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on Risk Management |
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Issue of 2019‒03‒18
eighteen papers chosen by |
| By: | Grundke, Peter; Pliszka, Kamil; Tuchscherer, Michael |
| Abstract: | This paper deals with stress tests for credit risk and shows how exploiting the discretion when setting up and implementing a model can drive the results of a quantitative stress test for default probabilities. For this purpose, we employ several variations of a CreditPortfolioView-style model using US data ranging from 2004 to 2016. We show that seemingly only slightly differing specifications can lead to entirely different stress test results - in relative and absolute terms. That said, our findings reveal that the conversion of a shock (i.e., stress event) increases the (non-stress) default probability by 20% to 80% - depending on the stress test model selected. Interestingly, forecasts for non-stress default probabilities are less exposed to model and estimation risk. In addition, the risk horizon over which the stress default probabilities are forecasted and whether we consider mean stress default probabilities or quantiles seem to play only a minor role for the dispersion between the results of the different model specifications. Our findings emphasize the importance of extensive robustness checks for model-based credit risk stress tests. |
| Keywords: | credit risk,default probability,estimation risk,model risk,stress tests |
| JEL: | G21 G28 G32 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:092019&r=all |
| By: | Emna Omri (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis - UCA - Université Côte d'Azur - CNRS - Centre National de la Recherche Scientifique); Nouri Chtourou (RUDE - Research Unit in Development Economics - Université de Sfax - University of Sfax); Damien Bazin (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis - UCA - Université Côte d'Azur - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Concentrating solar power (CSP) is a promising technology in Tunisia. However, its diffusion is facing many barriers which deter investments. Through the analysis of a CSP plant in Southern Tunisia by using the Global Risk Analysis (GRA) method, we try to analyze the main risks faced by investors. The main objective of this research is to identify and analyze the risks faced by CSP investors in Tunisia and develop strategies that should be adopted to accelerate the process of diffusion of this technology. This analysis allows us to conclude that the CSP project is very exposed to political, financial, physical-chemical, legal, and strategic hazards. Moreover, we show that among the four phases of the project, the preparation phase is the most vulnerable to hazards. In fact, the GRA method makes it possible to determine the list of the major risks, such as the risk of not obtaining permission to build a CSP plant, the risk of non compliance with the deadline, the risk of failure to achieve the expected performance, the risk of insufficient access to capital, and the risk of conflicts with local residents. In order to de-risk CSP technology in Tunisia, we propose some strategies, such as strengthening the public-private partnerships, using participatory approaches, creating local employment, etc. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02061788&r=all |
| By: | Karpouzis, Efstathios; Bouras, Chris; Kanas, Angelos |
| Abstract: | We construct a novel hand-collected large data set of 205 U.S. hedge funds and 1031 activist events over the period 2005-2013, which records both the Schedule 13D filing date and the voicing date, and explore the role of voicing in value creation. We employ alternative inferential statistical approaches, including parametric, non-parametric, and heteroscedasticity-robust tests along with bootstrapping. We reveal that the voice date is important in creating short-term firm value, and provide strong evidence that voicing is associated with positive abnormal returns. These abnormal returns are approximately 1.11%, and are higher than the abnormal returns around the Schedule13D date by approximately 64%. There is also evidence of positive voice abnormal returns for voicing events which lead Schedule 13D events. The results are robust to models of abnormal returns allowing for leverage effects, and to alternative inferential statistical procedures. These findings suggest that voicing leads to information revelation, with implications for U.S. stock market arbitrage and the regulation for hedge fund activism information disclosure. |
| Keywords: | We construct a novel hand-collected large data set of 205 U.S. hedge funds and 1031 activist events over the period 2005-2013, which records both the Schedule 13D filing date and the voicing date, and explore the role of voicing in value creation. We employ alternative inferential statistical approaches, including parametric, non-parametric, and heteroscedasticity-robust tests along with bootstrapping. We reveal that the voice date is important in creating short-term firm value, and provide strong evidence that voicing is associated with positive abnormal returns. These abnormal returns are approximately 1.11%, and are higher than the abnormal returns around the Schedule13D date by approximately 64%. There is also evidence of positive voice abnormal returns for voicing events which lead Schedule 13D events. The results are robust to models of abnormal returns allowing for leverage effects, and to alternative inferential statistical procedures. These findings suggest that voicing leads to information revelation, with implications for U.S. stock market arbitrage and the regulation for hedge fund activism information disclosure. |
| JEL: | G14 G23 G3 |
| Date: | 2019–03–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:92576&r=all |
