Results 111 to 120 of about 50,970 (142)
VaR (Value at Risk) Model [PDF]
The VaR model represents a significant progress in risk analysis, among the improvements it brings we can outline the attempt to measure risk itself in terms of an eventual loss, instead of focusing on gain-based approach.
Vergil VOINEAGU, Danut CULETU
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Artifactual unit root behavior of Value at risk (VaR)
Statistics & Probability Letters, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chan, Ngai Hang, Sit, Tony
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BANKPEDIA REVIEW, 2013
The value at risk (VaR) measures the risk of loss associated to financial assets. For a given time period (normally ranging from 1 to 10 days), and with a given probability confidence (generally equal to 95% or 99%); this measure represents the maximum loss the investor can suffer when holding financial assets.
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The value at risk (VaR) measures the risk of loss associated to financial assets. For a given time period (normally ranging from 1 to 10 days), and with a given probability confidence (generally equal to 95% or 99%); this measure represents the maximum loss the investor can suffer when holding financial assets.
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Portfolio risk measurement based on value at risk (VaR)
AIP Conference Proceedings, 2018Generally, the risk level of an investment is directly correlated with the returns to be earned by investors in the future. In current situation, it is difficult for investors, shareholders and financial managers to determine the total loss of their asset portfolio because standard deviation is insufficient to describe the actual total loss. Therefore,
Farah Azaliney Mohd Amin +3 more
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2010
In this chapter we review the main market risk measurement tool used in banking, known as value-at-risk (VaR). The review looks at the three main methodologies used to calculate VaR, as well as some of the key assumptions used in the calculations, including those on the normal distribution of returns, volatility levels and correlations. We also discuss
Moorad Choudhry +4 more
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In this chapter we review the main market risk measurement tool used in banking, known as value-at-risk (VaR). The review looks at the three main methodologies used to calculate VaR, as well as some of the key assumptions used in the calculations, including those on the normal distribution of returns, volatility levels and correlations. We also discuss
Moorad Choudhry +4 more
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Range-based models in estimating value-at-risk (VaR) [PDF]
This paper introduces new methods of estimating Value-at-Risk (VaR) using range-based GARCH (general autoregressive conditional heteroskedasticity) models. These models, which could be based on either the Parkinson range or the Garman-Klass range, are applied to ten stock market indices of selected countries in the Asia-Pacific region.
Mapa, Dennis, Beronilla, Nikkin
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Value-at-Risk dynamics: a copula-VAR approach
The European Journal of Finance, 2019In financial research and among risk management practitioners the estimation of a correct measure of the Value-at-Risk still proves interesting.
Giovanni de Luca +2 more
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Value-at-Risk (VaR) Computations Under Various VaR Models and Stress Testing
Journal of Transnational Management Development, 2004SUMMARY Bank for International Settlements (BIS) proposes that all banks calculate and report amount of market risk they incur and allocate sufficient amount of capital starting at the beginning of year 2002. BIS also suggests that value-at-risk (VaR) models in computing market risk should be used.
Suat Teker, M. Baris Akçay
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Conditional Expectile: An Alternative to Value at Risk (VaR)
SSRN Electronic Journal, 2021Various risk measures have been reviewed against the criteria commonly accepted by financial researchers and practitioners: coherence, elicitability, comonotonic additivity, and intuitiveness. It follows that the only risk measure that is both coherent and elicitable is an Expectile based risk measure. But unlike the VaR measure, the Expectile does not
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Review on Three New Value at Risk (VaR) Models
Advances in Economics, Management and Political Sciences, 2023The emergence of financial derivatives complicates traditional financial products and increases financial market volatility. Individuals and financial institutions are both exposed to more complex and uncontrollable risks in this environment. Because of the risk's uncertainty, we must use reasonable methods to predict and estimate it in order to ...
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