurtosis and a longer tail (heavy tail) compared to the normal distribution. One of the members of the process is the Variance Gamma (VG) process. The VG process has three parameters which each of them, to control volatility, kurtosis and skewness. In this research, the secondary data samples of options and stocks of two companies were used, namely zoom video communications, Inc. (ZM) and Nokia Corporation (NOK). The price of call options is determined by using closed form equations and Monte Carlo simulation. The Simulation was carried out for various values until convergent result was obtained.

Probability of Exercise of Option To estimate the risk the investors take when investing their money in stocks or stock options one must study if the option is exercised or not. From the point of view of a call option writer, especially those uncovered, one should study the probability of the exercise of option by a holder. The method presented in the paper enables to estimate risk connected with investment in options. In the assessment of risk that is born when investing money in stocks or options it is interesting whether the option will be exercised or not. From the writers' point of view, particularly those without coverage, it could be necessary to analyse probability of the exercise of options by buyers. The described method allows to assess at any time of call option duration whether the investor can be certain of the result of their investment. It can be applied also for the option strategies. In the paper the author has attempted to estimate the risk of call option and to estimate the probability of profit achievement in the case of long strangle option application. Investors using option strategies are able to do preliminary analysis of options and to minimize risk of their investment through choosing a proper date and price of exercise.

In this study, we assume that stock prices follow piecewise geometric Brownian motion, a variant of geometric Brownian motion except the ex-dividend date, and find pricing formulas of American call options. While piecewise geometric Brownian motion can effectively incorporate discrete dividends into stock prices without losing consistency, the process results in the lack of closed-form solutions for option prices. We aim to resolve this by providing analytical approximation formulas for American call option prices under this process. Our work differs from other studies using the same assumption in at least three respects. First, we investigate the analytical approximations of American call options and examine European call options as a special case, while most analytical approximations in the literature cover only European options. Second, we provide both the upper and the lower bounds of option prices. Third, our solutions are equal to the exact price when the size of the dividend is proportional to the stock price, while binomial tree results never match the exact option price in any circumstance. The numerical analysis therefore demonstrates the efficiency of our method. Especially, the lower bound formula is accurate, and it can be further improved by considering second order approximations although it requires more computing time.

This paper investigates the characteristics of implied risk-neutral distributions separately derived from Taiwan stock index call and put options prices. Differences in risk-neutral skewness and kurtosis between call and put options indicate deviations from put-call parity. We find that the sentiment effect is significantly related to differences between call and put option prices. Our results suggest the differential impact of investor sentiment and consumer sentiment on call and put option traders' expectations about underlying asset prices. Moreover, rational and irrational sentiment components have different influences on call and put option traders' beliefs.

<span>This study examines the response of the options market to new security registrations and issuances. Two methods are employed to gauge option market response. The first involves the calculation of implied standard deviations (ISDs) around primary security registration and issuance dates. The second employs American put-call parity to simultaneously evaluate the relationship between put, call and stock prices around these dates. We find a statistically significant mean decrease in relative ISD five trading days before announcement of new stock issuances and a statistically significant mean increase in relative ISD one day before announcement of new debt issuances. Put-call parity tests provide evidence that the options market anticipates stock price decreases prior to announcements of both stock and debt issuance.</span>

We use the basic Black-Scholes equation for pricing European stock options but we allow some of the parameters in the model to be uncertain and we model this uncertainty using fuzzy numbers. We compute the fuzzy number for the call value of option with and without uncertain dividends. This fuzzy set displays the uncertainty in the option's value due to the uncertainty in the input values to the model. We also correct an error in a recent paper which also fuzzified the Black-Scholes equation.


 
 Black-Scholes option pricing model is used to decide theoretical price of different Options contracts in many stock markets in the world. In can find many generalizations of BS model by modifying some assumptions of classical BS model. In this paper we compared two such modified Black-Scholes models with classical Black-Scholes model only for Indian option contracts. We have selected stock options form 5 different sectors of Indian stock market. Then we have found call and put option prices for 22 stocks listed on National Stock Exchange by all three option pricing models. Finally, we have compared option prices for all three models and decided the best model for Indian Options.
 Motivation/Background:
 In 1973, two economists, Fischer Black, Myron and Robert Merton derived a closed form formula for finding value of financial options. For this discovery, they got a Nobel prize in Economic science in 1997. Afterwards, many researchers have found some limitations of Black-Scholes model. To overcome these limitations, there are many generalizations of Black-Scholes model available in literature. Also, there are very limited study available for comparison of generalized Black-Scholes models in context of Indian stock market. For these reasons we have done this study of comparison of two generalized BS models with classical BS model for Indian Stock market.
 Method:
 First, we have selected top 5 sectors of Indian stock market. Then from these sectors, we have picked total 22 stocks for which we want to compare three option pricing models. Then we have collected essential data like, current stock price, strike price, expiration time, rate of interest, etc. for computing the theoretical price of options by using three different option pricing formulas. After finding price of options by using all three models, finally we compared these theoretical option price with market price of respected stock options and decided that which theoretical price has less RMSE error among all three model prices.
 Result:
 After going through the method described above, we found that the generalized Black-Scholes model with modified distribution has minimum RMSE errors than other two models, one is classical Black-Scholes model and other is Generalized Black-Scholes model with modified interest rate.