Value at Risk: The New Benchmark for Managing Financial Risk

By Phhilippe Jorion. 2000

3,353 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1cc90c5283679dc71d58f20175b55eea857924c8

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Citations in the corpus

Robustness of the Ljung-Box Test and its Rank Equivalent (2002, unknown): “See Jorion (2000) for background and references.”
Using extreme value theory to measure value-at-risk for daily electricity spot prices (2006, background): “Longin (1996) examines extreme movements in U.S. stock prices and shows that the extreme returns obey a Fréchet fattailed distribution. Ho, Burridge, Cadle, and Theobald (2000) and Gençay and Selçuk (2004) apply EVT to emerging stock markets which have been affected by a recent financial crisis.”
Dynamic Copula Modelling for Value at Risk (2006, unknown): “Therefore is suffices to discuss methods of VaR calculations using a long position (for more details see Jorion (2000), Tsay (2002)).”
VaR and expected shortfall in portfolios of dependent credit risks: Conceptual and practical insights (2002, background): “Often the performance of portfolio managers or even business units is measured by the ratio of (expected) profits and the amount of risk capital needed to sustain the portfolio; see, for example, Jorion (2001).”
Predicting Hedge Fund Failure: A Comparison of Risk Measures (2009, unknown): “previous research that finds performance as an important factor in predicting hedge fund failure (Liang (2000), BGP (2001), Malkiel and Saha (2005), and Chan et al.”
Nonparametric estimation of conditional VaR and expected shortfall (2008, background): “1 See, to name just a few, Morgan (1996), Duffie and Pan (1997), Jorion (2001) and Duffie and Singleton (2003) for financial background, statistical inferences, and various applications.”
The Ten Commandments for Optimizing Value-at-Risk and Daily Capital Charges (2009, unknown): “models to calculate their Value-at-Risk (VaR) thresholds (see Jorion (2000) for a”
Value-at-Risk for long and short trading positions (2003, background): “…and the well-publicized trading loss of well known financial institutions (see Jorion, 2000, for a brief history of these events) has led financial regulators and supervisory committee of banks to favor quantitative techniques which appraise the possible loss that these institutions can incur.”
Generating synthetic data in finance: opportunities, challenges and pitfalls (2020, result): “Alternatively, the evaluation of certain quantities such as Value-at-Risk (VaR) [55] is “backtested”, whereby historical data for a time period is used to train a generative model, at which point the true VaR observed in the subsequent historical period is compared with that obtained by the generati”
A comprehensive review of Value at Risk methodologies (2014, background): “As is well known, all these methodologies, usually called standard models, have numerous shortcomings, which have led to the development of new proposals (see Jorion, 2001).”