Value at risk models for Dutch bond portfolios

Vlaar, Peter

doi:10.1016/s0378-4266(99)00068-0

Cited by 90 publications

(54 citation statements)

References 17 publications

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“…Hence, any erroneous estimation from these correlation parameters can be avoided. In addition, Vlaar (2000) confirms that the accuracy of VaR estimates increases as the sample of data covers a longer horizon. However, the issue on resolving how long the data must be set for a reliable value of VaR remains uncertain.…”

Section: Literature Reviewsupporting

confidence: 60%

Market Risk Quantifications: Historical Simulation Approach on the Malaysian Stock Exchange

Ulum

2013

International Journal of Management Excellence

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Section: Literature Reviewsupporting

confidence: 60%

Market Risk Quantifications: Historical Simulation Approach on the Malaysian Stock Exchange

Ulum

2013

International Journal of Management Excellence

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“…However, it has some drawbacks; if two of the most important parameters of the VaR estimation-the length of the historical data period and the confidence level-are not set correctly, the VaR estimations will be inaccurate [12], Brooks and Persand [13]. The data inputs may be the reason for significant differences in VaR estimations for the same days when different historical VaR approaches are applied (equally and exponentially moving average, historical simulation), while the discrepancies amongst different historical VaR approaches may be significantly larger when the 1% confidence level is chosen [14].…”

Section: Value At Risk: Practitioners Side Regulatorymentioning

confidence: 99%

Value at Risk (VaR) Historical Approach: Could It Be More Historical and Representative of the Real Financial Risk Environment?

Vasileiou¹

2017

TEL

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The purpose of this paper is to suggest a new approach that improves the conventional Historical Value-at-Risk (HVaR) estimations' accuracy and can be easily applied by anyone. The main assumption for the newly suggested method is "the more representative to the financial conditions the data inputs are, the better the VaR estimations". Volatility is assumed to be the criterion for the "representative to the financial conditions" definition. In practice, the newly suggested approach does not use the previous x days observations as data inputs in the estimation process (as the HVaR does), but the last x filtered volatility (fv) observations of a "representative to the current financial conditions dataset". Depending on the volatility value, each observation is classified in several regimes, from which the VaR is estimated depending on the examined day's volatility. This way the HVaR approach is more historical. The empirical findings using data from the US and the Eurozone stock market show that the newly suggested filtered volatility approach not only significantly improves the VaR estimations, but also makes these estimations much more representative of the real financial conditions. The results using the filtered volatility approach are comparable to some previously documented VaR estimations that adopt advanced econometric models. In this point, we should note advanced econometric models have the drawbacks that are not usually applied in financial markets industry because of their complexity. The newly suggested approach: (i) popularizes some of the most advanced econometric techniques, (ii) improves the VaR estimations accuracy, and (iii) enables financial risk analysts and portfolio managers to estimate the risk-return under several volatility regimes in order to help them to apply their desired investment strategy. Finally, this paper not only examines accuracy using the traditional/conventional tests [1], but also suggests some new measures for the comparison amongst different VaR models and their ability to accurately estimate the real financial risk.

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“…This assumption comprises three factors-the expected change in value, the degree of uncertainty, and the type of distribution (Vlaar, 2000). Kupiec (1995) …”

Section: Literature Reviewmentioning

confidence: 99%

Does the VaR Measurement Using Monte-Carlo Simulation Work in China?—Evidence from Chinese Listed Banks

Wang¹,

Song²,

Lin³

2017

JFRM

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There are usually great demands for risk control in the banking industry. Value at risk (VaR) is an important risk measurement in the Basel Accords, and Monte-Carlo simulation is a common method for VaR measurement. We conduct a series of Monte-Carlo simulation for VaR measurement based on the banks listed in the China stock market. Our study thinks that it is reliable to use Monte-Carlo simulation to measure VaR in Chinese banks. Therefore, we think that such VaR measurement works in China.

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Value at risk models for Dutch bond portfolios

Cited by 90 publications

References 17 publications

Market Risk Quantifications: Historical Simulation Approach on the Malaysian Stock Exchange

Market Risk Quantifications: Historical Simulation Approach on the Malaysian Stock Exchange

Value at Risk (VaR) Historical Approach: Could It Be More Historical and Representative of the Real Financial Risk Environment?

Does the VaR Measurement Using Monte-Carlo Simulation Work in China?—Evidence from Chinese Listed Banks

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