nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒06‒28
27 papers chosen by

  1. Diversified reward-risk parity in portfolio construction By Jaehyung Choi; Hyangju Kim; Young Shin Kim
  2. Forecasting VaR and ES using a joint quantile regression and implications in portfolio allocation By Luca Merlo; Lea Petrella; Valentina Raponi
  3. How to Reconcile Pandemic Business Interruption Risk With Insurance Coverage. By Sandrine Spaeter
  4. Sample Recycling Method -- A New Approach to Efficient Nested Monte Carlo Simulations By Runhuan Feng; Peng Li
  5. Efficient Black-Box Importance Sampling for VaR and CVaR Estimation By Anand Deo; Karthyek Murthy
  6. 3D Tensor-based Deep Learning Models for Predicting Option Price By Muyang Ge; Shen Zhou; Shijun Luo; Boping Tian
  7. Modeling premiums of non-life insurance companies in India By Kartik Sethi; Siddhartha P. Chakrabarty
  8. Multivariate Pair Trading by Volatility & Model Adaption Trade-off By Chenyanzi Yu; Tianyang Xie
  9. Central Bank Policy and the Concentration of Risk: Empirical Estimates By Nuno Coimbra; Daisoon Kim; Hélène Rey
  10. Optimal asset allocation subject to withdrawal risk and solvency constraints By Areski Cousin; Ying Jiao; Christian Robert; Olivier David Zerbib
  11. A new measure to study erratic financial behaviors and time-varying dynamics of equity markets By Nick James; Max Menzies
  12. Universal Risk Budgeting By Alex Garivaltis
  13. Quantum Portfolio Optimization with Investment Bands and Target Volatility By Samuel Palmer; Serkan Sahin; Rodrigo Hernandez; Samuel Mugel; Roman Orus
  14. Liquidity Stress Testing in Asset Management - Part 2. Modeling the Asset Liquidity Risk By Roncalli, Thierry; Cherief, Amina; Karray-Meziou, Fatma; Regnault, Margaux
  15. Market Complete Option Valuation using a Jarrow-Rudd Pricing Tree with Skewness and Kurtosis By Yuan Hu; Abootaleb Shirvani; W. Brent Lindquist; Frank J. Fabozzi; Svetlozar T. Rachev
  16. Dynamic Bivariate Mortality Modelling By Ying Jiao; Yahia Salhi; Shihua Wang
  17. Switching From Incurred to Expected Loan Loss Provisioning: Early Evidence By López-Espinosa, Germán; Ormazabal, Gaizka; Sakasai, Yuki
  18. Uncertainty Aversion and Convexity in Portfolio Choice By Dong, Xueqi
  19. Robust deep hedging By Eva L\"utkebohmert; Thorsten Schmidt; Julian Sester
  20. A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface By Wenyong Zhang; Lingfei Li; Gongqiu Zhang
  21. Comparing risk elicitation in lotteries with visual or contextual framing aids By Estepa-Mohedano, Lorenzo; Espinosa, Maria Paz
  22. Interest Rate Risk of Savings Accounts By Jiri Witzany; Martin Divis
  23. From Decision in Risk to Decision in Time - and Return: A Restatement of Probability Discounting By Marc-Arthur Diaye; André Lapidus; Christian Schmidt
  24. Dispersion indexes based on bivariate measures of uncertainty By Francesco Buono; Camilla Cal\`i; Maria Longobardi
  25. Currency Hedging: Managing Cash Flow Exposure By Laura Alfaro; Mauricio Calani; Liliana Varela
  26. The COVID-19 Shock and Equity Shortfall: Firm-level Evidence from Italy By Carletti, Elena; Oliviero, Tommaso; Pagano, Marco; Pelizzon, Loriana; Subrahmanyam, Marti G.
