Rosella Castellano

Mean–Variance portfolio selection in presence of infrequently traded stocks

This paper deals with a mean–variance optimal portfolio selection problem in presence of risky assets characterized by low-frequency 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. 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 financial perspective.

Regularities and discrepancies of credit default swaps: a data science approach through Benford's law

In this paper, we search whether the Benfords law is applicable to monitor daily changes in sovereign Credit Default Swaps (CDS) quotes, which are acknowledged to be complex systems of economic content. This test is of paramount importance since the CDS of a country proxy its health and probability to default, being associated to an insurance against the event of its default. We fit the Benfords law to the daily changes in sovereign CDS spreads for 13 European countries, - both inside and outside the European Union and European Monetary Union. Two different tenors for the sovereign CDS contracts are considered: 5 yrs and 10 yrs, - the former being the reference and most liquid one. The time period under investigation is 2008-2015 which includes the period of distress caused by the European sovereign debt crisis. Moreover, (i) an analysis over relevant sub-periods is carried out, (ii) several insights are provided also by implementing the tracking of the Benfords law over moving windows. The main test for checking the conformance to Benfords law is - as usual - the

Can CDS indexes signal future turmoils in the stock market? A Markov switching perspective

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CDS volatility: the key signal of credit quality

test, whose values are presented and discussed for all cases. The analysis is further completed by elaborations based on Chebyshevs distance and Kullback and Leiblers divergence. The results highlight differences by countries and tenors. In particular, these results suggest that liquidity seems to be associated to higher levels of distortion. Greece - representing a peculiar case - shows a very different path with respect to the other European countries.

Credit default swaps: implied ratings versus official ones

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.

Optimal consumption/investment problem with light stocks: A mixed continuous-discrete time approach

This paper investigates the role of CDS volatility in providing information concerning the credit quality of a company.In Castellano and D’Ecclesia (J. Financ. Decis. Mak. 2:27, 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.

A Markov Switching Re-evaluation of Event-Study Methodology

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.

Performance of a Hedged Stochastic Portfolio Model in the Presence of Extreme Events

This paper addresses the optimal consumption/investment problem in a mixed discrete/continuous time model in presence of rarely traded stocks. Stochastic control theory with state variable driven by a jump-diffusion, via dynamic programming, is used. The theoretical study is validated through numerical experiments, and the proposed model is compared with the classical Merton’s portfolio. Some financial insights are provided.

Sustainable management of fossil fuels: A dynamic stochastic optimization approach with jump-diffusion

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 event windows. We propose the use of Markov Switching Models to capture the effect of an event on security prices. The proposed methodology is applied 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.

Bayesian Hidden Markov Models for Financial Data

Classical methods for computing the value-at-risk(VaR) do not account for the large price variationsobserved in financial markets. The historical methodis subject to event risk and may miss some fundamentalmarket evolution relevant to VaR; thevariance/covariance method tends to underestimate thedistribution tails and Monte Carlo simulation issubject to model risk. These methods are therebyusually completed with analyses derived fromcatastrophe scenarios.We propose a special case of the extreme-valueapproach for computing the value-at-risk of a stochasticmulticurrency portfolio when alternative hedgingstrategies are considered. This approach is able tocover market conditions ranging from the usual VaRenvironment to financial crises.We implement a multistage portfolio model with anexchange rate dynamic with stochastic volatility. Theparameters are estimated by GARCH-t models. Thesimulations are used to select multicurrencyportfolios whose exchange rate risk is hedged andrebalanced each ten days, accounting for VaR. Wecompare the performances of the two most classicalinstitutional options strategies – protective puts andcovered calls – to that of holding an unhedgedportfolio in presence of extreme events.