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  • 1
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    Amsterdam and Rotterdam: Tinbergen Institute
    Publication Date: 2013-11-13
    Description: In this paper we provide further evidence on the suitability of the median of the point VaR forecasts of a set of models as a GFC-robust strategy by using an additional set of new extreme value forecasting models and by extending the sample period for comparison. These extreme value models include DPOT and Conditional EVT. Such models might be expected to be useful in explaining financial data, especially in the presence of extreme shocks that arise during a GFC. Our empirical results confirm that the median remains GFC-robust even in the presence of these new extreme value models. This is illustrated by using the S&P500 index before, during and after the 2008-09 GFC. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria, including several tests for independence of the violations. The strategy based on the median, or more generally, on combined forecasts of single models, is straightforward to incorporate into existing computer software packages that are used by banks and other financial institutions.
    Keywords: G32 ; G11 ; G17 ; C53 ; C22 ; ddc:330 ; Value-at-Risk (VaR) ; DPOT ; daily capital charges ; robust forecasts ; violation penalties ; optimizing strategy ; aggressive risk management ; conservative risk management ; Basel ; global financial crisis ; Finanzkrise ; Basler Akkord ; Portfolio-Management ; Risikomaß ; Prognoseverfahren ; Extremwerttheorie ; Welt
    Language: English
    Type: doc-type:workingPaper
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  • 2
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    Amsterdam and Rotterdam: Tinbergen Institute
    Publication Date: 2013-11-13
    Description: The Basel II Accord requires that banks and other Authorized Deposit-taking Institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous violations, whereby realised losses exceed the estimated VaR. In this paper we define risk management in terms of choosing from a variety of risk models, and discuss the selection of optimal risk models. A new approach to model selection for predicting VaR is proposed, consisting of combining alternative risk models, and we compare conservative and aggressive strategies for choosing between VaR models. We then examine how different risk management strategies performed during the 2008-09 global financial crisis. These issues are illustrated using Standard and Poor’s 500 Composite Index.
    Keywords: G32 ; G11 ; G17 ; C53 ; C22 ; ddc:330 ; Value-at-Risk (VaR) ; daily capital charges ; violation penalties ; optimizing strategy ; risk forecasts ; aggressive or conservative risk management strategies ; Basel Accord ; global financial crisis ; Basler Akkord ; Einlagengeschäft ; Risikomaß ; Unternehmenspublizität ; Finanzkrise ; Welt
    Language: English
    Type: doc-type:workingPaper
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  • 3
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    Amsterdam and Rotterdam: Tinbergen Institute
    Publication Date: 2016-12-19
    Description: The Basel Committee on Banking Supervision (BCBS) (2013) recently proposed shifting the quantitative risk metrics system from Value-at-Risk (VaR) to Expected Shortfall (ES). The BCBS (2013) noted that "a number of weaknesses have been identified with using VaR for determining regulatory capital requirements, including its inability to capture tail risk" (p. 3). For this reason, the Basel Committee is considering the use of ES, which is a coherent risk measure and has already become common in the insurance industry, though not yet in the banking industry. While ES is mathematically superior to VaR in that it does not show "tail risk" and is a coherent risk measure in being subadditive, its practical implementation and large calculation requirements may pose operational challenges to financial firms. Moreover, previous empirical findings based only on means and standard deviations suggested that VaR and ES were very similar in most practical cases, while ES could be less precise because of its larger variance. In this paper we find that ES is computationally feasible using personal computers and, contrary to previous research, it is shown that there is a stochastic difference between the 97.5% ES and 99% VaR. In the Gaussian case, they are similar but not equal, while in other cases they can differ substantially: in fat-tailed conditional distributions, on the one hand, 97.5%-ES would imply higher risk forecasts, while on the other, it provides a smaller down-side risk than using the 99%-VaR. It is found that the empirical results in the paper generally support the proposals of the Basel Committee.
