The study of semiparametric volatility and correlation models is a very quickly developing area of financial
econometrics since about one decade. There is a broad variety of such models, which extend well known
parametric models in different ways. The purpose of those models is to capture nonstationarity and possible
structural breaks in volatility and correlation, for example, or to allow the use of nonparametric conditional
distributions, which can be applied to model slowly changing volatility and correlation components. Furthermore,
the influence of the economic environment and financial crises on the volatility and correlation can be modeled
by this class of models or financial risk can be decomposed into different components caused by different factors.

   This workshop aims to summarize the development and application of non- and semiparametric volatility and
correlation models in the last decade, to provide a forum for researchers to exchange their current results and to
discuss the frontiers of future research in this sub-area of financial econometrics. It should also provide a platform
for young researchers to present their own research results and to learn about the state of the art in this context.

  TOPICS INCLUDE, BUT ARE NOT LIMITED TO:· Long-term dynamics and economic sources of volatility· Long-term dynamics and economic sources of correlations· Regime switching and semiparametric stochastic volatility models· Structural breaks in volatility and correlations, and their relationship to financial crises· Semiparametric modeling of high-frequency and related data· Non- and semiparametric quantile regression for modeling volatility and correlations· Application to quantitative risk management and measurement of systemic risk