Περίληψη :  This thesis suggests a new methodology of retrieving the risk neutral density from option prices and to reexamine the information content of them based on estimates of the risk neutral moments directly retrieved by the data. To obtain the risk neutral moments, it provides exact, closedform formulas of them of any order for the future price of the underlying asset and its logreturn based on a set of European option prices. This approach of retrieving the risk neutral moments is found to perform very well even for small option price data sets. The estimates of the risk neutral density are based on an approximation of the true density through a Ctype GramCharlier series expansion. In contrast to the standard Atype GramCharlier approximation, this type of expansion is of exponential form which guarantees that will give positive probabilities.It can also allows for very large deviations from normality of the underlying stock price, or the logreturn distribution. The thesis investigates the information content of risk neutral moments about the future values of the realized mean and volatility in a freemodel manner. It is based on a general relationship which holds between the risk neutral moments and their physical counterparts which is derived by ruling out profitable arbitrage opportunities between the stock and option price markets.This relationship adjusts the slope of the risk neutral moments (or cumulants) for risk premium effects which can not be hedged out in the market, and thus they need to be priced. In the light of this, it suggests a new regression framework to estimate the degree of risk aversion and to predict the future values of the physical mean and volatility adjusted for the effects of the risk premium.

