Improved Forecasting of Realized Variance Measures

Author(s):  
Jeremias Bekierman ◽  
Hans Manner
Keyword(s):  
Author(s):  
Peter P. Carr ◽  
Hélyette Geman ◽  
Dilip B. Madan ◽  
Marc Yor

2021 ◽  
Vol 13 (14) ◽  
pp. 7987
Author(s):  
Mehmet Balcilar ◽  
Elie Bouri ◽  
Rangan Gupta ◽  
Christian Pierdzioch

We use the heterogenous autoregressive (HAR) model to compute out-of-sample forecasts of the monthly realized variance (RV) of movements of the spot and futures price of heating oil. We extend the HAR–RV model to include the role of El Niño and La Niña episodes, as captured by the Equatorial Southern Oscillation Index (EQSOI). Using data from June 1986 to April 2021, we show evidence for several model configurations that both El Niño and La Niña phases contain information useful for forecasting subsequent to the realized variance of price movements beyond the predictive value already captured by the HAR–RV model. The predictive value of La Niña phases, however, seems to be somewhat stronger than the predictive value of El Niño phases. Our results have important implications for investors, as well as from the perspective of sustainable decisions involving the environment.


Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4173
Author(s):  
Rangan Gupta ◽  
Christian Pierdzioch

We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers.


2002 ◽  
Vol 17 (5) ◽  
pp. 457-477 ◽  
Author(s):  
Ole E. Barndorff-Nielsen ◽  
Neil Shephard

2013 ◽  
Author(s):  
Carl Chiarella ◽  
Guanghua Lian ◽  
Petko S. Kalev
Keyword(s):  

2012 ◽  
Vol 62 (3) ◽  
pp. 1480-1501 ◽  
Author(s):  
Lorella Fatone ◽  
Marco Giacinti ◽  
Francesca Mariani ◽  
Maria Cristina Recchioni ◽  
Francesco Zirilli

2021 ◽  
Author(s):  
Diego Amaya ◽  
Jean-François Bégin ◽  
Geneviève Gauthier

We propose the option realized variance as an observable variable to summarize the information from high-frequency option data. This variable aggregates intraday option returns from midquote prices to compute an option’s total variability for a given day, providing additional information about the jump activity in the data generating process. Using the S&P 500 index time series and options data, this paper documents the performance of this realized measure in predicting the index realized variance as well as the equity and variance risk premiums. We estimate an option pricing model and analyze its parameter estimates. Our results show that excluding high-frequency option information produces significant differences in variance jump parameters, estimated risk premiums, and option pricing errors. This paper was accepted by Tyler Shumway, finance.


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