The hidden dangers of historical simulation

Pritsker, Matthew

doi:10.1016/j.jbankfin.2005.04.013

Cited by 180 publications

(109 citation statements)

References 30 publications

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“…Weaknesses and possible improvements for this method are studied in Pritsker (2006) and references therein.…”

Section: Historical Simulationmentioning

confidence: 99%

Market capitalization and Value-at-Risk

Dias

2013

Journal of Banking & Finance

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The potential of economic variables for financial risk measurement is an open field for research. This article studies the role of market capitalization in the estimation of Value-at-Risk (VaR). We test the performance of different VaR methodologies for portfolios with different market capitalization. We perform the analysis considering separately financial crisis periods and non-crisis periods.We find that VaR methods perform differently for portfolios with different market capitalization. For portfolios with stocks of different sizes we obtain better VaR estimates when taking market capitalization into account. We also find that it is important to consider crisis and non-crisis periods separately when estimating VaR across different sizes. This study provides evidence that market fundamentals are relevant for risk measurement.

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“…Weaknesses and possible improvements for this method are studied in Pritsker (2006) and references therein.…”

Section: Historical Simulationmentioning

confidence: 99%

Market capitalization and Value-at-Risk

Dias

2013

Journal of Banking & Finance

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“…However, the filtered historical simulation method is designed to improve on the shortcomings of historical simulation by augmenting the model-free estimates with parametric models. For example, Prisker [57] asserts that filtered historical simulation method compares favorably with historical simulation, the historical simulation method may not avoid the many shortcomings of purely model-free estimation approaches. When historical return series include insufficient extreme outcomes, the simulated value at risk may seriously undersestimate the actual market risk.…”

Section: Value-at-risk Resultsmentioning

confidence: 99%

The Stochastic Volatility Model, Regime Switching and Value-at-Risk (VaR) in International Equity Markets

Assaf¹

2017

JMF

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In this paper, we estimate two stochastic volatility models applied to international equity markets. The two models are the log-normal stochastic volatility (SV) model and the two-regime switching model. Then based on the one-dayahead forecasted volatility from each model, we calculate the Value-at-Risk (VaR) in each market. The estimated VaR measures from the SV are higher than those obtained from the regime-switching model for all markets and over all horizons. The exception is the Japanese market, where the stochastic volatility model generates low VaR estimates. Comparing those estimates with the unconditional return distribution, the two models generate smaller VaR measures; an evidence of the two models capturing volatility changes in international equity markets. Finally, we backtest each model and find that the performance of both models is the worst for the Canadian stock market, while the regime switching model does poorly for Germany. The results have significant implications for risk management, trading and hedging activities as well as in the pricing of equity derivatives.

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“…(Figure 6) We obtained the standardization, and satisfies the independence with the distributed residual, then using the Gaussian kernel function estimated that two kinds refer to the number the experience cumulative distribution function, this method causes to present the stepped the cumulative distribution function becomes smooth. Fits function the thought are as follows using the Gaussian kernel function [13][14] : …”

Section: The Establishment Of the Model And The Analysis Of The Resultsmentioning

confidence: 99%

Empirical Research on VAR Model Based on GJR-GARCH, EVT and Copula

Zhang¹,

Zhou²,

Ming³

et al. 2015

SJAMS

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Abstract:In this paper, we establish GJR-GARCH models to extract the residuals of logarithmic returns of two index---New York stock exchange composite index (NYA) and NASDAQ. and estimate the distribution function of the residuals utilizing Gaussian kernel method and Extreme Value Theory. The kernel cumulative distribution function estimates are well suited for the interior of the distribution where most of the residuals are found and the POT method of Extreme Value Theory fits the extreme residuals in upper and lower tails well. The monte carlo technique is used to simulate the income of securities index 20000 times after we get the marginal distribution of the residual income of securities index. Secondly, By using the copula function to get the joint distribution of mthe two stock index. Lastly, According to the theory of VAR calculate VAR value of the portfolio consisting of two equal weight comprehensive index in different confidence levels.

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The hidden dangers of historical simulation

Cited by 180 publications

References 30 publications

Market capitalization and Value-at-Risk

Market capitalization and Value-at-Risk

The Stochastic Volatility Model, Regime Switching and Value-at-Risk (VaR) in International Equity Markets

Empirical Research on VAR Model Based on GJR-GARCH, EVT and Copula

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