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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

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

Lilla, Francesca - 2016

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 of many limitations. HF data feature microstructure problem,...

Persistent link: https://www.econbiz.de/10014124325

Forecasting Realized Volatility : Does Anything Beat Linear Models?

Branco, Rafael; Rubesam, Alexandre; Zevallos, Mauricio - 2022

We evaluate the performance of several linear and nonlinear machine learning models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset which includes past values of the RV and additional...

Persistent link: https://www.econbiz.de/10014076641

The Limits to Volatility Predictability : Quantifying Forecast Accuracy Across Horizons

Zakamulin, Valeriy; Li, Xingyi - 2018

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about how far ahead one can forecast volatility. First, in this paper we introduce the notions of the spot and forward predicted volatilities and propose to...

Persistent link: https://www.econbiz.de/10014111954

Decoupling the Short- and Long-Term Behavior of Stochastic Volatility

Bennedsen, Mikkel - 2017

We study the empirical properties of realized volatility of the E-mini S&P 500 futures contract at various time scales, ranging from a few minutes to one day. Our main finding is that intraday volatility is remarkably rough and persistent. What is more, by further studying daily realized...

Persistent link: https://www.econbiz.de/10012967996

Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility?

Charoenwong, Ben - 2017

No. Conditional autocorrelation in realized shocks due to misspecification in expected return process affects the relative performance of longer-horizon volatility predictions of models using different frequencies of data. This is because, for multi-step forecasts of volatility, small violations...

Persistent link: https://www.econbiz.de/10012969447

Risk Everywhere : Modeling and Managing Volatility

Bollerslev, Tim - 2017

Based on a unique high-frequency dataset for more than fifty commodities, currencies, equity indices, and fixed income instruments spanning more than two decades, we document strong similarities in realized volatilities patterns across assets and asset classes. Exploiting these similarities...

Persistent link: https://www.econbiz.de/10012970195