  27. Exchange rates and the global transmission of equity market shocks By Ojea-Ferreiro, Javier; Reboredo, Juan C.

  1. By: Jaehyung Choi; Hyangju Kim; Young Shin Kim
    Abstract: We introduce diversified risk parity embedded with various reward-risk measures and more general allocation rules for portfolio construction. We empirically test advanced reward-risk parity strategies and compare their performance with an equally-weighted risk portfolio in various asset universes. All reward-risk parity strategies we tested exhibit consistent outperformance evidenced by higher average returns, Sharpe ratios, and Calmar ratios. The alternative allocations also reflect less downside risks in Value-at-Risk, conditional Value-at-Risk, and maximum drawdown. In addition to the enhanced performance and reward-risk profile, transaction costs can be reduced by lowering turnover rates. The Carhart four-factor analysis also indicates that the diversified reward-risk parity allocations gain superior performance.
    Date: 2021–06
  2. By: Luca Merlo; Lea Petrella; Valentina Raponi
    Abstract: In this paper we propose a multivariate quantile regression framework to forecast Value at Risk (VaR) and Expected Shortfall (ES) of multiple financial assets simultaneously, extending Taylor (2019). We generalize the Multivariate Asymmetric Laplace (MAL) joint quantile regression of Petrella and Raponi (2019) to a time-varying setting, which allows us to specify a dynamic process for the evolution of both VaR and ES of each asset. The proposed methodology accounts for the dependence structure among asset returns. By exploiting the properties of the MAL distribution, we then propose a new portfolio optimization method that minimizes the portfolio risk and controls for well-known characteristics of financial data. We evaluate the advantages of the proposed approach on both simulated and real data, using weekly returns on three major stock market indices. We show that our method outperforms other existing models and provides more accurate risk measure forecasts compared to univariate ones.
    Date: 2021–06
  3. By: Sandrine Spaeter
    Abstract: In the face of major risks, the financial capacities of private insurers are rapidly reached. Reinsurance is used to ensure an acceptable (and also imposed by regulation) solvency ratio. Yet standard reinsurance can also be unable to provide an adequate level of compensation. For major risks such as natural catastrophes, a risk transfer can be operated to the financial markets through securitization. The today well-known cat bonds, cat options, or swaps permit the issuer (a state prone to earthquakes, an insurer exposed to different major risks) to win on the financial market while loosing on the physical one following a cat. A pandemic is a cat. Unfortunately a nat cat risk management strategy based on securitization cannot be identically replicated for a pandemic cat. In this paper, we discuss the main differences between nat cats (also technological disasters) and pandemic catastrophes in terms of the economic losses. Risk correlation, basis risk, moral hazard, failure of world mutualization are mainly at stake. Then we propose a coverage strategy of the pandemic business interruption risk that combines self-insurance, insurance contracts, double triggered pandemic bonds and contingent public debt, each tool being mobilized with regard to their opportunity, transaction and management costs. We also discuss the outline of an adequate hybrid risk management governance by answering the question ’Who shall issue what?’.
    Keywords: pandemic risk, operational losses, (re)insurance, securitization, corporate risk management.
    JEL: G11 Q54 G22
    Date: 2021
  4. By: Runhuan Feng; Peng Li
    Abstract: Nested stochastic modeling has been on the rise in many fields of the financial industry. Such modeling arises whenever certain components of a stochastic model are stochastically determined by other models. There are at least two main areas of applications, including (1) portfolio risk management in the banking sector and (2) principle-based reserving and capital requirements in the insurance sector. As financial instrument values often change with economic fundamentals, the risk management of a portfolio (outer loop) often requires the assessment of financial positions subject to changes in risk factors in the immediate future. The valuation of financial position (inner loop) is based on projections of cashflows and risk factors into the distant future. The nesting of such stochastic modeling can be computationally challenging. Most of existing techniques to speed up nested simulations are based on curve fitting. The main idea is to establish a functional relationship between inner loop estimator and risk factors by running a limited set of economic scenarios, and, instead of running inner loop simulations, inner loop estimations are made by feeding other scenarios into the fitted curve. This paper presents a non-conventional approach based on the concept of sample recycling. Its essence is to run inner loop estimation for a small set of outer loop scenarios and to find inner loop estimates under other outer loop scenarios by recycling those known inner loop paths. This new approach can be much more efficient when traditional techniques are difficult to implement in practice.
