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Jump variation estimation with noisy high frequency financial data via wavelets

Zhang, Xin; Kim, Donggyu; Wang, Yazhen - In: Econometrics : open access journal 4 (2016) 3, pp. 1-26

This paper develops a method to improve the estimation of jump variation using high frequency data with the existence of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component of volatility in finance for portfolio allocation,...

Persistent link: https://www.econbiz.de/10011568279

Range-based volatility estimation and forecasting

Benčík, Daniel - 2014

In this paper, we analyze new possibilities in predicting daily ranges, i.e. differences between daily high and low prices. We empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to simple daily ranges and explore the use of these more...

Persistent link: https://www.econbiz.de/10010461231

Conditional variance forecasts for long-term stock returns

Mammen, Enno; Nielsen, Jens Perch; Scholz, Michael; … - In: Risks : open access journal 7 (2019) 4/113, pp. 1-22

squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the …

Persistent link: https://www.econbiz.de/10012127861

Bayesian predictive distributions of oil returns using mixed data sampling volatility models

Virbickaite, Audrone; Nguyen, Hoang; Minh-Ngoc Tran - 2023

such as Generalized Autoregressive Conditional Heteroskedastic (GARCH), Generalized Autoregressive Score (GAS), and … relevant financial/macroeconomic news into asset price movements. For inference and prediction, we employ an innovative … inclusion of exogenous variables is beneficial for GARCH-type models while offering only a marginal improvement for GAS and SV …

Persistent link: https://www.econbiz.de/10014252427

Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

Duong, Diep; Swanson, Norman R. - 2013

Ding, Granger and Engle (1993) and Ding and Granger (1996). Our prediction experiments use high frequency price returns … variations help less in the prediction of future realized volatility, than past "small" jump power variations. Additionally, we …, are strongly correlated with future volatility, and that past downside jump variations matter in prediction. Finally …

Persistent link: https://www.econbiz.de/10009771770

Exchange rate risk premium : an analysis of its determinants for the Mexican Peso-USD

Benavides, Guillermo - 2016

The objective of this paper is to analyze what are the main determinants of the exchange rate risk premium (ERP). The empirical case is conducted for the daily Mexican peso-USD exchange rate for a sample period from 2007 until 2015. According to the results the ERP is influenced by several...

Persistent link: https://www.econbiz.de/10011496736

Modeling and Forecasting Realized Range Volatility

Caporin, Massimiliano - 2011

In this paper, we estimate, model and forecast Realized Range Volatility, a new realized measure and estimator of the quadratic variation of financial prices. This estimator was early introduced in the literature and it is based on the high-low range observed at high frequency during the day. We...

Persistent link: https://www.econbiz.de/10013130487

Reexamining Financial and Economic Predictability with New Estimators of Realized Variance and Variance Risk Premium

Casas, Isabel - 2018

This study explores the predictive power of new estimators of the equity variance risk premium and conditional variance for future excess stock market returns, economic activity, and financial instability, both during and after the last global financial crisis. These estimators are obtained from...

Persistent link: https://www.econbiz.de/10012925879

High Frequency vs. Daily Resolution : The Economic Value of Forecasting Volatility Models

Lilla, Francesca - 2017

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem,...

Persistent link: https://www.econbiz.de/10012958968

The Term Structure of Volatility Predictability

Li, Xingyi - 2019

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about the accuracy of volatility forecasts and the horizon of volatility predictability. This paper aims to fill these gaps in the literature. We begin this...

Persistent link: https://www.econbiz.de/10012890910

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