EVALUATING INTERVAL FORECASTS

By Peter F. CHRISTOFFERSENti. 2016

632 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/15345dd0a431530b4d94494b4a0a73033887b64d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, methodology): “is distributed asymptotically as χ(2)(1) [46, 77].”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, methodology): “Using the conditional coverage test of Christoffersen (1998), they reach the conclusion that, while the empirical PIs (see Section 4.2.1) from combined forecasts do not provide significant gains for the PJM dataset considered, the QRA-based PIs are found to be more accurate than those of the best in”
Multinomial VaR Backtests: A simple implicit approach to backtesting expected shortfall (2016, methodology): “It is well known (Christoffersen, 1998) that if the losses Lt have conditional distribution functions Ft then, for fixed j, the sequence (It,j)t=1,...,n should satisfy: • the unconditional coverage hypothesis, E (It,j) = 1− αj for all t, and • the independence hypothesis, It,j is independent of…”
Linear Algebra and its Applications (2010, unknown): “To test the accuracy of the VaR models we employ unconditional coverage tests, introduced by Kupiec [16], and the independence and conditional coverage tests, introduced by Christoffersen [4].”
GARCH Modelling of Cryptocurrencies (2017, methodology): “Finally, we perform the unconditional and conditional coverage value at risk exceedances tests (Christoffersen (1998); Christoffersen et al. (2001)).”
Bayesian Tail Risk Interdependence Using Quantile Regression (2015, methodology): “To assess the backtesting performances we calculate the actual over expected number of violations (A/E), the expected loss given a violation (AD), and the well known conditional and unconditional coverage tests of Kupiec (1995) and Christoffersen (1998), and the Dynamic Quantile (DQ) test of Engle a”
Combining density forecasts using focused scoring rules (2017, unknown): “For this purpose we use the conditional coverage (CC) test of Christoffersen (1998) and the dynamic quantile (DQ) test of Engle and Manganelli (2004).”
Forecasting Value-at-Risk under Different Distributional Assumptions (2016, methodology): “According to Christoffersen [34], in order to be accurate, the hit sequence has to satisfy the two properties of correct failure rate and independence of exceptions.”
Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution (2016, methodology): “To test the independence hypothesis explicitly, Christoffersen (1998) considers the al- ternative of the first-order Markov process with the switching probability matrix Π = 1− π01 π01 1− π11 π11 , (16) where πij is the probability of an i ∈ {0, 1} on day t − 1 being followed by a j ∈ {0, 1} on…”
The role of the information set for forecasting—with applications to risk management (2014, methodology): “Following Berkowitz, Christoffersen and Pelletier (2011), typical areas of application of VaR estimates include the following: A. Risk controls for trading desks.”
Frontiers in VaR forecasting and backtesting (2016, methodology): “Wong (2010) criticizes the statistics proposed by Christoffersen (1998) because they are two-tailed and, consequently, they can reject a risk model for overconservative.”