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This chapter proposes a nonparametric estimator of the risk neutral density (RND) based on cross-sectional European option prices. The authors recast the arbitrage-free equation for option pricing as a functional linear regression model where the regressor is a curve and the independent variable is a scalar corresponding to the option price. Then, the authors show that the RND can be viewed as the solution of an ill-posed integral equation. To estimate the RND, the authors use an iterative method called Landweber-Fridman (LF). Then, the authors establish the consistency and asymptotic normality of the estimated RND. These results can be used to construct a confidence interval around the curve. Finally, some Monte Carlo simulations and application to the S&P 500 options show that this method performs well compared to alternative methods.

In this research, we analyze the impact of catastrophe events on risk-neutral densities which can be implied from European option markets. As catastrophe events we consider the destruction of the nuclear power plant at Fukushima and the downgrading of U.S. sovereign debt in 2011. In an event study, we analyze the impact on European blue chip index options traded at EUREX. We find that after a short adaption period, probability mass of especially risk-neutral density functions derived from long-term options is shifted toward the right side. Thus, very good states of the economy become more expensive indicating higher prices for deep out-of-the-money options. This signifies that there has been speculation on a recovery of the German stock market after the shocks.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.

In terms of notional value outstanding, derivatives markets declined in both over-the-counter and exchange-traded transactions during the 2007–2009 global financial crisis (GFC) period, as counterparty and credit concerns became pre-eminent. However, during the 2010–2011 second stage of the GFC, markets rebounded and by June 2011 outstandings reached new levels which highlight the importance these contracts continue to play in the day-to-day risk management and trading activities of corporations and financial intermediaries. The bulk of the contracts traded are interest rate-related instruments and are denominated in either US dollars or Euro. Credit-related instruments remain an important market segment, although outstandings remain at pre-crisis period levels. Of particular concern for regulators is the role of non-bank financial intermediaries, which are the main counterparty to derivatives transactions. While their share of the market remains unchanged over the last decade, outstandings overall have increased more than fourfold. The present volume considers the issues that participants face in today's derivatives markets including the potential impact of derivatives on economic stability, pricing issues, modelling as well as model performance and the application of derivatives for risk management and corporate control.

How do stock prices evolve over time? The standard assumption of geometric Brownian motion, questionable as it has been right along, is even more doubtful in light of the recent stock market crash and the subsequent prices of U.S. index options. With the development of rich and deep markets in these options, it is now possible to use options prices to make inferences about the risk-neutral stochastic process governing the underlying index. We compare the ability of models including Black–Scholes, naïve volatility smile predictions of traders, constant elasticity of variance, displaced diffusion, jump diffusion, stochastic volatility, and implied binomial trees to explain otherwise identical observed option prices that differ by strike prices, times-to-expiration, or times. The latter amounts to examining predictions of future implied volatilities.

Certain naïve predictive models used by traders seem to perform best, although some academic models are not far behind. We find that the better-performing models all incorporate the negative correlation between index level and volatility. Further improvements to the models seem to require predicting the future at-the-money implied volatility. However, an “efficient markets result” makes these forecasts difficult, and improvements to the option-pricing models might then be limited.