    Date: 2021–06
  5. By: Anand Deo; Karthyek Murthy
    Abstract: This paper considers Importance Sampling (IS) for the estimation of tail risks of a loss defined in terms of a sophisticated object such as a machine learning feature map or a mixed integer linear optimisation formulation. Assuming only black-box access to the loss and the distribution of the underlying random vector, the paper presents an efficient IS algorithm for estimating the Value at Risk and Conditional Value at Risk. The key challenge in any IS procedure, namely, identifying an appropriate change-of-measure, is automated with a self-structuring IS transformation that learns and replicates the concentration properties of the conditional excess from less rare samples. The resulting estimators enjoy asymptotically optimal variance reduction when viewed in the logarithmic scale. Simulation experiments highlight the efficacy and practicality of the proposed scheme
    Date: 2021–06
  6. By: Muyang Ge; Shen Zhou; Shijun Luo; Boping Tian
    Abstract: Option pricing is a significant problem for option risk management and trading. In this article, we utilize a framework to present financial data from different sources. The data is processed and represented in a form of 2D tensors in three channels. Furthermore, we propose two deep learning models that can deal with 3D tensor data. Experiments performed on the Chinese market option dataset prove the practicability of the proposed strategies over commonly used ways, including B-S model and vector-based LSTM.
    Date: 2021–06
  7. By: Kartik Sethi; Siddhartha P. Chakrabarty
    Abstract: We undertake an empirical analysis for the premium data of non-life insurance companies operating in India, in the paradigm of fitting the data for the parametric distribution of Lognormal and the extreme value based distributions of Generalized Extreme Value and Generalized Pareto. The best fit to the data for ten companies considered, is obtained for the Generalized Extreme Value distribution.
    Date: 2021–06
  8. By: Chenyanzi Yu; Tianyang Xie
    Abstract: Pair trading is one of the most discussed topics among financial researches. Despite a growing base of work, portfolio management for multivariate time series is rarely discussed. On the other hand, most researches focus on refining strategy rules instead of finding the optimal portfolio weight. In this paper, we brought up a simple yet profitable strategy called Volatility & Model Adaption Trade-off (VMAT) to leverage the issues. Experiment studies show its superior profit performance over baselines.
    Date: 2021–06
  9. By: Nuno Coimbra; Daisoon Kim; Hélène Rey
    Abstract: Before the 2008 crisis, the cross-sectional skewness of banks’ leverage went up and macro risk concentrated in the balance sheets of large banks. Using a model of profit-maximizing banks with heterogeneous Value-at-Risk constraints, we extract the distribution of banks’ risk-taking parameters from balance sheet data. The time series of these estimates allow us to understand systemic risk and its concentration in the banking sector over time. Counterfactual exercises show that (1) monetary policymakers confront the trade-off between stimulating the economy and financial stability, and (2) macroprudential policies can be effective tools to increase financial stability.
    JEL: E0 E5 F3 G01
    Date: 2021–06
  10. By: Areski Cousin (IRMA - Institut de Recherche Mathématique Avancée - UNISTRA - Université de Strasbourg - CNRS - Centre National de la Recherche Scientifique); Ying Jiao (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Christian Robert (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique); Olivier David Zerbib
    Abstract: This paper investigates the optimal asset allocation of a financial institution whose customers are free to withdraw their capital-guaranteed financial contracts at any time. Accounting for asset-liability mismatch risk of the institution, we present a general utility optimization problem in discrete time setting and provide a dynamic programming principle for the optimal investment strategies. Furthermore, we consider an explicit context, including liquidity risk, interest rate and credit intensity fluctuations, and show, by numerical results, that the optimal strategy improves the solvency and the asset returns of the institution compared to the baseline asset allocation.
