nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒01‒03
27 papers chosen by
Stan Miles
Thompson Rivers University

  1. Insurance valuation: A two-step generalised regression approach By Karim Barigou; Valeria Bignozzi; Andreas Tsanakas
  2. Building a hurricane risk map for continental Portugal based on loss data from hurricane Leslie By Andrea Hauser; Carlos Rosa; Rui Esteves; Alexandra Moura; Carlos Oliveira
  3. Deep Hedging under Rough Volatility By Blanka Horvath; Josef Teichmann; Zan Zuric
  4. Pricing equity-linked life insurance contracts with multiple risk factors by neural networks By Karim Barigou; Lukasz Delong
  5. Portfolio Allocation and Borrowing Constraints By Raslan Alzuabi; Sarah Brown; Daniel Gray; Mark N Harris; Christopher Spencer
  6. Option Pricing with State-dependent Pricing Kernel By Chen Tong; Peter Reinhard Hansen; Zhuo Huang
  7. A changepoint analysis of exchange rate and commodity price risks for Latin American stock markets By Hans Manner; Gabriel Rodriguez; Florian Stöckler
  8. Precautionary motives with multiple instruments By Heinzel Christoph; Richard Peter
  9. The financial origins of non-fundamental risk By Sushant Acharya; Keshav Dogra; Sanjay R. Singh
  10. Realized GARCH, CBOE VIX, and the Volatility Risk Premium By Peter Reinhard Hansen; Zhuo Huang; Chen Tong; Tianyi Wang
  11. La modélisation de la dynamique des volatilités et des corrélations entre les prix des matières premières et les rendements boursiers By Sami Mestiri; Sabrine Abdelghani
  12. Inequality in economic shock exposures across the global firm-level supply network By Abhijit Chakraborty; Tobias Reisch; Christian Diem; Stefan Thurner
  13. Sovereign Risk and Financial Risk By Simon Gilchrist; Bin Wei; Vivian Z. Yue; Egon Zakrajšek
  14. Bridging socioeconomic pathways of CO2 emission and credit risk By Florian Bourgey; Emmanuel Gobet; Ying Jiao
  15. Scenario-Free Analysis of Financial Stability with Interacting Contagion Channels By Farmer, J. Doyne; Kleinnijenhuis, Alissa; Wetzer, Thom; Wiersema, Garbrand
  16. American options in a non-linear incomplete market model with default By Miryana Grigorova; Marie-Claire Quenez; Agnès Sulem
  17. The Fairness of Credit Scoring Models By Christophe HURLIN; Christophe PERIGNON; Sébastien SAURIN
  18. Exploration of machine learning algorithms for maritime risk applications By Knapp, S.; van de Velden, M.
  19. Central bank balance sheet and systemic risk By Maelle Vaille
  20. Superstar Returns By Francisco Amaral; Martin Dohmen; Sebastian Kohl; Moritz Schularick
  21. Capital Controls and the Global Financial Cycle By Marina Lovchikova; Johannes Matschke
  22. Screening and Monitoring Corporate Loans By Sebastian Gryglewicz; Simon Mayer; Erwan Morellec
  23. Systemic implications of the bail-in design By Farmer, J. Doyne; Kleinnijenhuis, Alissa; Goodhart, Charles
  24. Ambiguity, Long-Run Risks, and Asset Prices By Bin Wei
  25. Asymmetries in Risk Premia, Macroeconomic Uncertainty and Business Cycles By Christoph Görtz; Mallory Yeromonahos
  26. On Risk and Time Pressure: When to Think and When to Do By Christoph Carnehl; Johannes Schneider
  27. Pricing Bermudan options using regression trees/random forests By Zineb El Filali Ech-Chafiq; Pierre Henry-Labordere; Jérôme Lelong

  1. By: Karim Barigou (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Valeria Bignozzi (Department of Statistics and Quantitative Methods University of Milano-Bicocca); Andreas Tsanakas (The Business School (formerly Cass), City, University of London)
    Abstract: Current approaches to fair valuation in insurance often follow a two-step approach, combining quadratic hedging with application of a risk measure on the residual liability, to obtain a cost-of-capital margin. In such approaches, the preferences represented by the regulatory risk measure are not reflected in the hedging process. We address this issue by an alternative two-step hedging procedure, based on generalised regression arguments, which leads to portfolios that are neutral with respect to a risk measure, such as Value-at-Risk or the expectile. First, a portfolio of traded assets aimed at replicating the liability is determined by local quadratic hedging. Second, the residual liability is hedged using an alternative objective function. The risk margin is then defined as the cost of the capital required to hedge the residual liability. In the case quantile regression is used in the second step, yearly solvency constraints are naturally satisfied; furthermore, the portfolio is a risk minimiser among all hedging portfolios that satisfy such constraints. We present a neural network algorithm for the valuation and hedging of insurance liabilities based on a backward iterations scheme. The algorithm is fairly general and easily applicable, as it only requires simulated paths of risk drivers.
