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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

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

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

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

A Gaussian Mixture Hidden Markov Model for the VIX

Aigner, Andreas A. - 2023

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...

Persistent link: https://www.econbiz.de/10014356167

Conditional Threshold Effects of Stock Market Volatility on Crude Oil Market Volatility

Motegi, Kaiji; Hamori, Shigeyuki - 2023

This paper analyzes conditional threshold effects of stock market volatility on crude oil market volatility. We use the conditional threshold autoregressive (CoTAR) model, a novel extension of TAR from a constant to time-varying threshold. The conditional threshold is specified as an empirical...

Persistent link: https://www.econbiz.de/10014353102

Deep Bayesian Estimation of the Rough Path-Dependent Volatility Model for Forecasting Purposes

Parent, Léo - 2023

The rough path-dependent volatility (RPDV) model (Parent 2022) effectively captures key empirical features that are characteristic of volatility dynamics, making it a suitable choice for volatility forecasting. However, its complex structure presents challenges when it comes to estimating the...

Persistent link: https://www.econbiz.de/10014354222

Flight to Safety : Does It Matter for Volatility Forecasting?

Ruzzi, Dario - 2020

We propose the use of a risk measure built on flight-to-safety (FTS) episodes into a volatility forecasting model. We assign to each day in the sample a probability of being a FTS day after observing (ab)normal movements in the US equity, US bond and gold markets. By allowing each FTS day to be...

Persistent link: https://www.econbiz.de/10012852744

A Simple Model Correction for Modelling and Forecasting (Un)Reliable Realized Volatility

Hizmeri, Rodrigo - 2020

We propose a dilution bias correction approach to deal with the errors-in-variables problem observed in realized volatility (RV) measures. The absolute difference between daily and monthly RV is shown to be proportional to the relative magnitude of the measurement error. Therefore, in...

Persistent link: https://www.econbiz.de/10012829634

Hark the Shark : Realized Volatility Modelling with Measurement Errors and Nonlinear Dependencies

Buccheri, Giuseppe - 2019

Despite their effectiveness, linear models for realized variance neglect measurement errors on integrated variance and exhibit several forms of misspecification due to the inherent nonlinear dynamics of volatility. We propose new extensions of the popular approximate long-memory HAR model apt to...

Persistent link: https://www.econbiz.de/10012900397