    Keywords: C53 ; C22 ; G32 ; G11 ; G17 ; ddc:330 ; Stochastic dominance ; Value-at-Risk ; Expected Shortfall ; Optimizing strategy ; Basel III Accord
    Language: English
    Type: doc-type:workingPaper
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  • 4
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    Amsterdam and Rotterdam: Tinbergen Institute
    Publication Date: 2016-04-21
    Description: The Basel II Accord requires that banks and other Authorized Deposit-taking Institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous violations, whereby realised losses exceed the estimated VaR. In this paper we define risk management in terms of choosing sensibly from a variety of risk models, discuss the selection of optimal risk models, consider combining alternative risk models, discuss the choice between a conservative and aggressive risk management strategy, and evaluate the effects of the Basel II Accord on risk management. We also examine how risk management strategies performed during the 2008-09 financial crisis, evaluate how the financial crisis affected risk management practices, forecasting VaR and daily capital charges, and discuss alternative policy recommendations, especially in light of the financial crisis. These issues are illustrated using Standard and Poor’s 500 Index, with an emphasis on how risk management practices were monitored and encouraged by the Basel II Accord regulations during the financial crisis.
    Keywords: G32 ; G11 ; G17 ; C53 ; C22 ; ddc:330 ; Value-at-Risk (VaR) ; daily capital charges ; exogenous and endogenous violations ; violation penalties ; optimizing strategy ; risk forecasts ; aggressive or conservative risk management strategies ; Basel II Accord ; financial crisis ; Basler Akkord ; Unternehmenspublizität ; Bankenliquidität ; Risikomaß ; Risikomanagement ; Finanzkrise
    Language: English
    Type: doc-type:workingPaper
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  • 5
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    Amsterdam and Rotterdam: Tinbergen Institute
    Publication Date: 2016-04-16
    Description: Bank risk managers follow the Basel Committee on Banking Supervision (BCBS) recommendations that recently proposed shifting the quantitative risk metrics system from Value-at-Risk (VaR) to Expected Shortfall (ES). The Basel Committee on Banking Supervision (2013, p. 3) noted that: “a number of weaknesses have been identified with using VaR for determining regulatory capital requirements, including its inability to capture tail risk”. The proposed reform costs and impact on bank balances may be substantial, such that the size and distribution of daily capital charges under the new rules could be affected significantly. Regulators and bank risk managers agree that all else being equal, a “better” distribution of daily capital charges is to be preferred. The distribution of daily capital charges depends generally on two sets of factors: (1) the risk function that is adopted (ES versus VaR); and (2) their estimated counterparts. The latter is dependent on what models are used by bank risk managers to provide for forecasts of daily capital charges. That is to say, while ES is known to be a preferable “risk function” based on its fundamental properties and greater accounting for the tails of alternative distributions, that same sensitivity to tails can lead to greater daily capital charges, which is the relevant (that is, controlling) practical reference for risk management decisions and observations. In view of the generally agreed focus in this field on the tails of non-standard distributions and low probability outcomes, an assessment of relative merits of estimated ES and estimated VaR is ideally not limited to mean variance considerations. For this reason, robust comparisons between ES and VaR will be achieved in the paper by using a Stochastic Dominance (SD) approach to rank ES and VaR.
    Keywords: G32 ; G11 ; G17 ; C53 ; C22 ; ddc:330 ; Stochastic dominance ; Value-at-Risk ; Expected Shortfall ; Optimizing strategy ; Basel III Accord
    Language: English
    Type: doc-type:workingPaper
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  • 6
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    Munich: Center for Economic Studies and Ifo Institute (CESifo)
    Publication Date: 2018-11-19
    Description: This paper investigates the impact of international shocks - interest rate, commodity price and industrial production shocks - on key macroeconomic variables in ten Central and Eastern European (CEE) countries by using near-VAR models and monthly data from the early 1990s to 2009. In contrast to previous work, the empirical analysis takes explicit account of the possibility of (multiple) structural breaks in the underlying time series. We establish strong evidence of structural breaks, particularly along the years 2007 and 2008, suggesting the very relevant impact of the recent global crisis on CEE economies. Moreover, our results suggest that the way how countries react to world commodity price shocks is related to the underlying economic structure and the credibility of the monetary policy. We also find that some countries like Slovakia and Slovenia - already euro area members - react stronger to foreign industrial production shocks than other countries and that the responses to such shocks are strongly correlated for selected CEE countries. Nevertheless, our results also shed light on substantial differences in responses to foreign interest rate shocks that originate from the US or the euro area.
    Keywords: E43 ; E50 ; E52 ; C22 ; O52 ; ddc:330 ; monetary policy ; foreign shocks ; multiple structural breaks ; near-VAR model ; CEE economies ; Schock ; Zins ; International ; Weltkonjunktur ; Makroökonomischer Einfluss ; VAR-Modell ; Schätzung ; Ostmitteleuropa
    Language: English
    Type: doc-type:workingPaper
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