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on Risk Management |
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Issue of 2023‒05‒29
24 papers chosen by |
| By: | Joana Passinhas; Ana Pereira |
| Abstract: | Ensuring the resilience of the financial system implies managing a trade-off between expected bank profitability and tail risk in bank returns. To describe this trade-off, we estimate a dynamic quantile regression model using bank-level data for Portugal that links future bank profitability to the current cyclical systemic risk environment net of the prevailing level of capital-based resilience (residual cyclical systemic risk). We find that an increase in residual cyclical systemic risk negatively affects the conditional distribution of bank profitability at the medium-term projection horizons, confirming the findings in the literature. We propose a novel calibration rule for the countercyclical capital buffer (CCyB), which is flexible enough to accommodate different preferences of the policymaker and factors in the prevailing levels of cyclical systemic risk and capital-based resilience. We illustrate the operationalisation of this rule under different assumptions for the policymaker preferences and show how tightening capital requirements alters the risk-return relationship of future profitability in the banking sector. We find evidence that increasing the CCyB rate improves the outlook for medium-term downside risk in bank profitability and worsens the outlook for short-term expected profitability, stressing the tradeoff faced by the policymaker when deploying policy instruments and the misalignment in the horizons at which costs and benefits take place. |
| JEL: | C21 C54 G17 G21 G28 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ptu:wpaper:w202303&r=rmg |
| By: | Zlata Tabachov\'a; Christian Diem; Andr\'as Borsos; Csaba Burger; Stefan Thurner |
| Abstract: | Realistic credit risk assessment, the estimation of losses from counterparty's failure, is central for the financial stability. Credit risk models focus on the financial conditions of borrowers and only marginally consider other risks from the real economy, supply chains in particular. Recent pandemics, geopolitical instabilities, and natural disasters demonstrated that supply chain shocks do contribute to large financial losses. Based on a unique nation-wide micro-dataset, containing practically all supply chain relations of all Hungarian firms, together with their bank loans, we estimate how firm-failures affect the supply chain network, leading to potentially additional firm defaults and additional financial losses. Within a multi-layer network framework we define a financial systemic risk index (FSRI) for every firm, quantifying these expected financial losses caused by its own- and all the secondary defaulting loans caused by supply chain network (SCN) shock propagation. We find a small fraction of firms carrying substantial financial systemic risk, affecting up to 16% of the banking system's overall equity. These losses are predominantly caused by SCN contagion. For every bank we calculate the expected loss (EL), value at risk (VaR) and expected shortfall (ES), with and without accounting for SCN contagion. We find that SCN contagion amplifies the EL, VaR, and ES by a factor of 4.3, 4.5, and 3.2, respectively. These findings indicate that for a more complete picture of financial stability and realistic credit risk assessment, SCN contagion needs to be considered. This newly quantified contagion channel is of potential relevance for regulators' future systemic risk assessments. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.04865&r=rmg |
| By: | Yannick Hoga |
| Abstract: | Systemic risk measures have been shown to be predictive of financial crises and declines in real activity. Thus, forecasting them is of major importance in finance and economics. In this paper, we propose a new forecasting method for systemic risk as measured by the marginal expected shortfall (MES). It is based on first de-volatilizing the observations and, then, calculating systemic risk for the residuals using an estimator based on extreme value theory. We show the validity of the method by establishing the asymptotic normality of the MES forecasts. The good finite-sample coverage of the implied MES forecast intervals is confirmed in simulations. An empirical application to major US banks illustrates the significant time variation in the precision of MES forecasts, and explores the implications of this fact from a regulatory perspective. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.10349&r=rmg |
| By: | Paulo M.M. Rodrigues; João Nicolau; Marian Z. Stoykov |
