Flogarch: Realizing Long Memory and Asymmetries in Returns Volatility

Elst, Harry-Paul Vander

doi:10.2139/ssrn.2559052

Cited by 5 publications

(3 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the reason relates to the use of logarithmic volatilities and was documented in Huang (2015) andVander Elst (2015).…”

Section: A Joint Model For Returns Volatilities and Economic Variablesmentioning

confidence: 99%

“…Later, Hansen et al (2012) proposed the class of Realized GARCH models that generalizes the GARCH-X by including a measurement equation for the realized measure of volatility. Hansen and Huang (2015), Hansen et al (2014), andVander Elst (2015) completed the class of Realized GARCH models with the Realized EGARCH, the Realized Beta GARCH, and the FloGARCH, respectively. Shephard and Sheppard (2010) proposed the HEAVY model that also focuses on modeling the conditional volatility of returns.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Macro-Driven VAR Forecasts: From Very High to Very Low-Frequency Data

Dominicy

Elst

2015

SSRN Journal

Self Cite

View full text Add to dashboard Cite

This paper studies in some details the joint-use of high-frequency data and economic variables to model financial returns and volatility. We extend the Realized LGARCH model by allowing for a timevarying intercept, which responds to changes in macroeconomic variables in a MIDAS framework and allows macroeconomic information to be included directly into the estimation and forecast procedure.Using more than 10 years of high-frequency transactions for 55 U.S. stocks, we argue that the combination of low-frequency exogenous economic indicators with high-frequency financial data improves our ability to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multiperiod Value-at-Risk. We document that nominal corporate profits and term spreads generate accurate risk measures forecasts at horizons beyond two business weeks.

show abstract

“…One of the reason relates to the use of logarithmic volatilities and was documented in Huang (2015) andVander Elst (2015).…”

Section: A Joint Model For Returns Volatilities and Economic Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Macro-Driven VAR Forecasts: From Very High to Very Low-Frequency Data

Dominicy

Elst

2015

SSRN Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…We apply our REGARCH-MIDAS and REGARCH-HAR to the exchange traded index fund, SPY, which tracks the S&P500 Index and 20 individual stocks and compare their performances to a quadratic REGARCH-Spline and a fractionally integrated REGARCH, the FloEGARCH (Vander Elst, 2015). We find that both our proposed models better capture the autocorrelation structure of latent and realized volatility relative to the original REGARCH, which is only able to capture the dependency over the very short term.…”

mentioning

confidence: 99%

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

Borup

Jakobsen

2019

Quantitative Finance

View full text Add to dashboard Cite

show abstract

Heterogeneous Volatility Information Content for the Realized GARCH Modeling and Forecasting Volatility

Xu,

Aschakulporn,

Zhang

2024

Studies in Nonlinear Dynamics &Amp; Econometrics

View full text Add to dashboard Cite

As the demand for accuracy in volatility modeling and forecasting increases, the literature tends to incorporate different volatility measures with heterogeneous information content to construct the hybrid volatility model. This study focuses on one of the popular hybrid volatility models: the Realized Generalized Autoregressive Heteroskedasticity (Realized GARCH) and embeds various volatility measures, including the CBOE VIX, VIX1D, Realized Volatility, and Daily Range to examine their heterogeneous impact on the conditional volatility estimation and forecasting. To evaluate the impact of the volatility measures, we first construct a volatility response function. This involves calculating the difference in one-step-ahead conditional volatility forecasts that incorporate information from both return and volatility measures against the forecasts based on return innovations only. Subsequently, the variance share is calculated to evaluate its role in explaining future variations in the Realized GARCH. Our results show that among these four volatility measures, VIX is the most informative volatility. Although VIX1D is overemphasized by the literature, its significance in volatility forecasting remains substantial, confirming that risk-neutral volatility measures are generally more informative than physical measures. Finally, we also find that incorporating multiple risk-neutral volatility measures does not improve forecasting performance compared to using a single measure due to overlapping information.

show abstract

Flogarch: Realizing Long Memory and Asymmetries in Returns Volatility

Cited by 5 publications

References 83 publications

Macro-Driven VAR Forecasts: From Very High to Very Low-Frequency Data

Macro-Driven VAR Forecasts: From Very High to Very Low-Frequency Data

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

Heterogeneous Volatility Information Content for the Realized GARCH Modeling and Forecasting Volatility

Contact Info

Product

Resources

About