Department of Economics and Business Economics

Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model

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Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model. / Borup, Daniel; Jakobsen, Johan Stax.

In: Quantitative Finance, 10.06.2019, p. 1-17.

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Borup, Daniel ; Jakobsen, Johan Stax. / Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model. In: Quantitative Finance. 2019 ; pp. 1-17.

Bibtex

@article{8a8633fd0e234e498349f7fa553743be,
title = "Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model",
abstract = "We introduce extensions of the Realized Exponential GARCH model (REGARCH) that capture the evident high persistence typically observed in measures of financial market volatility in a tractable fashion. The extensions decompose conditional variance into a short-term and a long-term component. The latter utilizes mixed-data sampling or a heterogeneous autoregressive structure, avoiding parameter proliferation otherwise incurred by using the classical ARMA structures embedded in the REGARCH. The proposed models are dynamically complete, facilitating multi-period forecasting. A thorough empirical investigation with an exchange-traded fund that tracks the S&P500 Index and 20 individual stocks shows that our models better capture the dependency structure of volatility. This leads to substantial improvements in empirical fit and predictive ability at both short and long horizons relative to the original REGARCH. A volatility-timing trading strategy shows that capturing volatility persistence yields substantial utility gains for a mean–variance investor at longer investment horizons.",
keywords = "GARCH-MIDAS, HAR, Long memory, Persistence, Realized exponential GARCH, Realized kernel",
author = "Daniel Borup and Jakobsen, {Johan Stax}",
year = "2019",
month = "6",
day = "10",
doi = "10.1080/14697688.2019.1614653",
language = "English",
pages = "1--17",
journal = "Quantitative Finance",
issn = "1469-7688",
publisher = "Routledge",

}

RIS

TY - JOUR

T1 - Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model

AU - Borup, Daniel

AU - Jakobsen, Johan Stax

PY - 2019/6/10

Y1 - 2019/6/10

N2 - We introduce extensions of the Realized Exponential GARCH model (REGARCH) that capture the evident high persistence typically observed in measures of financial market volatility in a tractable fashion. The extensions decompose conditional variance into a short-term and a long-term component. The latter utilizes mixed-data sampling or a heterogeneous autoregressive structure, avoiding parameter proliferation otherwise incurred by using the classical ARMA structures embedded in the REGARCH. The proposed models are dynamically complete, facilitating multi-period forecasting. A thorough empirical investigation with an exchange-traded fund that tracks the S&P500 Index and 20 individual stocks shows that our models better capture the dependency structure of volatility. This leads to substantial improvements in empirical fit and predictive ability at both short and long horizons relative to the original REGARCH. A volatility-timing trading strategy shows that capturing volatility persistence yields substantial utility gains for a mean–variance investor at longer investment horizons.

AB - We introduce extensions of the Realized Exponential GARCH model (REGARCH) that capture the evident high persistence typically observed in measures of financial market volatility in a tractable fashion. The extensions decompose conditional variance into a short-term and a long-term component. The latter utilizes mixed-data sampling or a heterogeneous autoregressive structure, avoiding parameter proliferation otherwise incurred by using the classical ARMA structures embedded in the REGARCH. The proposed models are dynamically complete, facilitating multi-period forecasting. A thorough empirical investigation with an exchange-traded fund that tracks the S&P500 Index and 20 individual stocks shows that our models better capture the dependency structure of volatility. This leads to substantial improvements in empirical fit and predictive ability at both short and long horizons relative to the original REGARCH. A volatility-timing trading strategy shows that capturing volatility persistence yields substantial utility gains for a mean–variance investor at longer investment horizons.

KW - GARCH-MIDAS

KW - HAR

KW - Long memory

KW - Persistence

KW - Realized exponential GARCH

KW - Realized kernel

UR - http://www.scopus.com/inward/record.url?scp=85067556773&partnerID=8YFLogxK

U2 - 10.1080/14697688.2019.1614653

DO - 10.1080/14697688.2019.1614653

M3 - Journal article

SP - 1

EP - 17

JO - Quantitative Finance

JF - Quantitative Finance

SN - 1469-7688

ER -