| By: | David Landriault; Bin Li; Mohamed Amine Lkabous |
| Abstract: | In this paper, we complement the existing literature on the occupation time in the red (below level $0$) of a spectrally negative L\'evy process, and later extend the analysis to the refracted spectrally negative L\'evy process. For both classes of processes, we derive an explicit expression for the distribution of such occupation time up to an independent exponential time. As an application, we consider the \emph{inverse occupation time} (also known as the time of cumulative Parisian ruin in \cite{guerinrenaud2015}), where ruin is deemed to occur at the earliest time the risk process cumulatively stays below a critical level over a pre-determined time-threshold. Some particular examples of spectrally negative L\'evy processes are also examined in more detail. |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1903.03721&r=all |
| By: | Cheikh Mbaye; Fr\'ed\'eric Vrins |
| Abstract: | We address the so-called calibration problem which consists of fitting in a tractable way a given model to a specified term structure like, e.g., yield or default probability curves. Time-homogeneous jump-diffusions like Vasicek or Cox-Ingersoll-Ross (possibly coupled with compounded Poisson jumps, JCIR), are tractable processes but have limited flexibility; they fail to replicate actual market curves. The deterministic shift extension of the latter (Hull-White or JCIR++) is a simple but yet efficient solution that is widely used by both academics and practitioners. However, the shift approach is often not appropriate when positivity is required, which is a common constraint when dealing with credit spreads or default intensities. In this paper, we tackle this problem by adopting a time change approach. On the top of providing an elegant solution to the calibration problem under positivity constraint, our model features additional interesting properties in terms of implied volatilities. It is compared to the shift extension on various credit risk applications such as credit default swap, credit default swaption and credit valuation adjustment under wrong-way risk. The time change approach is able to generate much larger volatility and covariance effects under the positivity constraint. Our model offers an appealing alternative to the shift in such cases. |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1903.04211&r=all |
| By: | O'Brien, Eoin (Central Bank of Ireland); O'Brien, Martin (Central Bank of Ireland); Velasco, Sofia (Central Bank of Ireland) |
| Abstract: | Following a number of years where the activation of the countercyclical capital buffer was limited, it is now becoming an increasingly relevant and actively used macroprudential policy tool across Europe. Against this background, this Note describes the high-level approach taken by the Central Bank of Ireland in setting the countercyclical capital buffer rate applicable to Irish exposures. In addition, the Note discusses issues around the identification of cyclical systemic risk in Ireland, and in particular the role of the credit-to-GDP gap as an appropriate reference indicator for countercyclical capital buffer rate decisions. The Note introduces work within the Central Bank of Ireland to develop a potential alternative reference indicator for informing countercyclical capital buffer decisions. In particular, an alternative measure of the national credit gap which looks to account for structural shifts in the economy and informs the estimation of the cycle through additional variables. This semi-structural measure of cyclical systemic risk addresses some of the main drawbacks of purely statistical methods such as excessively persistent trends, a feature that is particularly desirable in post-crisis circumstances. |
| Date: | 2018–07 |
| URL: | http://d.repec.org/n?u=RePEc:cbi:fsnote:4/fs/18&r=all |
| By: | Marcel Br\"autigam; Michel Dacorogna; Marie Kratz |
| Abstract: | Since the introduction of risk-based solvency regulation, pro-cyclicality has been a subject of concerns from all market participants. Here, we lay down a methodology to evaluate the amount of pro-cyclicality in the way financial institutions measure risk, and identify factors explaining this pro-cyclical behavior. We introduce a new indicator based on the Sample Quantile Process (SQP, a dynamic generalization of Value-at-Risk), conditioned on realized volatility to quantify the pro-cyclicality, and evaluate its amount in the markets, considering 11 stock indices as realizations of the SQP. Then we determine two main factors explaining the pro-cyclicality: the clustering and return-to-the-mean of volatility, as it could have been anticipated but not quantified before, and, more surprisingly, the very way risk is measured, independently of this return-to-the-mean effect. |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1903.03969&r=all |
| By: | Dumitru, Ana-Maria; Holden, Thomas |
| Abstract: | We build a non-stationary Hawkes model of sovereign credit risk for seven European countries, and estimate it on CDS data from the run-up to the Greek default. We model a country's credit risk as partly driven by a weighted combination of risks across countries. We find Spain and Portugal are the chief drivers of this component, with Greece's contribution also significant. Greece and Portugal are found to be particularly sensitive to external risk, with a Greek default 35% less likely in our period without shocks elsewhere. Our novel maximum-likelihood procedure permits tractable estimation of high-dimensional Hawkes models with unobserved events. |
| Keywords: | sovereign CDS spreads,credit risk,multivariate self-exciting point process,systemic risk |
| JEL: | G01 G12 G15 C32 C58 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:193632&r=all |
