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We thank Francis X. Diebold for comments and suggestions. Financial support from the Spanish Department of Economy (ECO2009-11151 and ECO2011-29751 projects) and Navarra Government (Jerónimo de Ayanz project) is gratefully acknowledged.
In microprudential risk management, the reasons explaining this asymmetric pattern are mainly related to how investors who own bank stocks and/or deposits perceive risk. Broadly speaking, stockholders care differently about large downside losses than they do about upside gains, showing greater sensitiveness to reductions in their level of financial wealth. This behavior is consistent with loss aversion or decreasing absolute risk aversion preferences; see, among others, the theoretical models in Gul (1991), Barberis et al. (2001), Berkelaar and Kouwenberg (2009), and the empirical evidence in McQueen and Vorkink (2004), Ang et al. (2005) and Bali et al. (2009).
As of September 30, 2011, 37 bank holding companies with assets over $50 billion, held $4.14 trillion insured deposits accounting for 61 percent of all insured deposits in the United States.
Note that we can reverse the model and focus on the returns of the individual bank as a function of the sytem in a more familiar representation closer to the CAPM and, particularly, the semi-variance models. Under this representation, the CoVaR would measure the exposure of the individual bank to the system.
The volatility equation can be reparameterized trivially as σt (Xt,i) = σ0,i – σ1,i|Xt,i| × I(Xt,i < 0) + σ2,i|Xt,i| × I(Xt,i ≥ 0). Absolute-valued returns sample at a high-frequency are an unbiased (but particularly noisy) proxy of the latent volatility process, so σt (Xt,i) can be seen as a function of the volatility of the individual returns.
The average value of the LIBOR-OIS spread over August 2007 through March 2009 reached 981 basis points, well above the pre-crisis average value of 104 basis points.
In our view, using smoothing techniques may be preferable because it allows us to avoid seasonal effects related to the timing in which accounting information is updated.
The average number of time-series observations is 781. The distribution of the number of available observations in our sample does not seem to be related to firm-specific characteristics. For instance, the cross-sectional correlation between available observations and average size over the period is 4.96 percent.
The BCBS Bank for International Settlements (2011) has developed an indicator-based measurement approach to identify and measure the systemic importance of banks. There are five indicators, namely, bank size, interconnectedness, substituitability, cross-border activity, and complexity. Size, measure by total exposures, account for 20 percent of the overall index.
In October 2010, the Financial Stability Board recommended that all jurisdictions should put in place a policy framework to reduce risks and externalities associated with domestic and global systemically important financial institutions, combining higher capital buffers, an efficient resolution mechanism, and more intensive supervisory oversight.