A volatility model must be able to forecast volatility; this is the central requirement in almost all financial applications. In this paper we outline some stylized facts about volatility that should be incorporated in a model: pronounced persistence and mean-reversion, asymmetry such that the sign of an innovation also affects volatility and the possibility of exogenous or pre-determined variables influencing volatility. We use data on the Dow Jones Industrial Index to illustrate these stylized facts, and the ability of GARCH-type models to capture these features. We conclude with some challenges for future research in this area
This Chapter reviews the main classes of models that incorporate volatility, with a focus on the mos...
Volatility in financial markets has both low and high–frequency components which determine its dynam...
Volatility persistence is a stylized statistical property of financial time-series data such as exch...
A volatility model must be able to forecast volatility; this is the central requirement in almost al...
A volatility model must be able to forecast volatility. This is the central requirement in almost al...
The forecasting ability of the most popular volatility forecasting models is examined and an alterna...
[[abstract]]This study uses exchange-traded fund (ETF) data to investigate the ability of the time-s...
This study provides comprehensive analysis of the possible influences of the real body, and both upp...
Volatility is considered among the most vital concepts of the financial market and is frequently use...
This paper examines the predictive power of idiosyncratic volatility in the context of daily stock m...
Abstract: Volatility is a key parameter used inmany financial applications, from deriva-tives valuat...
It is sometimes argued that an increase in stock market volatility raises required stock returns, an...
Modern institutions from multinationals to nation states use the global derivatives market in order ...
Volatility is arguably one of the most important measures in financial economics since it is often u...
The three main purposes of forecasting volatility are for risk management, for asset alloca-tion, an...
This Chapter reviews the main classes of models that incorporate volatility, with a focus on the mos...
Volatility in financial markets has both low and high–frequency components which determine its dynam...
Volatility persistence is a stylized statistical property of financial time-series data such as exch...
A volatility model must be able to forecast volatility; this is the central requirement in almost al...
A volatility model must be able to forecast volatility. This is the central requirement in almost al...
The forecasting ability of the most popular volatility forecasting models is examined and an alterna...
[[abstract]]This study uses exchange-traded fund (ETF) data to investigate the ability of the time-s...
This study provides comprehensive analysis of the possible influences of the real body, and both upp...
Volatility is considered among the most vital concepts of the financial market and is frequently use...
This paper examines the predictive power of idiosyncratic volatility in the context of daily stock m...
Abstract: Volatility is a key parameter used inmany financial applications, from deriva-tives valuat...
It is sometimes argued that an increase in stock market volatility raises required stock returns, an...
Modern institutions from multinationals to nation states use the global derivatives market in order ...
Volatility is arguably one of the most important measures in financial economics since it is often u...
The three main purposes of forecasting volatility are for risk management, for asset alloca-tion, an...
This Chapter reviews the main classes of models that incorporate volatility, with a focus on the mos...
Volatility in financial markets has both low and high–frequency components which determine its dynam...
Volatility persistence is a stylized statistical property of financial time-series data such as exch...