| Abstract: | This paper provides novel theoretical results for the estimation of the conditional tail index of Pareto and Pareto-type distributions in a time series context. We show that both the estimators and relevant test statistics are normally distributed in the limit, when independent and identically distributed or dependent data are considered. Simulation results provide support for the theoretical findings and highlight the good finite sample properties of the approach in a time series context. The proposed methodology is then used to analyze stock returns’ tail risk dynamics. Two empirical applications are provided. The first consists in testing whether the time-varying tail exponents across firms follow Kelly and Jiang’s (2014) assumption of common firm level tail dynamics. The results obtained from our sample seem not to favour this hypothesis. The second application, consists of the evaluation of the impact of two market risk indicators, VIX and Expected Shortfall (ES) and two firm specific covariates, capitalization and market-to-book on stocks tail risk dynamics. Although all variables seem important drivers of firms’ tail risk dynamics, it is found that overall ES and firms’ capitalization seem to have overall wider impact. |
| JEL: | C22 C58 G12 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ptu:wpaper:w202306&r=rmg |
| By: | Ashish Dhiman |
| Abstract: | Machine Learning has invariantly found its way into various Credit Risk applications. Due to the intrinsic nature of Credit Risk, quantifying the uncertainty of the predicted risk metrics is essential, and applying uncertainty-aware deep learning models to credit risk settings can be very helpful. In this work, we have explored the application of a scalable UQ-aware deep learning technique, Deep Evidence Regression and applied it to predicting Loss Given Default. We contribute to the literature by extending the Deep Evidence Regression methodology to learning target variables generated by a Weibull process and provide the relevant learning framework. We demonstrate the application of our approach to both simulated and real-world data. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.04967&r=rmg |
| By: | Gergely Ganics (Banco de España); María Rodríguez-Moreno (Banco de España) |
| Abstract: | We present a house price-at-risk (HaR) model that fits the historical developments in the Spanish housing market. By means of quantile regressions we show that a model including quarterly real house price growth, a misalignment measure and a consumer confidence index is able to accurately forecast the developments in the Spanish housing market up to two years ahead. We also show how the HaR model can be used to monitor the downside risk. |
| Keywords: | house price-at-risk, house prices, quantile regressions |
| JEL: | C31 E37 G01 R31 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:2244&r=rmg |
| By: | Carmen Broto (Banco de España); Luis Fernández Lafuerza (Banco de España); Mariya Melnychuk (Banco de España) |
| Abstract: | Buffers for systemically important institutions (SIIs) were designed to mitigate the risks posed by these large and complex banks. With a panel data model for a sample of listed European banks, we demonstrate that capital requirements for SIIs effectively reduce the perceived systemic risk of these institutions, which we proxy with the SRISK indicator in Brownlees and Engle (2017). We also study the impact of the adjustment mechanisms that banks use to comply with SII buffer requirements and their contribution to systemic risk. The results show that banks mainly respond to higher SII buffers by increasing their equity, as intended by the regulators. Once we control for the options SIIs employ to fulfil these requirements and SII characteristics (e.g. total asset size), we find a residual effect of having SII status. This result suggests that being an SII provides a positive signal to markets by further decreasing its contribution to systemic risk. |
| Keywords: | capital requirements, systemically important institutions, systemic risk, SRISK, macroprudential policy |
| JEL: | C54 E58 G21 G32 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:2243&r=rmg |
| By: | Ali Shirazi; Fereshteh Sadeghi Naieni Fard |
| Abstract: | Finding the hedge ratios for a portfolio and risk compression is the same mathematical problem. Traditionally, regression is used for this purpose. However, regression has its own limitations. For example, in a regression model, we can't use highly correlated independent variables due to multicollinearity issue and instability in the results. A regression model cannot also consider the cost of hedging in the hedge ratios estimation. We have introduced several methods that address the linear regression limitation while achieving better performance. These models, in general, fall into two categories: Regularization Techniques and Common Factor Analyses. In regularization techniques, we minimize the variance of hedged portfolio profit and loss (PnL) and the hedge ratio sizes, which helps reduce the cost of hedging. The regularization techniques methods could also consider the cost of hedging as a function of the cost of funding, market condition, and liquidity. In common factor analyses, we first map variables into common factors and then find the hedge ratios so that the hedged portfolio doesn't have any exposure to the factors. We can use linear or nonlinear factors construction. We are introducing a modified beta variational autoencoder that constructs common factors nonlinearly to compute hedges. Finally, we introduce a comparison method and generate numerical results for an example. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.04801&r=rmg |
