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Tail model risk in a stress scenario for house prices

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TAIL MODEL RISK IN A STRESS SCENARIO FOR HOUSE PRICES.pdf (358.43 KB)
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Sarmiento, Camilo
Abstract (Spanish)
Este artículo incorpora incertidumbres del riesgo del modelo en un escenario de estrés para los precios de la vivienda. Nuestro enfoque consiste en mapear los cuantiles de distribución gaussiana (u otra alternativa) a los cuantiles de la distribución empírica usando un criterio estadístico. El mapeo corrige la presencia de colas más gruesas (en relación con la distribución gaussiana) en la distribución reparametrizada. El foco del análisis está en la cola de la distribución, que está asociada con eventos adversos inciertos. La aplicación del modelo a los precios de la vivienda indica la relevancia de nuestro mapeo en términos de capturar las caídas de los precios de las viviendas observadas en la Gran Recesión. La derivación de intervalos de confianza, además, proporciona reservas para el riesgo del modelo. Por ejemplo, si bien el capital regulatorio puede utilizar la estimación puntual del cuantil de la distribución, las organizaciones de gestión de riesgos pueden utilizar estos intervalos de confianza para reforzar la resiliencia de instituciones financieras a la caída de los precios de las viviendas.
Abstract (English)
This paper incorporates uncertainties of model risk in a stress scenario for house prices. Our approach consists of mapping the Gaussian (or other alternative) distribution quantiles to the quantiles of the empirical distribution using a statistical criterion. The mapping corrects for the presence of fatter tails (relative to the Gaussian distribution) into the re-parameterized distribution. The focus of the analysis is at the tail of the distribution, which is associated with uncertain adverse events. The application of the model to house prices indicates the relevance of our mapping in terms of capturing the home price declines observed in the Great Recession. The derivation of confidence intervals, furthermore, provides buffers for model risk. For example, while regulatory capital may use the point estimate of the quantile of the distribution, risk management organizations may use this confidence intervals to bolster resilience of financial institutions to home price declines.
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10 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/3416
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AG - Centro de Estudios Económicos
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