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Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction

Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction
Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction
We make use of quantile regression theory to obtain a combination of individual potentially-biased VaR forecasts that is optimal because, by construction, it meets the correct out-of-sample conditional coverage criterion ex post. This enables a Wald-type conditional quantile forecast encompassing test to be used for any finite set of competing (semi/non)parametric models which can be nested. Two attractive properties of this backtesting approach are its robustness to both model risk and estimation uncertainty. We deploy the techniques to analyse inter-day and high frequency intra-day VaR models for equity, FOREX, fixed income and commodity trading desks. The forecast combination of both types of models is especially warranted for more extreme-tail risks. Overall, our empirical analysis supports the use of high frequency 5 minute price information for daily risk management.
quantile regression, optimal forecast combination, encompassing, conditional coverage, high-frequency data, realized variance
0169-2070
28-42
Fuertes, Ana-Maria
c9223f2d-85a6-4744-a8aa-3de8bcf06299
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Fuertes, Ana-Maria
c9223f2d-85a6-4744-a8aa-3de8bcf06299
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e

Fuertes, Ana-Maria and Olmo, Jose (2013) Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction. International Journal of Forecasting, 29 (1), 28-42. (doi:10.1016/j.ijforecast.2012.05.005).

Record type: Article

Abstract

We make use of quantile regression theory to obtain a combination of individual potentially-biased VaR forecasts that is optimal because, by construction, it meets the correct out-of-sample conditional coverage criterion ex post. This enables a Wald-type conditional quantile forecast encompassing test to be used for any finite set of competing (semi/non)parametric models which can be nested. Two attractive properties of this backtesting approach are its robustness to both model risk and estimation uncertainty. We deploy the techniques to analyse inter-day and high frequency intra-day VaR models for equity, FOREX, fixed income and commodity trading desks. The forecast combination of both types of models is especially warranted for more extreme-tail risks. Overall, our empirical analysis supports the use of high frequency 5 minute price information for daily risk management.

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More information

Published date: January 2013
Keywords: quantile regression, optimal forecast combination, encompassing, conditional coverage, high-frequency data, realized variance
Organisations: Economics

Identifiers

Local EPrints ID: 348636
URI: https://eprints.soton.ac.uk/id/eprint/348636
ISSN: 0169-2070
PURE UUID: 5c0f1f2e-c61a-4878-85cf-8d234ccad0ad
ORCID for Jose Olmo: ORCID iD orcid.org/0000-0002-0437-7812

Catalogue record

Date deposited: 18 Feb 2013 10:04
Last modified: 03 Dec 2019 01:36

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