This paper is concerned with the issues of modeling and projecting the dynamics of volatility when a group of potentially useful predetermined variables is available. We predict realized volatility and value at risk (VaR) with a nested set of multiplicative error models for realized volatility....

In this paper we address the issue of forecasting Value-at-Risk (VaR) using different volatility measures: realized volatility, bipower realized volatility, two scales realized volatility, realized kernel as well as the daily range. We propose a dynamic model with a flexible trend specification...

We conduct an out-of-sample backtesting exercise of Growth-at-Risk (GaR) predictions for 24 OECD countries. We consider forecasts constructed from quantile regression and GARCH models. The quantile regression forecasts are based on a set of recently proposed measures of downside risks to GDP,...

By means of a difference-in-differences approach (sigma-DID), we investigate the effect that hedging has on corporate risk. Examining the relation between hedging and the idiosyncratic variance of stock returns, we show that when new commodity derivatives are introduced in the Chicago Mercantile...

We introduce LASSO-type regularization for large dimensional realized covariance estimators of log-prices. The procedure consists of shrinking the off-diagonal entries of the inverse realized covariance matrix towards zero. This technique produces covariance estimators that are positive definite...

This paper is concerned with the issues of modeling and projecting the dynamics of volatility when a group of potentially useful predetermined variables is available. We predict realized volatility and value at risk (VaR) with a nested set of multiplicative error models for realized volatility....

The financial econometrics literature on Ultra High-Frequency Data (UHFD) has been growing steadily in recent years. However, it is not always straightforward to construct time series of interest from the raw data and the consequences of data handling procedures on the subsequent statistical...

Nonlinear time series models can exhibit components such as long range trends and seasonalities that may be modeled in a flexible fashion. The resulting unconstrained maximum likelihood estimator can be too heavily parameterized and suboptimal for forecasting purposes. The paper proposes the use...

In this paper we address the issue of forecasting Value–at–Risk (VaR) using different volatility measures: realized volatility, bipower realized volatility, two scales realized volatility, realized kernel as well as the daily range. We propose a dynamic model with a flexible trend...

The explosion of algorithmic trading has been one of the most pro-minent recent trends in the financial industry. Algorithmic trading consists of automated trading strategies that attempt to minimize transaction costs by optimally placing orders. The key ingredient of many of these strategies...