| By: | Suparna Biswas; Rituparna Sen |
| Abstract: | Spectral risk measures (SRMs) belongs to the family of coherent risk measures. A natural estimator for the class of spectral risk measures (SRMs) has the form of $L$-statistics. In the literature, various authors have studied and derived the asymptotic properties of the estimator of SRM using the empirical distribution function. But no such estimator of SRM is studied considering distribution function estimator other than empirical cdf. We propose a kernel based estimator of SRM. We try to investigate the large sample properties of general $L$-statistics based on i.i.d cases and apply them to our kernel based estimator of SRM. We prove that the estimator is strongly consistent and the estimator is asymptotically normal. We compare the finite sample performance of the kernel based estimator with that of empirical estimator of SRM using Monte Carlo simulation, where appropriate choice of smoothing parameter and the user's coefficient of risk aversion plays an important role. Based on our simulation study we have estimated the exponential SRM of four future index-that is Nikkei 225, Dax, FTSE 100 and Hang Seng using our proposed kernel based estimator. |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1903.03304&r=all |
| By: | Meital Graham-Rozen (Bank of Israel); Noam Michelson (Bank of Israel) |
| Abstract: | In this paper, we study credit insurance in Israel between 2010 and 2017, using a unique database provided for our use by ICIC—the Israeli Credit Insurance Company, the leading credit insurer in Israel. The research aims to determine what factors impact on the acceptance rate (the amount of insurance provided relative to the amount of insurance requested). This is the main parameter set by ICIC, and it reflects the pricing of the risks in each transaction. We find that the acceptance rate is impacted on primarily by the extent of the insurance company's exposure to the buyer's country, but also by the size of the insured company, the risk of the buyer with whom the company is transacting, and by the global real economic situation. These factors impact differently on domestic buyers and on buyers abroad, apparently due to differences in information on the two types of buyers. In addition, we examine the scope of exports insured by credit insurance and characterize them by geographical distribution and by risk levels. Finally, we examine—for the first time in Israel—suppliers' credit and find a strong link between suppliers' credit risk and real activity. |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:boi:wpaper:2018.03&r=all |
| By: | Emenike, Kalu O. |
| Abstract: | This paper evaluates the nature of stock market volatility in Africa after the global financial crisis. Specifically, the paper examines volatility clustering and volatility asymmetry in aftermath of the global financial crisis for Botswana, Régionale des Valeurs Mobilières (BRVM), Egypt, Ghana, Kenya, Malawi, Mauritius, Morocco, Namibia, Nigeria, Rwanda, South Africa, Tunisia, Uganda, and Zambia. The paper employs autoregressive asymmetric generalized autoregressive conditional heteroscedasticity (AR(i)-GJR-GARCH(1,1)) model. The major findings are as follows: (i) there is evidence of volatility clustering in Africa stock markets returns after the global financial crisis, although with varying degrees; (ii) there is existence of volatility persistence in the African stock market returns after the global financial crisis except for few countries, which are not very persistent; (iii) after the global financial crisis, Africa stock markets returns are asymmetric, with negative shocks producing higher volatility in the immediate future than positive shocks of the same magnitude in some countries, and positive shocks producing higher volatility in other countries. The findings provide comparative basis for assessing market patterns, predicting market risk, and gauging market sentiment in Africa stock markets, as well as provide foreign portfolio managers required evidence for harvesting volatility through portfolio rebalancing for optimal performance. |
| Keywords: | stock market returns, volatility clustering, asymmetry, GARCH models, Africa |
| JEL: | C22 G0 N27 |
| Date: | 2018–10–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:91653&r=all |
| By: | Pablo D'Erasmo; Igor Livshits; Koen Schoors (-) |
| Abstract: | Banking regulation routinely designates some assets as safe and thus does not require banks to hold any additional capital to protect against losses from these assets. A typical such safe asset is domestic government debt. There are numerous examples of banking regulation treating domestic government bonds as “safe,” even when there is clear risk of default on these bonds. We show, in a parsimonious model, that this failure to recognize the riskiness of government debt allows (and induces) domestic banks to “gamble” with depositors’ funds by purchasing risky government bonds (and assets closely correlated with them). A sovereign default in this environment then results in a banking crisis. Critically, we show that permitting banks to gamble this way lowers the cost of borrowing for the government. Thus, if the borrower and the regulator are the same entity (the government), that entity has an incentive to ignore the riskiness of the sovereign bonds. We present empirical evidence in support of the key mechanism we are highlighting, drawing on the experience of Russia in the run-up to its 1998 default and on the recent Eurozone debt crisis. |
| Keywords: | Banking; Sovereign default; Prudential regulation; Financial crisis |
| JEL: | G01 G28 F34 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:rug:rugwps:19/964&r=all |
| By: | Radanliev, Petar; De Roure, Dave; Cannady, Stacy; Mantilla Montalvo, Rafael; Nicolescu, Razvan; Huth, Michael |
