Uncertainty is inherent in every financial model. It is driven by changing fundamentals, human psychology, and the manner in which the markets discount potential future states of the macroeconomic environment. While defining uncertainty in financial markets can quickly escalate into philosophical discussions, volatility is widely accepted as a practical measure of risk. Most market variables re...

Empirical studies showed that …rm-level volatility has been increasing but the aggregate volatility has been decreasing in the US for the post-war period. This paper proposes a uni…ed explanation for these diverging trends. Our explanation is based on a story of …nancial development –measured by the reduction of borrowing constraints because of greater access to external …nancing and options fo...

State-of-the-art stochastic volatility models generate a “volatility smirk” that explains why out-of-the-money index puts have high prices relative to the Black-Scholes benchmark. These models also adequately explain how the volatility smirk moves up and down in response to changes in risk. However, the data indicate that the slope and the level of the smirk ‡uctuate largely independently. Whil...

We provide a detailed summary of the large and vibrant emerging literature that deals with the multivariate modeling of conditional volatility of financial time series within the framework of stochastic volatility. The developments and achievements in this area represent one of the great success stories of financial econometrics. Three broad classes of multivariate stochastic volatility models ...

In financial econometrics the modeling of asset return series is closely related to the estimation of the corresponding conditional densities. One reason why one is interested in the whole conditional density and not only in the conditional mean, is that the conditional variance can be interpreted as a measure of time-dependent volatility of the return series. In fact, the modeling and the pred...

The purpose of this study is to model the nonparametric realized volatility of the futures contract as traded in domestic U.S. markets for exchange involving the South African rand and the U.S. dollar (ZAR). The study embraces a Bayesian regularization radial basis function (RBF) artificial neural network (ANN) to model the complex volatility patterns. The modeling characteristics revealed by t...

In this paper we document that realized variation measures constructed from highfrequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictab...

because of its extensive applications in financial analysis, stock market volatility modeling is a significantly important issue for stock market practitioners and academicians. using garch models to formulate the conditional variance heteroskedasticity and the taking advantages of panel data technique such as higher degrees of freedom, more flexibility in the control of the omitted or unobserv...

In this paper, we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly Gaussian, this unpredict...