    Keywords: Asset allocation,asset-liability management,withdrawal risk,liquidity risk,utility maximization
    Date: 2021–06–01
  11. By: Nick James; Max Menzies
    Abstract: This paper introduces a new framework to quantify distance between finite sets with uncertainty present, where probability distributions determine the locations of individual elements. Combining this with a Bayesian change point detection algorithm, we produce a new measure of similarity between time series with respect to their structural breaks. Next, we apply this to financial data to study the erratic behavior profiles of 19 countries and 11 sectors over the past 20 years. Then, we take a closer examination of individual equities and their behavior surrounding market crises, times when change points are consistently observed. Combining new and existing methods, we study the dynamics of our collection of equities and highlight an increase in equity similarity in recent years, particularly during such crises. Finally, we show that our methodology may provide a new outlook on diversification and risk-reduction during times of extraordinary correlation between assets, where traditional portfolio optimization algorithms encounter difficulties.
    Date: 2021–06
  12. By: Alex Garivaltis
    Abstract: I juxtapose Cover's vaunted universal portfolio selection algorithm (Cover 1991) with the modern representation (Qian 2016; Roncalli 2013) of a portfolio as a certain allocation of risk among the available assets, rather than a mere allocation of capital. Thus, I define a Universal Risk Budgeting scheme that weights each risk budget (instead of each capital budget) by its historical performance record (a la Cover). I prove that my scheme is mathematically equivalent to a novel type of Cover and Ordentlich 1996 universal portfolio that uses a new family of prior densities that have hitherto not appeared in the literature on universal portfolio theory. I argue that my universal risk budget, so-defined, is a potentially more perspicuous and flexible type of universal portfolio; it allows the algorithmic trader to incorporate, with advantage, his prior knowledge (or beliefs) about the particular covariance structure of instantaneous asset returns. Say, if there is some dispersion in the volatilities of the available assets, then the uniform (or Dirichlet) priors that are standard in the literature will generate a dangerously lopsided prior distribution over the possible risk budgets. In the author's opinion, the proposed "Garivaltis prior" makes for a nice improvement on Cover's timeless expert system (Cover 1991), that is properly agnostic and open (from the very get-go) to different risk budgets. Inspired by Jamshidian 1992, the universal risk budget is formulated as a new kind of exotic option in the continuous time Black and Scholes 1973 market, with all the pleasure, elegance, and convenience that that entails.
    Date: 2021–06
  13. By: Samuel Palmer; Serkan Sahin; Rodrigo Hernandez; Samuel Mugel; Roman Orus
    Abstract: In this paper we show how to implement in a simple way some complex real-life constraints on the portfolio optimization problem, so that it becomes amenable to quantum optimization algorithms. Specifically, first we explain how to obtain the best investment portfolio with a given target risk. This is important in order to produce portfolios with different risk profiles, as typically offered by financial institutions. Second, we show how to implement individual investment bands, i.e., minimum and maximum possible investments for each asset. This is also important in order to impose diversification and avoid corner solutions. Quite remarkably, we show how to build the constrained cost function as a quadratic binary optimization (QUBO) problem, this being the natural input of quantum annealers. The validity of our implementation is proven by finding the efficient frontier, using D-Wave Hybrid and its Advantage quantum processor, on static portfolios taking assets from the S&P500. We use three different subsets of this index. First, the S&P100 which consists of 100 of the largest companies of the S&P500; second, the 200 best-performing companies of the S&P500; and third, the full S&P500 itself. Our results show how practical daily constraints found in quantitative finance can be implemented in a simple way in current NISQ quantum processors, with real data, and under realistic market conditions. In combination with clustering algorithms, our methods would allow to replicate the behaviour of more complex indexes, such as Nasdaq Composite or others, in turn being particularly useful to build and replicate Exchange Traded Funds (ETF).
    Date: 2021–06
  14. By: Roncalli, Thierry; Cherief, Amina; Karray-Meziou, Fatma; Regnault, Margaux
    Abstract: This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers. In this second article focused on asset liquidity risk modeling, we propose a market impact model to estimate transaction costs. After presenting a toy model that helps to understand the main concepts of asset liquidity, we consider a two-regime model, which is based on the power-law property of price impact. Then, we define several asset liquidity measures such as liquidity cost, liquidation ratio and shortfall or time to liquidation in order to assess the different dimensions of asset liquidity. Finally, we apply this asset liquidity framework to stocks and bonds and discuss the issues of calibrating the transaction cost model.
