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Ebook Credit Risk and IFRS: The Case of Credit Default Swaps

... the impact of International Financial Reporting Standards (IFRS) on the pricing of credit risk in the over-the-counter Credit Default ... bonds or credit ratings. Callen, Livnat, and Segal (2009) show that U.S. GAAP earnings are an important determinant of CDS spreads. ...

Story - puput - 02/21/2011 - 04:25 - 0 comments - 0 attachments

Ebook Accounting Discretion, Loan Loss Provisioning, and Discipline of Banks’ Risk-Taking

... high profile proposals by Financial Stability Forum (2009) and U.S. Treasury (2009) call for standards setters to re-evaluate the ... Current accounting procedures under both U.S. GAAP and IFRS utilize an incurred loss framework where a provision for loan losses is ...

Story - puput - 11/03/2010 - 04:25 - 0 comments - 0 attachments


PDF Ebook Cost of Capital Effects and Changes in Growth Expectations around U.S. Cross-Listings

Submitted by antoq on Sat, 04/03/2010 - 07:28

There is mounting evidence that countries’ institutional frameworks play an important role for access to finance and equity valuations (e.g., La Porta et al, 1997 and 2002). In light of this evidence, cross-listing in the U.S. has been suggested as a way for firms from countries with poor institutions to privately overcome these shortcomings (Coffee, 1999; Stulz, 1999). Consistent with this notion, several studies document that cross-listings have significant effects on firms’ market values, using either event-study returns (e.g., Foerster and Karolyi, 1999; Miller, 1999; Lee, 2004) or comparisons with firms that are not cross-listed (e.g., Doidge, 2004; Doidge et al., 2004, 2008a). This evidence suggests that U.S. cross-listings offer substantial benefits. However, the sources of these benefits are not yet well understood (e.g., Leuz, 2003; Doidge et al., 2004).

One important question is whether and to what extent cross-listing in the U.S. affects firms’ cost of capital. The bonding argument suggests that a U.S. cross-listing strengthens outside investor protection making it easier for firms to raise external finance (e.g., Reese and Weisbach, 2002; Benos and Weisbach, 2004; Doidge et al., 2004). Moreover, listings on NASDAQ, NYSE or AMEX require foreign firms to comply with SEC disclosure rules, which typically imply a substantial increase in disclosure and could manifest in a lower cost of capital (e.g., Verrecchia, 2001; Lambert et al., 2007).Similarly, U.S. cross-listings can improve investor recognition and enlarge a firm’s investor base (e.g., Merton, 1987; Foerster and Karolyi, 1999).

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Ebook Asset Returns and the Business Cycle in a One Sector Production Economy

Submitted by puput on Wed, 01/13/2010 - 03:20

Modelling asset prices in a production economy has long been a challenge for students of the business cycle. In particular, as shown by Rouwenhorst (1995), standard dynamic stochastic general equilibrium (DSGE) models with time separable utility usually fail to solve the equity premium puzzle identified by Merha and Prescott (1985), i.e. the fact that the yearly average return to equity exceeds the risk free rate by more than 6%.

In an endowment economy, any trick that increases the volatility of the stochastic discount factor is a potential solution to the equity premium puzzle 5 though some of these tricks might not be empirically credible, e.g. increasing the curvature of the utility function to very high levels. In contrast, in a production economy, consumption is endogenous. Such an environment offers its agents a number a ways of smoothing their consumption path. As a consequence, when utility is time separable, increasing risk aversion does not necessarily result in an increased volatility of the stochastic discount factor. Another challenge emerges that consists in generating sufficiently volatile capital gains. Lettau (2003) and Rouwenhorst (1995) show that standard DSGE models fail to generate enough volatility in these items.

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Ebook Analysis Of Dynamic Protein Expression Data

Submitted by wulan on Thu, 07/30/2009 - 03:51

While the focus of biochemical research was addressed on the genome in the last decade the view is now turned onto the proteome. Big data sets of gene expression obtained from DNA-microarrays made the development of statistical methods necessary to make correct inferences from these measurements. For quantitative protein expression analysis either mass spectrometry (cf. Aebersold and Goodlett ([1]) and Gygi et al. ([7])) or two-dimensional gel electrophoresis (2-DE) (cf. Westermeier et al. ([14])) is applied. In this paper we focus on the analysis of protein expression data obtained from a new detection method (Difference GelElectrophoresis,DIGE) based on fluorescence labelling before 2-DE. 2-DE separates the proteins of a mixture by their isoelectric point (pI) and molecular size to distinct spots. After separation the proteins are detected using a confocal fluorescence scanner whereas fluorescence intensity of a spot can be regarded as a measure of expression for its respective protein. DIGE enables the user to put up to three different mixtures of proteins on the same gel.

The different mixtures are labelled by different fluorescence dyes (Cy2, Cy3 and Cy5). For quantitative proteome analysis image analysis software automatically determines the boundaries and sizes of the spots. Usually, a DIGE experiment is designed such that m independent replications of treatment and control mixtures are put on the same m gels. The internal standard, a mixture of same amounts of all m treatment and m control probes, is also put on each gel. This internal standard allows high accuracy calibration of the expression values. Calibration and normalization of protein expression data is reviewed in section 2. In order to obtain information about interactions of treatment and control with the time, DIGE experiments often include measurements over several time points. Known statistical methods for the analysis of longitudinal data can be used to analyze those experiments. One possible method for such an analysis is detailed in section 3. Often, 2-DE data contains up to 50% of missing values.

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