| By: | Jason Brown; Nida Çakır Melek; Johannes Matschke; Sai Sattiraju |
| Abstract: | This paper contributes to the literature on deviations from rational expectations in financial markets and to the literature on evaluating density forecasts. We first develop a novel statistic to evaluate the overall accuracy of distributional forecasts, and find two methods that yield accurate distributional forecasts. We then propose another statistic to examine the relative accuracy over the entire distribution range. Our results indicate more oil price realizations in the left tail than predicted. We argue that this finding points to a persistent behavioral forecasting bias and a departure from the rational expectations hypothesis. Investors hence underestimate left tail risk and under-insure against very low oil prices. |
| Keywords: | option pricing; density forecasts; tail risks |
| JEL: | C52 C58 G12 G17 G41 Q47 |
| Date: | 2023–03–31 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedkrw:96072&r=rmg |
| By: | Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Jacobus Nel (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Joshua Nielsen (Boulder Investment Technologies, LLC, 1942 Broadway Suite 314C, Boulder, CO, 80302, USA); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | We study whether booms and busts in the stock market of the United States (US) drives its volatility. Given this, first, we employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. We successfully detect major crashes and rallies during the weekly period from January 1973 to December 2020. Second, we utilize a nonparametric causality-in-quantiles approach to analyze the predictive impact of our bubble indicators on daily data-based weekly realized volatility (RV). This econometric framework allows us to circumvent potential misspecification due to nonlinearity and instability, rendering the results of weak causal influence derived from a linear framework invalid. The MS-LPPLS-CIs reveal strong evidence of predictability for RV over its entire conditional distribution. We observe relatively stronger impacts for the positive bubbles indicators, with our findings being robust to an alternative metric of volatility, namely squared returns, and weekly realized volatilities derived from 5 (RV5)- and 10 (RV10)-minutes interval intraday data. Furthermore, we detect evidence of predictability for RV5 and RV10 of nine other developed and emerging stock markets. Finally, we also find strong evidence of causal feedbacks from RV5 and RV10 on to the MS-LPPLS-CIs of the 10 countries considered. Our findings have significant implications for investors and policymakers. |
| Keywords: | Multi-Scale Positive and Negative Bubbles, Realized Volatility, Nonparametric Causality-in-Quantiles Test, International Stock Markets |
| JEL: | C22 G15 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202310&r=rmg |
| By: | Bampinas, Georgios; Panagiotidis, Theodore |
| Abstract: | This paper studies the cross-market linkages between six international stock markets and the two major cryptocurrency markets during the Covid-19 pandemic and the Russian invasion of Ukraine. By employing the local (partial) Gaussian correlation approach, we find that during the Covid-19 pandemic period both cryptocurrency markets possess limited diversification and safe haven properties, which further diminish during the war. Bootstrap tests for contagion suggest that during the Covid-19 pandemic the East Asian markets lead the transmission of contagion towards the two cryptocurrency markets. During the Russian invasion, the US stock market emerges as the principal transmitter of contagion. Uncovering the role of pandemic (Infectious Disease EMV Index) and geopolitical risk (GPR index) induced uncertainties, we find that under conditions of high uncertainty and financial distress the dependency between the US and UK stock markets with both cryptocurrency markets increases considerably. The latter is more profound during the Russian-Ukrainian conflict. |
| Keywords: | Bitcoin, Ethereum, cryptocurrency, stock market, tail dependence, local Gaussian partial correlation, pandemic uncertainty, geopolitical risk uncertainty |
| JEL: | C51 C58 G1 |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:117094&r=rmg |
| By: | José E. Gutiérrez (Banco de España); Luis Fernández Lafuerza (Banco de España) |
| Abstract: | As noted in recent literature, firms can run on credit lines due to fear of future credit restrictions. We exploit the 2011 stress test supervised by the European Banking Authority (EBA) and the Spanish Central Credit Register to explore: 1) the occurrence and magnitude of these runs after the release of negative stress test results; and 2) banks’ behaviour before and after the release of this information. We find that, following the release of the results, firms drew down approximately 10 pp more available funds from lines granted by banks that had a worse performance in the stress test. Moreover, before the release date, poorer performing banks were more likely to reduce the size of credit lines, while those with more significant balances of undrawn credit lines were more likely to cut term lending. |