    Keywords: Asset liquidity, stress testing, bid-ask spread, market impact, transaction cost, participation rate, power law, liquidation cost, liquidation ratio, liquidation shortfall, time to liquidation
    JEL: C02 G32
    Date: 2021–04–01
  15. By: Yuan Hu; Abootaleb Shirvani; W. Brent Lindquist; Frank J. Fabozzi; Svetlozar T. Rachev
    Abstract: Applying the Cherny-Shiryaev-Yor invariance principle, we introduce a generalized Jarrow-Rudd (GJR) option pricing model with uncertainty driven by a skew random walk. The GJR pricing tree exhibits skewness and kurtosis in both the natural and risk-neutral world. We construct implied surfaces for the parameters determining the GJR tree. Motivated by Merton's pricing tree incorporating transaction costs, we extend the GJR pricing model to include a hedging cost. We demonstrate ways to fit the GJR pricing model to a market driver that influences the price dynamics of the underlying asset. We supplement our findings with numerical examples.
    Date: 2021–06
  16. By: Ying Jiao (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Yahia Salhi (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Shihua Wang
    Abstract: The dependence structure of the life statuses plays an important role in the valuation of life insurance products involving multiple lives. Although the mortality of individuals is well studied in the literature, their dependence remains a challenging field. In this paper, the main objective is to introduce a new approach for analyzing the mortality dependence between two individuals in a couple. It is intended to describe in a dynamic framework the joint mortality of married couples in terms of marginal mortality rates. The proposed framework is general and aims to capture, by adjusting some parametric form, the desired effect such as the "broken-heart syndrome". To this end, we use a well-suited multiplicative decomposition, which will serve as a building block for the framework and thus will be used to separate the dependence structure from the marginals. We make the link with the existing practice of affine mortality models. Finally, given that the framework is general, we propose some illustrative examples and show how the underlying model captures the main stylized facts of bivariate mortality dynamics.
    Keywords: Bivariate Mortality,Dependence,Conditional Survival Probability,Copula,Broken-Heart Syndrome
    Date: 2021–06–01
  17. By: López-Espinosa, Germán; Ormazabal, Gaizka; Sakasai, Yuki
    Abstract: This paper provides early evidence on the effect of global regulation mandating a switch from loan loss provisioning (LLP) based on incurred credit losses (ICL) to LLP based on expected credit losses (ECL). Using a sample of systemically important banks from 74 countries, we find that ECL provisions are more predictive of future bank risk than ICL provisions. To corroborate that the switch to ECL provisioning results in more information to assess bank risk, we analyze the market reaction to disclosures on the first-time impact of the accounting change; we find that a higher impact on loan loss allowances elicits lower stock returns, higher changes in CDS spreads, and higher changes in bid-ask spreads. Critically, these patterns are most pronounced when credit conditions deteriorate. Finally, we also find evidence that, as credit conditions worsen, the rule change induces an increase in provisions and a contraction of credit. Our study contributes to the debate on the effect of the ECL model on procyclicality, an especially pressing issue in the context of the current pandemic.
    Keywords: bank accounting; expected credit losses; loan loss provision
    JEL: G21 M41
    Date: 2020–05
  18. By: Dong, Xueqi
    Abstract: This note studies the implication of the general notion of uncertainty aversion (Schmeidler 1989) on the problem of portfolio choice, which involves allocating the proportions of fixed capital to several assets. We prove that if an investor is both risk averse and uncertainty averse, then preference in a portfolio space is convex. This result means that the convexity in a portfolio choice problem can be guaranteed without restricting preference representation to a particular functional form.
    Keywords: Convexity, Portfolio Choice, Ambiguity, Uncertainty Aversion, Risk Aversion
    JEL: D8
    Date: 2021–06–11
  19. By: Eva L\"utkebohmert; Thorsten Schmidt; Julian Sester
    Abstract: We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black-Scholes model as well as the constant elasticity of variance (CEV) model as special cases. Based on a general dynamic programming principle, we are able to link the associated nonlinear expectation to a variational form of the Kolmogorov equation which opens the door for fast numerical pricing in the robust framework. The main novelty of the paper is that we propose a deep hedging approach which efficiently solves the hedging problem under parameter uncertainty. We numerically evaluate this method on simulated and real data and show that the robust deep hedging outperforms existing hedging approaches, in particular in highly volatile periods.
