3 resultados para évaluation continue

em University of Connecticut - USA


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In this paper, we extend the debate concerning Credit Default Swap valuation to include time varying correlation and co-variances. Traditional multi-variate techniques treat the correlations between covariates as constant over time; however, this view is not supported by the data. Secondly, since financial data does not follow a normal distribution because of its heavy tails, modeling the data using a Generalized Linear model (GLM) incorporating copulas emerge as a more robust technique over traditional approaches. This paper also includes an empirical analysis of the regime switching dynamics of credit risk in the presence of liquidity by following the general practice of assuming that credit and market risk follow a Markov process. The study was based on Credit Default Swap data obtained from Bloomberg that spanned the period January 1st 2004 to August 08th 2006. The empirical examination of the regime switching tendencies provided quantitative support to the anecdotal view that liquidity decreases as credit quality deteriorates. The analysis also examined the joint probability distribution of the credit risk determinants across credit quality through the use of a copula function which disaggregates the behavior embedded in the marginal gamma distributions, so as to isolate the level of dependence which is captured in the copula function. The results suggest that the time varying joint correlation matrix performed far superior as compared to the constant correlation matrix; the centerpiece of linear regression models.

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This paper develops a reduced form three-factor model which includes a liquidity proxy of market conditions which is then used to provide implicit prices. The model prices are then compared with observed market prices of credit default swaps to determine if swap rates adequately reflect market risks. The findings of the analysis illustrate the importance of liquidity in the valuation process. Moreover, market liquidity, a measure of investors. willingness to commit resources in the credit default swap (CDS) market, was also found to improve the valuation of investors. autonomous credit risk. Thus a failure to include a liquidity proxy could underestimate the implied autonomous credit risk. Autonomous credit risk is defined as the fractional credit risk which does not vary with changes in market risk and liquidity conditions.

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I propose that the Last in, First out (LIFO) inventory valuation method needs to be reevaluated. I will evaluate the impact of the LIFO method on earnings of publically traded companies with a LIFO reserve over the past 10 years. I will begin my proposal with the history of how the LIFO method became an acceptable valuation method and discuss the significance of LIFO within the accounting profession Next I will provide a description of LIFO, the First in, First out (FIFO), and the weighted average inventory valuation methods and explore the differences among each. More specifically, I will explore the arguments for and against the use of the LIFO method and the potential shift towards financial standards that do not allow LIFO (a standard adopted and influenced by the International Financial Accounting Standards Board). Data will be collected from Compustat for publicly traded companies (with a LIFO Reserve) for the past 10 years. I will document which firms use LIFO, analyze trends relating to LIFO usage and LIFO reserves (the difference in the cost of inventory between using LIFO and FIFO), and evaluate the effect on earnings. The purpose of this research is to evaluate the accuracy of LIFO in portraying earnings and to see how much tax has gone uncollected over the years because of the use of LIFO. Moreover, I will provide an opinion as to whether U.S. GAAP should adopt a standard similar to IFRS and ban the LIFO method.