Value at Risk (VaR) is a market risk measure widely used by risk managers and market regulatory authorities. There is a variety of methodologies proposed in the literature for the estimation of VaR. However, few of them get to say something about its distribution or its confidence intervals. This paper compares different methodologies for computing such intervals. Several methods, based on asymptotic normality, extreme value theory and subsample bootstrap, are used. Using Monte Carlo simulations, it is found that these approaches are only valid for high quantiles. In particular, there is a good performance for VaR(99%), in terms of coverage rates, and bad performance for VaR(95%) and VaR(90%). The results are confirmed by an empirical application for the stock market index returns of G7 countries.
We compute a measure of the finance-neutral potential output for Colombia, Chile and Mexico. Our methodology is based on Borio et al (2013, 2014) and incorporates the cycle of credit, house prices and the real exchange rate on the computation of the output gap. The literature on business cycles in emerging market economies, particularly papers focusing on Latin American economies, has highlighted the importance of including shocks to the interest rate in world capital markets together with financial frictions; terms of trade fluctuations; and a procyclical government spending process. Our results show that around the financial crises of the 1990s the finance-neutral output gap behaved differently than the traditional measures observed by policymakers. In particular, gaps are higher before crises and lower after them.
We study the interdependence between real commodity prices and world real GDP using long-term annual data since 1870, by performing two empirical exercises. First, we compute long-term and medium-term cycles and measure their degree of synchronization for different leads and lags. Second, we perform several causality tests in order to better understand the nature of their interdependence. Our results show that GDP and commodity-price cycles are correlated, and there is evidence of short-term causality between them. However, there is no evidence of Granger causality from GDP to medium and long term cycles of commodity prices. This finding is consistent with the technology-based theories of commodity-price cycles. Searching for a supply-side determinant, we study the interdependence between oil-price and the remaining commodity-price cycles. Our results imply that oil prices are key drivers of metal price cycles for all fluctuation frequencies.
This paper presents the first version of SYSMO, the analytical framework employed by the Financial Stability Department at the Banco de la República (the Central Bank of Colombia) to perform its biannual, top-down, stress testing exercise. The framework comprises: (i) a module to produce internally consistent macroeconomic scenarios; (ii) a set of satellite risk models that capture the materialization of credit and market risks in times of stress, and (iii) a bank model that simulates the endogenous response of banks to an adverse scenario. The framework also incorporates endogenous contagion and funding risks, key regulatory constraints (solvency and liquidity), and the feedback effects between the endogenous response of banks and the macroeconomic scenario. The use of SYSMO is illustrated with the example of the stress testing exercise published in the Banco de la República's Financial Stability Report of the second semester of 2017.
Value at Risk (VaR) is a market risk measure widely used by risk managers and market regulatory authorities. There is a variety of methodologies proposed in the literature for the estimation of VaR. However, few of them get to say something about its distribution or its confidence intervals. This paper compares different methodologies for computing such intervals. Several methods, based on asymptotic normality, extreme value theory and subsample bootstrap, are used. Using Monte Carlo simulations, it is found that these approaches are only valid for high quantiles.In particular, there is a good performance for VaR(99%), in terms of coverage rates, and bad performance for VaR(95%) and VaR(90%). The results are confirmed by an empirical application for the stock market index returns of G7 countries.
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