| Keywords: | credit lines, bank runs, stress tests, bank risk management |
| JEL: | G01 G14 G21 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:2245&r=rmg |
| By: | Jie Dong |
| Abstract: | Digital transformation challenges financial management while reducing costs and increasing efficiency for enterprises in various countries. Identifying the transmission paths of enterprise financial risks in the context of digital transformation is an urgent problem to be solved. This paper constructs a system of influencing factors of corporate financial risks in the new era through literature research. It proposes a path identification method of financial risks in the context of the digital transformation of enterprises based on DEMATEL-ISM-MICMAC. This paper explores the intrinsic association among the influencing factors of corporate financial risks, identifies the key influencing factors, sorts out the hierarchical structure of the influencing factor system, and analyses the dependency and driving relationships among the factors in this system. The results show that: (1) The political and economic environment being not optimistic will limit the enterprise's operating ability, thus directly leading to the change of the enterprise's asset and liability structure and working capital stock. (2) The enterprise's unreasonable talent training and incentive mechanism will limit the enterprise's technological innovation ability and cause a shortage of digitally literate financial talents, which eventually leads to the vulnerability of the enterprise's financial management. This study provides a theoretical reference for enterprises to develop risk management strategies and ideas for future academic research in digital finance. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.04216&r=rmg |
| By: | Li Rong Wang; Hsuan Fu; Xiuyi Fan |
| Abstract: | We study the impacts of business cycles on machine learning (ML) predictions. Using the S&P 500 index, we find that ML models perform worse during most recessions, and the inclusion of recession history or the risk-free rate does not necessarily improve their performance. Investigating recessions where models perform well, we find that they exhibit lower market volatility than other recessions. This implies that the improved performance is not due to the merit of ML methods but rather factors such as effective monetary policies that stabilized the market. We recommend that ML practitioners evaluate their models during both recessions and expansions. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.09937&r=rmg |
| By: | Andrew Na; Meixin Zhang; Justin Wan |
| Abstract: | In this paper, we propose a generative adversarial network (GAN) approach for efficiently computing volatility surfaces. The idea is to make use of the special GAN neural architecture so that on one hand, we can learn volatility surfaces from training data and on the other hand, enforce no-arbitrage conditions. In particular, the generator network is assisted in training by a discriminator that evaluates whether the generated volatility matches the target distribution. Meanwhile, our framework trains the GAN network to satisfy the no-arbitrage constraints by introducing penalties as regularization terms. The proposed GAN model allows the use of shallow networks which results in much less computational costs. In our experiments, we demonstrate the performance of the proposed method by comparing with the state-of-the-art methods for computing implied and local volatility surfaces. We show that our GAN model can outperform artificial neural network (ANN) approaches in terms of accuracy and computational time. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.13128&r=rmg |
| By: | Sung Hoon Choi; Donggyu Kim |
| Abstract: | In this paper, we develop a novel large volatility matrix estimation procedure for analyzing global financial markets. Practitioners often use lower-frequency data, such as weekly or monthly returns, to address the issue of different trading hours in the international financial market. However, this approach can lead to inefficiency due to information loss. To mitigate this problem, our proposed method, called Structured Principal Orthogonal complEment Thresholding (Structured-POET), incorporates structural information for both global and national factor models. We establish the asymptotic properties of the Structured-POET estimator, and also demonstrate the drawbacks of conventional covariance matrix estimation procedures when using lower-frequency data. Finally, we apply the Structured-POET estimator to an out-of-sample portfolio allocation study using international stock market data. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.01464&r=rmg |