    Date: 2021–06
  20. By: Wenyong Zhang; Lingfei Li; Gongqiu Zhang
    Abstract: We propose a two-step framework for predicting the implied volatility surface over time without static arbitrage. In the first step, we select features to represent the surface and predict them over time. In the second step, we use the predicted features to construct the implied volatility surface using a deep neural network (DNN) model by incorporating constraints that prevent static arbitrage. We consider three methods to extract features from the implied volatility data: principal component analysis, variational autoencoder and sampling the surface, and we predict these features using LSTM. Using a long time series of implied volatility data for S\&P500 index options to train our models, we find that sampling the surface with DNN for surface construction achieves the smallest error in out-of-sample prediction. Furthermore, the DNN model for surface construction not only removes static arbitrage, but also significantly reduces the prediction error compared with a standard interpolation method. Our framework can also be used to simulate the dynamics of the implied volatility surface without static arbitrage.
    Date: 2021–06
  21. By: Estepa-Mohedano, Lorenzo; Espinosa, Maria Paz
    Abstract: Eliciting risk preferences usually involves tasks that subjects may find complex, such as calculations of expected values and assessment of probabilities in multiple price lists (MPL). There is a serious concern that the decisions of the subjects may be driven by miscalculations or miscalibration of probabilities, rather than by their risk preferences. In this paper, we test whether introducing aids to the usual lottery choiceswould help to reduce the error rate and possibly change risk aversion elicitation. The experiment was run with subjectsfrom a rural area in Honduras. We compare the risk elicitation results of a multiple price list and two different treatments, one with visual aids (graphical representation of probabilities) and the other with contextual framing aids (bills to represent rewards and a distribution of ten beans between the two rewards to represent a lottery). Our results indicate that risk attitudes elicitation was affected with contextual framing aids, reducing risk aversion. For the treatment with visual aids we observe no effect.
    Keywords: risk elicitation, visual aids, contextual framing aids
    JEL: C93 D81
    Date: 2021–06–08
  22. By: Jiri Witzany (Faculty of Finance and Accounting, Prague University of Business and Economics, Czech Republic,); Martin Divis (Faculty of Finance and Accounting, Prague University of Business and Economics, Czech Republic)
    Abstract: Interest rate risk measurement and management of non-maturity deposit balances presents a challenge for practitioners and academic researchers as well. The paper provides a review of several methodological approaches focusing on the area of savings accounts rate sensitivity modeling and estimation. The proposed models are tested on a Czech banking sector dataset providing mixed results regarding the cointegration type models generally recommended in the literature. On the other hand, the analysis shows that simpler regression models may provide more robust results if the cointegration tests between the saving accounts rate and the market rate series fail.
    Keywords: Interest rate risk, savings accounts, non-maturity deposits, cointegration, pass through rate
    JEL: C32 E43 E58 G21
    Date: 2021–06
  23. By: Marc-Arthur Diaye (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); André Lapidus (PHARE - Philosophie, Histoire et Analyse des Représentations Économiques - UP1 - Université Paris 1 Panthéon-Sorbonne); Christian Schmidt (PHARE - Philosophie, Histoire et Analyse des Représentations Économiques - UP1 - Université Paris 1 Panthéon-Sorbonne)
    Abstract: This paper aims at restating, in a decision theory framework, the results of some signicant contributions of the literature on probability discounting that followed the publication of the pioneering article by Rachlin et al. (1991). We provide a restatement of probability discounting in terms of rank-dependent utility, in which the utilities of the outcomes of n-issues lotteries are weighted by probabilities transformed after their transposition into time-delays. This formalism makes the typical cases of rationality in time and in risk mutually exclusive, but allows looser types of rationality. The resulting attitude toward probability and toward risk are then determined in relation to the values of the two parameters involved in the procedure of probability discounting.
