Derivatives and Speculative Bubbles

A market crash is a period of financial instability that generally results in a sharp depreciation of financial assets. The latest global financial crisis of 2008 affected world economy in many ways, therefore, the reasons of crashes should be analysed in order to prevent future crashes and minimize loses. The current paper investigates the relation between financial innovations in the form of derivatives and market crashes. The methodology is to apply the LPPL model to predict possible crashes and to test market conditions. The Sornette fitting procedure is applied in order to get the consistent model estimates. The empirical research finds out the applicability of the LPPL model to predict historical crashes within the periods of relatively low price volatility. The model testing on historical crashes shows that the LPPL predicts crashes with the lowest RMSE in the middle of XX century; while the model outcomes related to the crash of 2008 envisage the low forecasting power. The author describes the decrease in forecasting power with relation to financial innovations. The author states that learning set choice affect substantially the model outcomes. The learning set should be choose considering the lowest possible volatility period that starts from the local price minimum.