This paper studies the relation between implied and realized volatility by using daily S&P 500 index option price over the period between January 1995 and December 1999. In particular, we want to test the how different measurement errors affect the stability of this relationship. Two sources of measurement errors are considered. The first one is the measurement error in realized volatility. Fou...

When estimating integrated volatilities based on high-frequency data, simplifying assumptions are usually imposed on the relationship between the observation times and the price process. In this paper, we establish a central limit theorem for the Realized Volatility in a general endogenous time setting. We ∗We are grateful to Andrew Patton and Neil Shephard, and the participants of the Stevanov...

Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applica...

Using the multiple threshold autoregressive and moving average (TARMA) model we analyze the nonlinearities in the dynamics of realized volatilities of daily stock returns of 30 companies in the Dow Jones index. We find that the realized volatility processes can be characterized by the high, moderate, and low regimes and that the persistence, variance and ARMA error term change with each regime....

This paper sets up a statistical framework for modeling realized volatility (RV) using Dynamic Conditional Score (DCS) model. It first shows how, dataset on stock indices, preliminary analysis of RV, based fitting linear Gaussian model to its logarithm, suggests the use two component dynamic specification. also indicates departure from normality, weekly pattern in data and presence heteroscedas...

We forecast the realized and median volatility of agricultural commodities using variants heterogeneous autoregressive (HAR) model. obtain tick-by-tick data on five widely-traded (corn, rough rice, soybeans, sugar, wheat) from CME/ICE. Real out-of-sample forecasts are produced for between 1 66 days ahead. Our in-sample analysis shows that HAR model which decompose measures into their continuous...