This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous component using estimators which are not...

In this paper, we provide evidence on two alternative mechanisms of interaction between returns and volatilities: the leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and implied volatility, and find that implied...

Recent contributions highlight the importance of intraday jumps in forecasting realized volatility at horizons up to one month. We extend the methodology developed in Maheu and McCurdy (2011) to exploit the information content of intraday data in forecasting the density of returns. Considering...

This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high-frequency data on four prominent energy markets, we perform a model-free decomposition of realized variance into its continuous and discontinuous components. We find strong...

The present paper analyzes the forecastability and tradability of volatility on the large S&P500 index and the liquid SPY ETF, VIX index and VXX ETN. Even though there is already a huge array of literature on forecasting high frequency volatility, most publications only evaluate the forecast in...

Purpose: This paper examines the volatility of stock return in Dhaka stock exchange, BangladeshMethodology: Using Random Walk model (RW), Autoregressive model (AR), Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and extensive GARCH model with Normal, and Student...

Following recent advances in the non-parametric realized volatility approach, we separately measure the discontinuous jump part of the quadratic variation process for individual stocks and incorporate it into heterogeneous autoregressive volatility models. We analyze the distributional...

Estimation of volatility is important for many financial applications. The most common methods are based on a time series analysis, either sampling prices at regular intervals or using extreme values from within the time intervals. We examine an alternative, where we use the exit times from a...

We study the impact of different central bank communication practices on the trading behavior and profitability of fast and slow traders in the foreign exchange market. We focus, in particular, on how the Bank of Japan's practice of introducing some randomness to the time at which it releases...

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time-series. However, econometric studies have shown that...