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Forecasting value at risk with intra-day return curves
- Publication Year :
- 2020
- Publisher :
- Elsevier, 2020.
-
Abstract
- Methods for incorporating high resolution intra-day asset price data into risk forecasts are being developed at an increasing pace. Existing methods such as those based on realized volatility depend primarily on reducing the observed intra-day price fluctuations to simple scalar summaries. In this study, we propose several methods that incorporate full intra-day price information as functional data objects in order to forecast value at risk (VaR). Our methods are based on the recently proposed functional generalized autoregressive conditionally heteroscedastic (GARCH) models and a new functional linear quantile regression model. In addition to providing daily VaR forecasts, these methods can be used to forecast intra-day VaR curves, which we considered and studied with companion backtests to evaluate the quality of these intra-day risk measures. Using high-frequency trading data from equity and foreign exchange markets, we forecast the one-day-ahead daily and intra-day VaR with the proposed methods and various benchmark models. The empirical results suggested that the functional GARCH models estimated based on the overnight cumulative intra-day return curves exhibited competitive performance with benchmark models for daily risk management, and they produced valid intra-day VaR curves.
- Subjects :
- Heteroscedasticity
Intra day
business.industry
Realized variance
Autoregressive conditional heteroskedasticity
05 social sciences
01 natural sciences
010104 statistics & probability
Autoregressive model
0502 economics and business
Econometrics
H1
Foreign exchange
0101 mathematics
Business and International Management
business
health care economics and organizations
Value at risk
Risk management
050205 econometrics
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 01692070
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....780cd61c8576f001dc8bca16cec9e496