VOLATILITY PATTERNS OF THE LARGEST POLISH COMPANIES: SOME EVIDENCE FROM HIGH-FREQUENCY DATA

Consistent High-Precision Volatility from High-Frequency Data

Estimates of daily volatility are investigated. Realized volatility can be computed from returns observed over time intervals of different sizes. For simple statistical reasons, volatility estimators based on high-frequency returns have been proposed, but such estimators are found to be strongly biased as compared to volatilities of daily returns. This bias originates from microstructure effect...

The Volatility of Earnings: Evidence from High-Frequency Firm-Level Data

The first contribution of this paper is to use UK monthly firm-level data to show that there is a large amount of transitory volatility in firm-level average earnings from month to month. We conclude that this cannot all be explained away as the consequence of measurement error, composition effects or variation in remunerated hours i.e. we suggest this volatility is real. The second contributio...

Some Determinants of Corporate Financing Decisions: Evidence from the Listed Companies in Tehran Stock Exchange

The aim of this empirical study is to explore the trade-off model and pecking order model of capital structure. The investigation is performed using panel data procedures for a sample of 76 firms listed in Tehran Stock Exchange during 2007-2010.The study employs OLS regression model in examining the capital structure of firms in Iran. The study employs variables reflecting differing theoretical...

Forecasting Volatility using High Frequency Data

Spot volatility estimation for high-frequency data

The availability of high-frequency intraday data allows us to accurately estimate stock volatility. This paper employs a bivariate diffusion to model the price and volatility of an asset and investigates kernel type estimators of spot volatility based on highfrequency return data. We establish both pointwise and global asymptotic distributions for the estimators. Jianqing Fan is Frederick Moore...