| By: | Luis Fernando Melo-Velandia; José Vicente Romero; Mahicol Stiben Ramírez-González |
| Abstract: | En este artículo, analizamos la estructura de dependencia en las colas de las distribuciones de los Credit Default Swaps (CDS) y el ciclo financiero global en un grupo de once mercados emergentes. Utilizando un modelo Copula-CoVaR, proporcionamos evidencia de la dependencia significativa en las colas de las distribuciones de variables relacionadas con el ciclo financiero global, como el VIX, y los CDS de mercados emergentes. Estos hallazgos son importantes en el contexto de mercados financieros globales estresados (cola derecha de las distribuciones del VIX), ya que ofrecen a los inversores internacionales información relevante sobre cómo rebalancear sus portafolios mediante una métrica más general que el CoVaR tradicional. Además, nuestros resultados respaldan la importancia del ciclo financiero global en la dinámica del riesgo soberano. **** RESUMEN: In this paper, we analyze the tail-dependence structure of credit default swaps (CDS) and the global financial cycle for a group of eleven emerging markets. Using a Copula-CoVaR model, we provide evidence that there is a significant taildependence between variables related with the global financial cycle, such as the VIX, and emerging market CDS. These results are particularly important in the context of distressed global financial markets (right tail of the distributions of the VIX) because they provide international investors with relevant information on how to rebalance their portfolios and a more suitable metric to analyze sovereign risk that goes beyond the traditional CoVaR. Additionally, we present further evidence supporting the importance of the global financial cycle in sovereign risk dynamics. |
| Keywords: | Global financial cycle, Country risk, CDS, Copula-CoVaR, Ciclo financiero global, Riesgo soberano |
| JEL: | G15 G17 C58 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:bdr:borrec:1231&r=rmg |
| By: | Carsten H. Chong; Viktor Todorov |
| Abstract: | We propose model-free (nonparametric) estimators of the volatility of volatility and leverage effect using high-frequency observations of short-dated options. At each point in time, we integrate available options into estimates of the conditional characteristic function of the price increment until the options' expiration and we use these estimates to recover spot volatility. Our volatility of volatility estimator is then formed from the sample variance and first-order autocovariance of the spot volatility increments, with the latter correcting for the bias in the former due to option observation errors. The leverage effect estimator is the sample covariance between price increments and the estimated volatility increments. The rate of convergence of the estimators depends on the diffusive innovations in the latent volatility process as well as on the observation error in the options with strikes in the vicinity of the current spot price. Feasible inference is developed in a way that does not require prior knowledge of the source of estimation error that is asymptotically dominating. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.04137&r=rmg |
| By: | Duncan Ermini Leaf |
| Abstract: | Statistical inferential results generally come with a measure of reliability for decision-making purposes. For a policy implementer, the value of implementing published policy research depends critically upon this reliability. For a policy researcher, the value of policy implementation may depend weakly or not at all upon the policy's outcome. Some researchers might find it advantageous to overstate the reliability of statistical results. Implementers may find it difficult or impossible to determine whether researchers are overstating reliability. This information asymmetry between researchers and implementers can lead to an adverse selection problem where, at best, the full benefits of a policy are not realized or, at worst, a policy is deemed too risky to implement at any scale. Researchers can remedy this by guaranteeing the policy outcome. Researchers can overcome their own risk aversion and wealth constraints by exchanging risks with other researchers or offering only partial insurance. The problem and remedy are illustrated using a confidence interval for the success probability of a binomial policy outcome. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.03205&r=rmg |
| By: | Christian Bongiorno; Marco Berritta |
