Uncertainty analysis of flood quantile estimates with reference to Tanzania

Mkhandi, S.; Kachroo, R.K.; Guo, Shishang

doi:10.1016/0022-1694(95)02955-9

Cited by 11 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these contexts the uncertainty surrounding quantile-based risk assessments has been the subject of many papers (36,71,72,73,74,75) . Both model choice and parameter uncertainty has been considered.…”

Section: Application To Nonfinancial Problemsmentioning

confidence: 99%

Quantile Uncertainty and Value‐at‐Risk Model Risk

Alexander¹,

Sarabia²

2012

Risk Analysis

View full text Add to dashboard Cite

This article develops a methodology for quantifying model risk in quantile risk estimates. The application of quantile estimates to risk assessment has become common practice in many disciplines, including hydrology, climate change, statistical process control, insurance and actuarial science, and the uncertainty surrounding these estimates has long been recognized. Our work is particularly important in finance, where quantile estimates (called Value-at-Risk) have been the cornerstone of banking risk management since the mid 1980s. A recent amendment to the Basel II Accord recommends additional market risk capital to cover all sources of "model risk" in the estimation of these quantiles. We provide a novel and elegant framework whereby quantile estimates are adjusted for model risk, relative to a benchmark which represents the state of knowledge of the authority that is responsible for model risk. A simulation experiment in which the degree of model risk is controlled illustrates how to quantify Value-at-Risk model risk and compute the required regulatory capital add-on for banks. An empirical example based on real data shows how the methodology can be put into practice, using only two time series (daily Value-at-Risk and daily profit and loss) from a large bank. We conclude with a discussion of potential applications to nonfinancial risks.

show abstract

Section: Application To Nonfinancial Problemsmentioning

confidence: 99%

Quantile Uncertainty and Value‐at‐Risk Model Risk

Alexander¹,

Sarabia²

2012

Risk Analysis

View full text Add to dashboard Cite

show abstract

“…2 Another area where quantile risk assessments are frequently applied is statistical process control, for computing capability indices (Anghel, 2001), for measuring efficiency (Wheelocker and Wilson, 2008) and for reliability analysis (Unnikrishnan Nair and Sankaran, 2009). Several papers acknowledge the uncertainty surrounding quantile-based risk assessments: see Mkhandi et al (1996), Derman (1996), Jorion (1996), Green and Figlewski (1999), Brooks and Persand (2002), Peng and Qi (2006), Kerkhof et al (2010) and others. The term 'model risk' is frequently applied to encompass various sources of uncertainty in statistical models, but there is no general concensus on its precise definition.…”

Section: Introductionmentioning

confidence: 99%

Endogenizing Model Risk in Quantile Estimates

Alexander

Sarabia

2010

SSRN Journal

View full text Add to dashboard Cite

We quantify and endogenize the model risk associated with quantile estimates using a maximum entropy distribution (MED) as benchmark. Moment-based MEDs cannot have heavy tails, however generalized beta generated distributions have attractive properties for popular applications of quantiles. These are MEDs under three simple constraints on the parameters that explicitly control tail weight and peakness. Model risk arises because analysts are constrained to use a model distribution that is not the MED. Then the model's α quantile differs from the α quantile of the MED so the tail probability under the MED associated with the model's α quantile is not α, it is a random variable,α. Model risk is endogenized by parameterizing the uncertainty aboutα, whence the quantile becomes a generated random variable. To obtain a point model-risk-adjusted quantile, the generated distribution is used to adjust the model's α quantile for any systematic bias and uncertainty due to model risk. An illustration based on Value-at-Risk (VaR) computes a model-risk-adjusted VaR for risk capital reserves which encompass both portfolio and VaR model risk.

show abstract

Comparative study of flood quantiles estimation by nonparametric models

Kim

Heo

2002

Journal of Hydrology

View full text Add to dashboard Cite

Uncertainty analysis of flood quantile estimates with reference to Tanzania

Cited by 11 publications

References 6 publications

Quantile Uncertainty and Value‐at‐Risk Model Risk

Quantile Uncertainty and Value‐at‐Risk Model Risk

Endogenizing Model Risk in Quantile Estimates

Comparative study of flood quantiles estimation by nonparametric models

Contact Info

Product

Resources

About