A Markov Switching Re-evaluation of Event-Study Methodology

This paper reconsiders event-study methodology in light of evidences showing that Cumulative Abnormal Return (CAR) can result in misleading inferences about financial market efficiency and pre(post)-event behavior. In particular, CAR can be biased downward, due to the increased volatility on the event day and within the event window. We propose the use of Markov Switching Models to capture the effect of an event on security prices. We apply the proposed methodology to a set of 45 historical series on Credit Default Swap (CDS) quotes subject to multiple credit events, such as reviews for downgrading. Since CDSs provide insurance against the default of a particular company or sovereign entity, this study checks if market anticipates reviews for downgrading and evaluates the time period the announcements lag behind the market

Moody’s Ratings Statistical Forecasting for Industrial and Retail Firms

Long-term ratings of companies are obtained from public data plus some additional nondisclosed information. A model based on data from firms’ public accounts is proposed to directly obtain these ratings, showing fairly close similitude with published results from Credit Rating Agencies. The rating models used to assess the creditworthiness of a firm may involve some possible conflicts of interest, as companies pay for most of the rating process and are, thus, clients of the rating firms. Such loss of faith among investors and criticism toward the rating agencies were especially severe during the financial crisis in 2008. To overcome this issue, several alternatives are addressed; in particular, the focus is on elaborating a rating model for Moody’s long-term companies’ ratings for industrial and retailing firms that could be useful as an external check of published rates. Statistical and artificial intelligence methods are used to obtain direct prediction of awarded rates in these sectors, without aggregating adjacent classes, which is usual in previous literature. This approach achieves an easy-to-replicate methodology for real rating forecasts based only on public available data, without incurring the costs associated with the rating process, while achieving a higher accuracy. With additional sampling information, these models can be extended to other sectors

Regimes in CDS Spreads: A Markov Switching Model of iTraxx Europe Indices

This paper investigates the determinants of the iTraxx CDS Europe indices, finding strong evidence that they are regime dependent. During volatile periods credit spreads become highly sensitive to stock volatility and more sensitive to this than to stock returns. They are also almost immune to interest rates changes. During tranquil periods credit spreads are more sensitive to stock returns than to volatility and most indices are sensitive to interest rate moves. However for companies in the financial sector interest rates have no significant influence in either regime. We also found some evidence that raising interest rates can decrease the probability of credit spreads entering a volatile period. Our findings are useful for policy makers and, since equity hedge ratios based on single-state models cannot capture the regime dependent behaviour of credit spreads, our results may also help traders to improve the efficiency of hedging credit default swaps. Finally, the volatility clustering and autocorrelation that we have identified in the price dynamics of iTraxx indices should prove useful for pricing the iTraxx options that are now being actively traded over-the-counter.iTraxx, Credit Default Swap Index, Markov Switching, Credit Spreads

An extended generalized Markov model for the spread risk and its calibration by using filtering techniques in Solvency II framework

The Solvency II regulatory regime requires the calculation of a capital requirement, the Solvency Capital Requirement (SCR), for the insurance and reinsurance companies, that is based on a market-consistent evaluation of the Basic Own Funds probability distribution forecast over a one-year time horizon. This work proposes an extended generalized Markov model for rating-based pricing of risky securities for spread risk assessment and management within the Solvency II framework, under an internal model or partial internal model. This model is based on Jarrow, Lando and Turnbull (1997), Lando (1998) and Gambaro et al. (2018) and models the credit rating transitions and the default process using an extension of a time-homogeneous Markov chain and two subordinator processes. This approach allows simultaneous modeling of credit spreads for different rating classes and credit spreads to fluctuate randomly even when the rating does not change. The estimation methodologies used in this work are consistent with the scope of the work and the scope of the proposed model, i.e., pricing of defaultable bonds and calculation of SCR for the spread risk sub-module, and with the market-consistency principle required by Solvency II. For this purpose, estimation techniques on time series known as filtering techniques are used, which allow the model parameters to be jointly estimated under both the real-world probability measure (necessary for risk assessment) and the risk-neutral probability measure (necessary for pricing). Specifically, an appropriate set of time series of credit spread term structures, differentiated by economic sector and rating class, is used. The proposed model, in its final version, returns excellent results in terms of goodness of fit to historical data, and the projected data are consistent with historical data and the Solvency II framework. The filtering techniques, in the different configurations used in this work (particle filtering with Gauss-Legendre quadrature techniques, particle filtering with Sequential Importance Resampling algorithm, Kalman filter), were found to be an effective and flexible tool for estimating the models proposed, able to handle the high computational complexity of the problem addressed

Credit Scoring for M-Shwari using Hidden Markov Model

The introduction of mobile based Micro-credit facility, M-Shwari, has heightened the need to develop a proper decision support system to classify the customers based on their credit scores. This arises due to lack of proper information on the poor and unbanked as they are locked out of the formal banking sector. A classification technique, the hidden Markov model, is used. The poor customers’ scanty deposits and withdrawal dynamics in the M-Shwari account estimate the credit risk factors that are used in training and learning the hidden Markov model. The data is generated through simulation and customers categorized in terms of their credit scores and credit quality levels. The model classifies over 80 percent of the customers as having average and good credit quality level. This approach offers a simple and novice method to cater for the unbanked and poor with minimal or no financial history thus increasing financial inclusion in Kenya

CDS Volatility: the Key Signal of Credit Quality

This paper investigates the role of CDS volatility in providing information concerning the credit quality of a company. In Castellano and D'Ecclesia (2011) a first analysis of how CDS quotes respond to rating announcements is provided and it showed that market participants do not rely much on Rating Agencies, especially during periods characterized by very high volatility, i.e. during a financial crisis. Here, a more accurate analysis of the CDS's ability to provide timely information on the creditworthiness of reference entities is performed, estimating the volatility of CDS quotes by using Exponential GARCH(1,1) models. The event study methodology is applied to a sample of CDS quotes for US and European markets, over the period 2004-2009. Results provide an accurate understanding of market behavior in the presence of news released by Rating Agencies. Overall, market participants seem to provide timely reactions around the event date and we show that the key element of signaling is represented by the changing volatility in CDS quotes, before and after the rating event

Numerical estimates of risk factors contingent on credit ratings

AbstractAssuming a favorable or an adverse outcome for every combination of a credit class and an industry sector, a binary string, termed as a macroeconomic scenario, is considered. Given historical transition counts and a model for dependence among credit-rating migrations, a probability is assigned to each of the scenarios by maximizing a likelihood function. Applications of this distribution in financial risk analysis are suggested. Two classifications are considered: 7 non-default credit classes with 6 industry sectors and 2 non-default credit classes with 12 industry sectors. We propose a heuristic algorithm for solving the corresponding maximization problems of combinatorial complexity. Probabilities and correlations characterizing riskiness of random events involving several industry sectors and credit classes are reported

The Brazilian Currency Turmoil of 2002: A Nonlinear Analysis

This paper investigates the main sources of instability in Brazil during the currency and financial distress episode of 2002. We test for financial contagion from the Argentine crisis and the impact of factors including IMF intervention and political uncertainty in raising the probability of crisis. The empirical investigation employs a Markov switching model with endogenous transition probabilities.Brazil; contagion; financial crises; IMF intervention; Markov switching model; political uncertainty.