| Abstract: | In optimal covariance cleaning theory, minimizing the Frobenius norm between the true population covariance matrix and a rotational invariant estimator is a key step. This estimator can be obtained asymptotically for large covariance matrices, without knowledge of the true covariance matrix. In this study, we demonstrate that this minimization problem is equivalent to minimizing the loss of information between the true population covariance and the rotational invariant estimator for normal multivariate variables. However, for Student's t distributions, the minimal Frobenius norm does not necessarily minimize the information loss in finite-sized matrices. Nevertheless, such deviations vanish in the asymptotic regime of large matrices, which might extend the applicability of random matrix theory results to Student's t distributions. These distributions are characterized by heavy tails and are frequently encountered in real-world applications such as finance, turbulence, or nuclear physics. Therefore, our work establishes a connection between statistical random matrix theory and estimation theory in physics, which is predominantly based on information theory. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.14098&r=rmg |
| By: | Ranoua Bouchouicha; Jilong Wu; Ferdinand M. Vieider (-) |
| Abstract: | The fourfold pattern of risk attitudes has been called ‘the most distinctive implication of prospect theory’. It constitutes the central mechanism by which prospect theory (PT) explains the coexistence of gambling and insurance. Here, we compare risk-taking patterns obtained from certainty equivalents (CEs) to risk-taking patterns observed when presenting all single choices contained in the CE lists one-by-one in a binary choice setup. Choices obtained from CEs indicate a clear fourfold pattern. Binary choices, on the other hand, indicate risk aversion for small probability gains, and risk seeking for small probabilities losses—the opposite of what is predicted by the fourfold pattern. The use of CEs to measure PT parameters is often justified based on the fact that they avoid endogenous reference points, which have been documented by comparing CEs to probability equivalents (PEs). We show that loss aversion in a PT model can actually not account for this discrepancy, since the gap between CEs and PEs requires different loss aversion coefficients for each PE task. Our results thus question the applicability of PT beyond the restrictive realm of CEs, which are arguably a poor proxy for most real-world decisions. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:rug:rugwps:23/1068&r=rmg |
| By: | Philippe Goulet Coulombe (University of Quebec in Montreal); Maximilian Gobel (Bocconi University) |
| Abstract: | When it comes to stock returns, any form of predictability can bolster risk-adjusted profitability. We develop a collaborative machine learning algorithm that optimizes portfolio weights so that the resulting synthetic security is maximally predictable. Precisely, we introduce MACE, a multivariate extension of Alternating Conditional Expectations that achieves the aforementioned goal by wielding a Random Forest on one side of the equation, and a constrained Ridge Regression on the other. There are two key improvements with respect to Lo and MacKinlay’s original maximally predictable portfolio approach. First, it accommodates for any (nonlinear) forecasting algorithm and predictor set. Second, it handles large portfolios. We conduct exercises at the daily and monthly frequency and report significant increases in predictability and profitability using very little conditioning information. Interestingly, predictability is found in bad as well as good times, and MACE successfully navigates the debacle of 2022. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:bbh:wpaper:23-01&r=rmg |
| By: | Lorenzo Bastianello; Alain Chateauneuf; Bernard Cornet |
| Abstract: | Two acts are comonotonic if they yield high payoffs in the same states of nature. The main purpose of this paper is to derive a new characterization of Cumulative Prospect Theory (CPT) through simple properties involving comonotonicity. The main novelty is a concept dubbed gain-loss hedging: mixing positive and negative acts creates hedging possibilities even when acts are comonotonic. This allows us to clarify in which sense CPT differs from Choquet expected utility. Our analysis is performed under the simpler case of (piece-wise) constant marginal utility which allows us to clearly separate the perception of uncertainty from the evaluation of outcomes. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.14843&r=rmg |
| By: | Andrea Gamba; Alessio Saretto |
| Abstract: | If firms can issue debt only at discrete dates, debt maturity is an effective device against the commitment problem on debt and investment policies. With shorter maturities, debt dynamics are less persistent and more valuable because upward leverage adjustments are faster and long-run leverage lower. Debt maturities that are relatively shorter than asset maturities increase marginal q, and reduce underinvestment. A decomposition of the credit spread consistent with equilibrium shows that the component due to the commitment problem on future debt issuances is sizeable when leverage and default risk are low, and is lower for shorter maturity. |
| Keywords: | credit risk; debt-equity agency conflicts; leverage ratchet effect; financial contracting; debt maturity |
| JEL: | G12 G31 G32 E22 |
| Date: | 2023–04–19 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddwp:96046&r=rmg |