Recently, in line with the progressive development of the credit derivatives market, the academic research has begun to explore the relationship between Credit Default Swap market and rating events. In this paper, following a market based approach, we calibrate an Implied Rating model on Credit Default Swap market spreads. The non parametric mapping of Implied Ratings is calibrated on a large data set of Credit Default Swap quotes that includes the years of financial turmoils. This allows also to investigate the existence of possible differences between normal and abnormal market conditions. Unlike other models, the one proposed considers a linear penalty function which allows to evaluate market quotes in a neutral way and to formalize a more computationally efficient programming model. We compare the behaviors of credit rating agencies in different markets (EU and USA) and in different sub-periods, in order to analyze whether Implied Rating changes anticipate or follow the effective rating changes supplied by Fitch Ratings, Moody's and Standard and Poor's.
This paper introduces a new temperature index, which can suitably represent the underlying of a weather derivative. Such an index is defined as the weighted mean of daily average temperatures measured in different locations. It may be used to hedge volumetric risk, that is the effect of unexpected fluctuations in the demand/supply for some specific commodities-of agricultural or energy type, for exampledue to unfavorable temperature conditions. We aim at exploring the long term memory property of the volatility of such an index, in order to assess whether there exist some long-run paths and regularities in its riskiness. The theoretical part of the paper proceeds in a stepwise form: first, the daily average temperatures are modeled through autoregressive dynamics with seasonality in mean and volatility; second, the assessment of the distributional hypotheses on the parameters of the model is carried out for analyzing the long term memory property of the volatility of the index. The theoretical results suggest that the single terms of the index drive the long memory of the overall aggregation; moreover, interestingly, the proper selection of the parameters of the model might lead both to cases of persistence and antipersistence. The applied part of the paper provides some insights on the behaviour of the volatility of the proposed index, which is built starting from single daily average temperature time series.
This paper deals with a mean-variance optimal portfolio selection problem in presence of risky assets characterized by low frequency of trading and, therefore, low liquidity. To model the dynamics of illiquid assets, we introduce pure-jump processes. This leads to the development of a portfolio selection model in a mixed discrete/continuous time setting. In this paper, we pursue the twofold scope of analyzing and comparing either long-term investment strategies as well as short-term trading rules. The theoretical model is analyzed by applying extensive Monte Carlo experiments, in order to provide useful insights from a …nancial perspective.
Single-name Credit Default Swaps (CDS) are considered the main providers of direct information related with a reference entity's creditworthiness and, for this reason, they have often been the core of news on the current financial crisis. The academic research has focused mainly on the capacity of CDS in anticipating agencies' official rating changes and-in this respect-on their superior signalling power, compared to bond and stock markets. The aim of this work is, instead, to investigate the ability of fluctuations in CDS indexes in anticipating the occurrence of stock market crises. Our goal is to show that CDS indexes may provide investors and institutions with early warning signals of financial distresses in the stock market. We make use of a Markov switching model with states characterized by increasing levels of volatility and compare the times in which the first switch in a high volatility state occurs, respectively, in CDS and stock market index quotes. The data set consists of daily closing quotes for 5 years CDS and stock market index prices, covering the time period from 2004 to 2010. In order to capture possible geographic differences in CDS index capacity of foreseeing stock market distresses, data referring to two different regions, Europe and United States, are analyzed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.