Extreme quantile estimation based on financial time series

Dutta, Santanu; Biswas, Suparna

doi:10.1080/03610918.2015.1112908

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…From the literature, we observe that though a risk measure's interpretation might be straightforward estimating the risk measure is not (see [11]) because simplifying assumptions about the loss distribution is required. We find a huge literature proposing different estimators of VaR and ES and some estimators have proven to be superior to others; see McNeil and Frey [12]; Abad et al [13]; Dutta and Biswas [14]; Nadarajah et. al.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Estimation of Range Value at Risk

Biswas

Sen

2023

Computation

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Range value at risk (RVaR) is a quantile-based risk measure with two parameters. As special examples, the value at risk (VaR) and the expected shortfall (ES), two well-known but competing regulatory risk measures, are both members of the RVaR family. The estimation of RVaR is a critical issue in the financial sector. Several nonparametric RVaR estimators are described here. We examine these estimators’ accuracy in various scenarios using Monte Carlo simulations. Our simulations shed light on how changing p and q with respect to n affects the effectiveness of RVaR estimators that are nonparametric, with n representing the total number of samples. Finally, we perform a backtesting exercise of RVaR based on Acerbi and Szekely’s test.

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Section: Introductionmentioning

confidence: 99%

Nonparametric Estimation of Range Value at Risk

Biswas

Sen

2023

Computation

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“…In other words, we consider a case in which the CCP estimates the unconditional margin level from historical data which, unbeknownst to the CCP, comes from a GARCH process. (In their simulation study, Dutta and Biswas [14] find that the sample quantile estimator performs about as well as the extreme value estimator in GARCH models; see their Table 10 and especially their model (ix) which has parameters similar to those we find in Table 1. This lends further support to our focus on Û p .…”

Section: Quantile Estimationmentioning

confidence: 59%

“…|X ∞ | > u) = E[P(σ ∞ |Z| > u|Z)],and, for Z = 0,(13) givesP(σ ∞ |Z| > u|Z) ∼ (c 0 |Z| κ )u −κ .Thus,(14) is an interchange of limit and expectation:lim u→∞ u κ E[P(σ ∞ |Z| > u|Z)] = E[ lim u→∞ u κ P(σ ∞ |Z| > u|Z)] = c 0 E[|Z| κ ]. (29)…”

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confidence: 99%

Persistence and Procyclicality in Margin Requirements

Glasserman

2018

Management Science

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Margin requirements for derivative contracts serve as a buffer against the transmission of losses through the financial system by protecting one party to a contract against default by the other party. However, if margin levels are proportional to volatility, then a spike in volatility leads to potentially destabilizing margin calls in times of market stress. Risk-sensitive margin requirements are thus procyclical in the sense that they amplify shocks. We use a GARCH model of volatility and a combination of theoretical and empirical results to analyze how much higher margin levels need to be to avoid procyclicality while reducing counterparty credit risk. Our analysis compares the tail decay of conditional and unconditional loss distributions to compare stable and risk-sensitive margin requirements. Greater persistence and burstiness in volatility leads to a slower decay in the tail of the unconditional distribution and a higher buffer needed to avoid procyclicality. The tail decay drives other measures of procyclicality as well. Our analysis points to important features of price time series that should inform "anti-procyclicality" measures but are missing from current rules.

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“…Then it discussed about some probabilistic and statistical models like non-Gaussian to overcome such problem. Recent study by David, Abhay and Robert (2011) and Dutta & Biswas (2017) study confirms that financial tail data need special attention. It is clear that financial data do have fat tails and over the period of time different researchers has followed different methodology to overcome it but unable to capture as a whole.…”

Section: Review Of Literaturementioning

confidence: 86%

OLS versus quantile regression in extreme distributions

Maiti

2018

CyA

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Financial data mostly have fat tail and an analyst is much concerned about the tail part. Most of the study in finance extensible uses linear regression but when it comes to tail analysis it becomes ineffective. So, the present study tries to address the same by using Quantile regression in the tail analysis to study the value effect in 10 portfolios formed from BSE 500 stocks based on P/B ratio. The study result clearly indicates that Quantile regression estimates give more comprehensive and vibrant picture of the unpredictable effect of the predictors on the response variables.

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Extreme quantile estimation based on financial time series

Cited by 8 publications

References 13 publications

Nonparametric Estimation of Range Value at Risk

Nonparametric Estimation of Range Value at Risk

Persistence and Procyclicality in Margin Requirements

OLS versus quantile regression in extreme distributions

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