1,721,152 research outputs found
The performance of covered calls and protective puts
Covered calls and protective puts are amongst the most popular options trading strategies, and their performance has been the subject of a large number of empirical investigations. This paper argues that whether or not such strategies raise or lower the expected return of the investor depends on the values of a number of forecast parameters, and so is uncertain when the position is initiated. Thus, however many previous empirical studies have been conducted, they are not definitive. In fact, these studies have shown that, in practice, covered calls and protective puts can generate both profits and losses. However, such strategies do lower the variance of returns, and this is supported by the previous studies. Of course, in the context of financial instruments such as options, the variance may not be an appropriate measure of ris
Is the forward rate for the Greek drachma unbiased? A VECM analysis with both overlapping and non-overlapping data
Black-Scholes versus artificial neural networks in pricing FTSE 100 options
This paper compares the performance of Black-Scholes with an artificial neural network (ANN) in pricing European style call options on the FTSE 100 index. It is the first extensive study of the performance of ANNs in pricing UK options, and the first to allow for dividends in the closed-form model. For out-of-the-money options, the ANN is clearly superior to Black-Scholes. For in-the-money options, if the sample space is restricted by excluding deep in-the-money and long maturity options (3.4% of total volume), the performance of the ANN is comparable with that of Black-Scholes. The superiority of the ANN is a surprising result, given that European style equity options are the home ground of Black-Scholes, and suggests that ANNs may have an important role to play in pricing other options for which there is either no closed-form model, or the closed-form model is less successful than Black-Scholes for equity options
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