| Abstract: | This paper is focused on mapping the current evolution of Internet of Things (IoT) and its associated cyber risks for the Industry 4.0 (I4.0) sector. We report the results of a qualitative empirical study that correlates academic literature with 14 - I4.0 frameworks and initiatives. We apply the grounded theory approach to synthesise the findings from our literature review, to compare the cyber security frameworks and cyber security quantitative impact assessment models, with the world leading I4.0 technological trends. From the findings, we build a new impact assessment model of IoT cyber risk in Industry 4.0. We therefore advance the efforts of integrating standards and governance into Industry 4.0 and offer a better understanding of economics impact assessment models for I4.0. |
| Keywords: | IoT Cyber Risk, IoT risk analysis, IoT cyber insurance, IoT MicroMort, Cyber Value-at-Risk |
| JEL: | L0 L5 L50 L52 L53 O2 O20 O3 O30 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:92566&r=all |
| By: | Kevin Denny |
| Abstract: | Based on a simple prior, this note derives upper bounds for the coefficient of absolute & relative risk aversion if utility can be written as depending linearly on the mean and variance of income. |
| Keywords: | Risk aversion; Mean-variance utility; Risk tolerance |
| JEL: | D80 |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:ucn:wpaper:201902&r=all |
| By: | José Renato Haas Ornelas; Roberto Baltieri Mauad |
| Abstract: | This paper provides empirical evidence of the predictive power of the currency implied volatility term structure (IVTS) on exchange rate behavior from both cross-section and time-series perspectives. Intriguingly, the direction of the prediction is not the same for developed and emerging markets. For developed markets, a high slope means low future returns, while for emerging markets this means high future returns. In order to analyze predictability from a cross-section perspective, we build portfolios based on the slope of the term structure, and thus present a new currency trading strategy. For developed (emerging) currencies, we buy (sell) the two currencies with the lowest slopes and sell (buy) those two with the highest slopes. The proposed strategy performs better than common currency strategies - carry trade, risk reversal and volatility risk premium - based on the Sharpe ratio, considering only currency returns, which supports the exchange rate predictability of the IVTS from a cross-section perspective |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:492&r=all |
| By: | Mawuli Segnon; Stelios Bekiros |
| Abstract: | In this paper, we revisit the stylized facts of cryptocurrency markets and propose various approaches for modeling the dynamics governing the mean and variance processes. We first provide the statistical properties of our proposed models and study in detail their forecasting performance and adequacy by means of point and density forecasts. We adopt two loss functions and the model confidence set (MSC) test to evaluate the predictive ability of the models and the likelihood ratio test to assess their adequacy. Our results confirm that cryptocurrency markets are characterized by regime shifting, long memory and multifractality. We find that the Markov switching multifractal (MSM) and FIGARCH models outperform other GARCH-type models in forecasting bitcoin returns volatility. Furthermore, combined forecasts improve upon forecasts from individual models. |
| Keywords: | Bitcoin, Multifractal processes, GARCH processes, Model confidence set, Likelihood ratio test |
| JEL: | C22 C53 C58 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:cqe:wpaper:7919&r=all |
| By: | Mikhail Drugov (New Economic School and CEPR); Dmitry Ryvkin (Department of Economics, Florida State University) |
| Abstract: | Heavy-tailed fluctuations are common in many environments, such as sales of creative and innovative products or the financial sector. We study how the presence of heavy tails in the distribution of shocks affects the optimal allocation of prizes in rank-order tournaments. While a winner-take-all prize schedule maximizes aggregate effort for light-tailed shocks, prize sharing becomes optimal when shocks acquire heavy tails, increasingly so following a skewness order. Extreme prize sharing { rewarding all ranks but the very last { is optimal when shocks have a decreasing failure rate, such as power laws. Hence, under heavy-tailed uncertainty, typically associated with strong inequality in the distribution of gains, providing incentives and reducing inequality go hand in hand. |
| Keywords: | heavy tails, power law, tournament, optimal allocation of prizes, failure rate |
| JEL: | C72 D86 M52 |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:cfr:cefirw:w0250&r=all |
| By: | Mikhail Drugov (New Economic School and CEPR); Dmitry Ryvkin (Department of Economics, Florida State University) |
| Abstract: | Tournaments are settings where agents' performance is determined jointly by effort and luck, and top performers are rewarded. We study the impact of the \shape of luck" { the details of the distribution of performance shocks { on incentives in tournaments. The focus is on the effect of competition, defined as the number of rivals an agent faces, which can be deterministic or stochastic. We show that individual and aggregate effort in tournaments are affected by an increase in competition in ways that depend critically on the shape of the density and failure (hazard) rate of shocks. When shocks have heavy tails, aggregate effort can decrease with stronger competition. |
| Keywords: | tournament, competition, heavy tails, stochastic number of players, unimodality, log-supermodularity, failure rate |
| JEL: | C72 D72 D82 |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:cfr:cefirw:w0251&r=all |