Reference health
LIGHT Benchmark - Comprehensive Backtesting Framework for Market Risk Models Comparison
69/69 checkable references clean · checked 2026-09-15
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
55 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 69 checked references that resolve
resolves10.1016/j.jbankfin.2007.03.009
The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR
resolves10.1111/j.1539-6975.2008.00266.x
<scp>Risk Measurement Performance of Alternative Distribution Functions</scp>
resolves10.1002/for.977
Evaluating predictive performance of value-at-risk models in emerging markets: a reality check
resolves10.1090/S0002-9904-1967-11751-8
An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
resolves10.1214/aoms/1177699147
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
resolves10.1016/j.jempfin.2016.01.006
Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective
resolves10.1016/j.intfin.2004.05.002
Estimation of Value-at-Risk by extreme value and conventional methods: a comparative evaluation of their predictive performance
resolves10.1016/j.jedc.2018.03.016
Improving daily Value-at-Risk forecasts: The relevance of short-run volatility for regulatory quality assessment
resolves10.3390/econometrics4010003
Forecasting Value-at-Risk under Different Distributional Assumptions
resolves10.1016/j.jempfin.2004.01.004
A comparison of extreme value theory approaches for determining value at risk
resolves10.2478/fiqf-2018-0013
COMPARISON OF SEMI-PARAMETRIC AND BENCHMARK VALUE-AT-RISK MODELS IN SEVERAL TIME PERIODS WITH DIFFERENT VOLATILITY LEVELS
resolves10.1016/j.jeconom.2015.03.035
Intraday Value-at-Risk: An asymmetric autoregressive conditional duration approach
resolves10.1016/j.eneco.2017.06.015
Forecasting the VaR of crude oil market: Do alternative distributions help?
resolves10.1016/j.jfds.2016.06.001
Forecasting daily conditional volatility and h-step-ahead short and long Value-at-Risk accuracy: Evidence from financial data
resolves10.1016/j.eneco.2020.104757
Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models
resolves10.1016/j.eneco.2009.02.005
Extreme Value Theory and Value at Risk: Application to oil market
resolves10.1002/for.1167
A decision rule to minimize daily capital charges in forecasting value‐at‐risk
resolves10.1002/for.1269
International Evidence on GFC‐Robust Forecasts for Risk Management under the Basel Accord
resolves10.1016/S0927-5398(00)00012-8
Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach
resolves10.1016/j.jbankfin.2021.106248
Forecasting VaR and ES using a joint quantile regression and its implications in portfolio allocation
resolves10.1016/j.resourpol.2019.101497
Modeling volatility of precious metals markets by using regime-switching GARCH models
resolves10.1108/15265941011025189
Filtered extreme‐value theory for value‐at‐risk estimation: evidence from Turkey
resolves10.1016/j.jeconom.2019.07.002
Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns
resolves10.1016/j.eneco.2021.105452
Revisiting value-at-risk and expected shortfall in oil markets under structural breaks: The role of fat-tailed distributions
resolves10.1016/j.iref.2018.08.014
Modeling the joint dynamic value at risk of the volatility index, oil price, and exchange rate
resolves10.1016/j.inteco.2018.03.001
Estimating value-at-risk using a multivariate copula-based volatility model: Evidence from European banks
resolves10.1002/fut.20293
Value at risk and conditional extreme value theory via markov regime switching models
resolves10.1016/j.najef.2022.101669
Forecasting risk measures using intraday and overnight information
resolves10.1016/j.ijforecast.2011.10.002
Ranking the predictive performances of value-at-risk estimation methods
resolves10.21511/imfi.19(1).2022.04
Value-at-risk (VAR) estimation and backtesting during COVID-19: Empirical analysis based on BRICS and US stock markets
resolves10.1016/j.iimb.2012.09.001
Evaluation of Basel III revision of quantitative standards for implementation of internal models for market risk
resolves10.1016/j.jimonfin.2011.08.002
Emerging markets and portfolio foreign exchange risk: An empirical investigation using a value-at-risk decomposition technique
resolves10.1016/j.intfin.2016.08.008
Value-at-Risk under Lévy GARCH models: Evidence from global stock markets
resolves10.1016/j.irfa.2022.102180
Predicting VaR for China's stock market: A score-driven model based on normal inverse Gaussian distribution
resolves10.1016/j.irfa.2012.02.003
Econometric modeling and value-at-risk using the Pearson type-IV distribution
resolves10.1016/j.econmod.2014.12.022
Value-at-risk estimates of the stock indices in developed and emerging markets including the spillover effects of currency market
resolves10.1016/j.econmod.2010.11.016
Empirical analysis of jump dynamics, heavy-tails and skewness on value-at-risk estimation
resolves10.1016/j.najef.2018.06.010
Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data
resolves10.1016/j.resourpol.2020.101601
Characteristics of spillovers between the US stock market and precious metals and oil
resolves10.1109/TIT.1967.1054010
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
resolves10.1162/qss_a_00112
Large-scale comparison of bibliographic data sources: Scopus, Web of
Science, Dimensions, Crossref, and Microsoft Academic
resolves10.1016/j.econmod.2022.105781
Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model
resolves10.1080/15140326.2022.2046989
What drives risk in China’s soybean futures market? Evidence from a flexible GARCH-MIDAS model
resolves10.1016/j.jfds.2016.12.001
Daily value-at-risk modeling and forecast evaluation: The realized volatility approach
resolves10.1016/j.jempfin.2006.02.001
Value-at-Risk analysis for long-term interest rate futures: Fat-tail and long memory in return innovations
resolves10.1016/j.eneco.2015.06.010
Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach
resolves10.1016/j.irfa.2023.102645
From BASEL III to BASEL IV and beyond: Expected shortfall and expectile risk measures
resolves10.1016/j.econmod.2017.02.014
Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model
resolves10.1016/j.eneco.2019.03.019
Oil price risk evaluation using a novel hybrid model based on time-varying long memory
resolves10.1016/j.jbankfin.2018.06.001
Value at risk and expected shortfall based on Gram-Charlier-like expansions
The 55 references without a DOI — listed, not checked
no DOI — not checkedref1
no DOI — not checkedref2
no DOI — not checkedref3
no DOI — not checkedref4
no DOI — not checkedNaN NaN gjr-GARCH-ged (1,1) [mean:NONE; train:500] gjr-GARCH-ged (1,1) [mean:NONE; train:500] gjr-GARCH-ged (1,1) [mean:NONE; train:500] gjr-GARCH-jsu (1,1) [mean:NONE; train:500] gjr-GARCH-jsu (1,1) [mean:NONE; train:500] gjr-GARCH-jsu (1,1) [mean:NONE; train:500] gjr-GARCH-sged (1,1) [mean:NONE; train:500] gjr-GARCH-sged (1,1) [mean:NONE; train:500] gjr-GARCH-sged (1,1)
no DOI — not checkedref6
no DOI — not checkedref7
no DOI — not checkedref8
no DOI — not checkedref9
no DOI — not checkedref10
no DOI — not checkedmean:NONE; train:500] gjr-GARCH-ged (1,1) [mean:NONE; train:500] gjr-GARCH-jsu (1,1) [mean:NONE; train:500] gjr-GARCH-jsu (1,1) [mean:NONE; train:500] gjr-GARCH-jsu (1,1) [mean:NONE; train:500] gjr-GARCH-sged (1,1) [mean:NONE; train:500] gjr-GARCH-sged (1,1) [mean:NONE; train:500] gjr-GARCH-sged (1,1)
no DOI — not checkedref12
no DOI — not checkedref13
no DOI — not checkedref14
no DOI — not checkedref15
no DOI — not checkedref16
no DOI — not checkedref17
no DOI — not checkedref18
no DOI — not checkedref19
no DOI — not checkedref20
no DOI — not checkedref21
no DOI — not checkedref22
no DOI — not checkedref23
no DOI — not checkedref24
no DOI — not checkedref25
no DOI — not checkedref26
no DOI — not checkedref27
no DOI — not checkedref28
no DOI — not checkedref29
no DOI — not checkedref30
no DOI — not checkedref31
no DOI — not checkedref33
no DOI — not checkedA robust VaR model under different time periods and weighting schemes
no DOI — not checkedref40
no DOI — not checkedRevisions to the Basel II market risk framework -updated as of 31 December
no DOI — not checkedBasel IV: Finalising post-crisis reforms
no DOI — not checkedCapital requirements for market risks: Value-at-risk models and stressed-VaR after the financial crisis
no DOI — not checkedForecasting Value-at-Risk of Cryptocurrencies with RiskMetrics type models
no DOI — not checkedMethods for evaluating value-at-risk estimates
no DOI — not checkedConditional density and value-at-risk prediction of Asian currency exchange rates
no DOI — not checkedImproving the accuracy of tail risk forecasting models by combining several realized volatility estimators
no DOI — not checkedref70
no DOI — not checkedref71
no DOI — not checkedVolatility and VaR forecasting in the Madrid Stock Exchange
no DOI — not checkedElicitability and backtesting: Perspectives for banking regulation
no DOI — not checkedref75
no DOI — not checkedGAS and GARCH based valueat-risk modeling of precious metals
no DOI — not checkedTail risk of electricity futures
no DOI — not checkedWeb of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today's
no DOI — not checkedref84
no DOI — not checkedDo bitcoin and precious metals do any good together? An extreme dependence and risk spillover analysis
no DOI — not checkedref86
no DOI — not checkedref114
no DOI — not checkedGlobal financial crisis and VaR performance in emerging markets: A case of EU candidate states-Turkey and Croatia
no DOI — not checkedRanking of VaR and ES Models: Performance in Developed and Emerging Markets
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-09-15 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.2139/ssrn.4586897"><img src="https://citestamp.com/citestamped/10.2139/ssrn.4586897/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.2139/ssrn.4586897)