    Keywords: time discounting,Probability discounting,logarithmic time perception,rank-dependent utility,rationality,attitude toward probabilities,attitude toward risk
    Date: 2021–06–10
  24. By: Francesco Buono; Camilla Cal\`i; Maria Longobardi
    Abstract: The concept of varentropy has been recently introduced as a dispersion index of the reliability of measure of information. In this paper, we introduce new measures of variability for two bivariate measures of uncertainty, the Kerridge inaccuracy measure and the Kullback-Leibler divergence. These new definitions and related properties, bounds and examples are presented. Finally we show an application of Kullback-Leibler divergence and its dispersion index using the mean-variance rule introduced in portfolio theory.
    Date: 2021–06
  25. By: Laura Alfaro; Mauricio Calani; Liliana Varela
    Abstract: Foreign currency derivative markets are among the largest in the world, yet their role in emerging markets is relatively understudied. We study firms' currency risk exposure and their hedging choices by employing a unique dataset covering the universe of FX derivatives transactions in Chile since 2005, together with firm-level information on sales, international trade, trade credits and foreign currency debt. We uncover four novel facts: (i) natural hedging of currency risk is limited, (ii) financial hedging is more likely to be used by larger firms and for larger amounts, (iii) firms in international trade are more likely to use FX derivatives to hedge their gross --not net-- cash currency risk, and (iv) firms are more likely to pay higher premiums for longer maturity contracts. We then show that financial intermediaries can affect the forward exchange rate market through a liquidity channel, by leveraging a regulatory negative supply shock that reduced firms' use of FX derivatives and increased the forward premiums.
    JEL: F31 F38 G30 G38
    Date: 2021–06
  26. By: Carletti, Elena; Oliviero, Tommaso; Pagano, Marco; Pelizzon, Loriana; Subrahmanyam, Marti G.
    Abstract: This paper estimates the drop in profits and the equity shortfall triggered by the COVID-19 shock and the subsequent lockdown, using a representative sample of 80,972 Italian firms. We find that a 3-month lockdown entails an aggregate yearly drop in profits of 170 billion euros, with an implied equity erosion of 117 billion euros for the whole sample, and 31 billion euros for firms that became distressed, i.e., ended up with negative book value after the shock. As a consequence of these losses, about 17% of the sample firms, whose employees account for 8.8% of total employment in the sample (about 800 thousand employees), become distressed. Small and medium-sized enterprises (SMEs) are affected disproportionately, with 18.1% of small firms, and 14.3% of medium-sized ones becoming distressed, against 6.4% of large firms. The equity shortfall and the extent of distress are concentrated in the Manufacturing and Wholesale Trading sectors and in the North of Italy. Since many firms predicted to become distressed due to the shock had fragile balance sheets even prior to the COVID-19 shock, restoring their equity to their pre-crisis levels may not suffice to ensure their long-term solvency.
    Keywords: COVID-19; Distress; equity; losses; Pandemics; Recapitalization
    JEL: G01 G32 G33
    Date: 2020–05
  27. By: Ojea-Ferreiro, Javier (European Commission); Reboredo, Juan C. (Universidade de Santiago de Compostela)
    Abstract: We assess the role played by exchange rates in buffering or amplifying the propagation of shocks across international equity markets. Using copula functions we model the joint dependence between exchange rates and two global equity markets and, from a copula framework, we obtain the conditional expectation and measure the exchange rate contribution to shock propagation between those equity markets. Our estimates for emerging Latin American economies (Argentina, Brazil, Chile and Mexico) and two developed markets (Europe and the USA) document the following: (a) the contribution of exchange rates to the transmission of equity shocks is time varying and asymmetric and differs across countries; and (b) exchange rates diversify shocks from abroad for investors based in emerging economies (particularly Brazil, Chile and Mexico) and echo the effect of shocks from abroad for investors based in developed markets. This evidence has implications for international investors in terms of portfolio and risk management decisions.
    Keywords: Exchange rates; International equity markets; Copulas; Expected shortfall
    JEL: C58 F31 G15
    Date: 2021–04

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