IMF Working Papers Volume 2007 Issue 290: Stochastic Volatilities and Correlations, Extreme Values and Modeling the Macroeconomic Environment, Under Which Brazilian Banks Operate (2007)

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Stochastic Volatilities and Correlations, Extreme Values and Modeling the Macroeconomic Environment, Under Which Brazilian Banks Operate

Author:

Mr. Marcos R Souto

Mr. Marcos R Souto
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and

Mr. Theodore M. Barnhill

Mr. Theodore M. Barnhill null

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Series:: IMF Working Papers

Volume/Issue:: Volume 2007: Issue 290

Language:: English

Publication Date:: 01 Dec 2007

DOI:: https://doi.org/10.5089/9781451868531.001

ISBN:: 9781451868531

ISSN:: 1018-5941

Pages:: 52

Keywords:: WP; FX rate; normal distribution; time series; Forecasting; stochastic volatility; fat-tail distributions; Monte Carlo estimation; Br rate; Wilk-Shapiro statistics; standard deviation; covariances model; Oil; Gold; Credit risk; Credit

Legal-disclaimer:: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Series:: IMF Working Papers

Volume/Issue:: Volume 2007: Issue 290

Language:: English

Publication Date:: 01 Dec 2007

DOI:: https://doi.org/10.5089/9781451868531.001

ISBN:: 9781451868531

ISSN:: 1018-5941

Pages:: 52

Keywords:: WP; FX rate; normal distribution; time series; Forecasting; stochastic volatility; fat-tail distributions; Monte Carlo estimation; Br rate; Wilk-Shapiro statistics; standard deviation; covariances model; Oil; Gold; Credit risk; Credit

Legal-disclaimer:: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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Using monthly data for a set of variables, we examine the out-of-sample performance of various variance/covariance models and find that no model has consistently outperformed the others. We also show that it is possible to increase the probability mass toward the tails and to match reasonably well the historical evolution of volatilities by changing a decay factor appropriately. Finally, we implement a simple stochastic volatility model and simulate the credit transition matrix for two large Brazilian banks and show that this methodology has the potential to improve simulated transition probabilities as compared to the constant volatility case. In particular, it can shift CTM probabilities towards lower credit risk categories.

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IMF Working Papers

ISSN:: 1018-5941

Publisher:: International Monetary Fund

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