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Title: Estimating higher distribution moments with high frequency data Authors:  Manuel Schmid - TU Dresden (Germany) [presenting]
Abstract: In standard return modelling approaches, returns are often assumed to follow a normal distribution. This assumption implies zero skewness as well as a zero excess kurtosis. Both of these implications do not correspond to empirical observation and eventually lead to problems e.g. in financial risk management. On the other side, the typical non-parametric estimation of these values requires a huge amount of data to be reliable. For this reason, it is advisable to exploit the availability of high frequency data and construct estimators in the fashion of the well-known realized variance. A previous estimation approach is extended to non-martingale price processes. On the basis of Monte Carlo simulations, we show that our estimators are unbiased and consistent when the underlying price process can be modelled as a stochastic volatility jump diffusion process. Distribution properties of the estimators are discussed.