    Keywords: Market-consistent valuation,Quantile regression,Solvency II,Cost-of-capital,Dynamic risk measurement
    Date: 2021–12–03
  2. By: Andrea Hauser; Carlos Rosa; Rui Esteves; Alexandra Moura; Carlos Oliveira
    Abstract: A complete model to analyse and predict future losses in the property portfolio of an insurance company due to hurricanes is proposed. A novel statistical model, in which weather data is not required, is considered. Climate data may not be reliable, or may be difficult to deal with or to obtain, hence we reconstruct the storm behaviour through the registered claims and respective losses. The model is calibrated using the loss data of the property portfolio of the insurance company Fidelidade, from hurricane Leslie, which hit the center of continental Portugal in October 2018. Several scenarios are simulated and risk maps are built. The simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can be used to adjust the policy premiums accounting for a storm risk. The risk map of the company also depends on its portfolio, namely its exposure, providing a hurricane risk management tool for the insurance company.
    Keywords: Risk; Hurricanes; Property Insurance; Regression Models
    Date: 2021–12
  3. By: Blanka Horvath (ETH Zürich - Department of Mathematics); Josef Teichmann (ETH Zurich; Swiss Finance Institute); Zan Zuric (Imperial College London - Department of Mathematics)
    Abstract: We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular we analyse the hedging performance of the original architecture under rough volatility models with view to existing theoretical results for those. Furthermore, we suggest parsimonious but suitable network architectures capable of capturing the non-Markoviantity of time-series. Secondly, we analyse the hedging behaviour in these models in terms of P&L distributions and draw comparisons to jump diffusion models if the the rebalancing frequency is realistically small.
    Keywords: Imperfect Hedging, Derivatives Pricing, Derivatives Hedging, Deep Learning, Rough Volatility
    JEL: C61 C58 C45 G32
    Date: 2021–02
  4. By: Karim Barigou (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Lukasz Delong (Warsaw School of Economics - Institut of Econometrics)
    Abstract: This paper considers the pricing of equity-linked life insurance contracts with death and survival benefits in a general model with multiple stochastic risk factors: interest rate, equity, volatility, unsystematic and systematic mortality. We price the equity-linked contracts by assuming that the insurer hedges the risks to reduce the local variance of the net asset value process and requires a compensation for the non-hedgeable part of the liability in the form of an instantaneous standard deviation risk margin. The price can then be expressed as the solution of a system of non-linear partial differential equations. We reformulate the problem as a backward stochastic differential equation with jumps and solve it numerically by the use of efficient neural networks. Sensitivity analysis is performed with respect to initial parameters and an analysis of the accuracy of the approximation of the true price with our neural networks is provided.
    Keywords: Equity-linked contracts,Neural networks,Stochastic mortality,BSDEs with jumps,Hull-White stochastic interest rates,Heston model
    Date: 2021–11–10
  5. By: Raslan Alzuabi (Department of Economics, University of Sheffield, UK); Sarah Brown (Department of Economics, University of Sheffield, UK); Daniel Gray (Department of Economics, University of Sheffield, UK); Mark N Harris (School of Accounting,Economics and Finance, Curtin University, Perth, Australia); Christopher Spencer (School of Business and Economics, Loughborough University, UK)
    Abstract: We explore the empirical relationship between borrowing constraints and household financial portfolio allocation. To motivate our analysis we develop a mean-variance model of portfolio allocation with three tradable asset classes defined by increasing risk, and establish a link between borrowing restrictions and financial portfolio allo- cation at the household level. Under non-restrictive assumptions the proportion of wealth allocated to the medium-risk asset is ambiguous. We also demonstrate that in the presence of both correlated background risk and borrowing constraints the domain of the non-binding risk-return space will be a function of background risk. We then analyse the US Survey of Consumer Finances with a view to empirically exploring the predictions of our theoretical framework. The distribution of medium-risk assets in US households is remarkably similar to that for high-risk assets, and suggests the presence of a more general ‘risk puzzle’, which our proxies for borrowing constraints partially explain. Our findings indicate that such constraints are inversely related to the proportion of financial wealth allocated to both high-risk and medium-risk assets, but are positively related to low-risk asset holdings. In light of our findings, further work aimed at accounting for the allocation of medium-risk assets in US households is considered expedient.
    Keywords: Asset Allocation; Borrowing Constraints; Fractional Models
    JEL: G11 D11 D14 C35
    Date: 2021–12
  6. By: Chen Tong; Peter Reinhard Hansen; Zhuo Huang
    Abstract: We introduce a new volatility model for option pricing that combines Markov switching with the Realized GARCH framework and leads to a novel pricing kernel with a regime-specific variance risk premium. An analytical approximation method based on an Edgeworth expansion of cumulative returns enables us to derive the pricing formula for European options in this setting. The Markov switching Realized GARCH model is easy to estimate because inferences about regimes can be deduced with realized volatility measures. In an empirical application with S&P 500 index options from 1990 to 2019, we find that investors' aversion to volatility-specific risk is time varying. The proposed framework outperforms competing methods and reduces option pricing errors by 15% or more both in-sample as well as out-of-sample.
    Date: 2021–12
  7. By: Hans Manner (University of Graz, Austria); Gabriel Rodriguez (Department of Economics, Pontificia Universidad Catolica del Peru); Florian Stöckler (University of Graz, Austria)
    Abstract: Focusing on countries whose economies are exposed to fluctuations in commodity prices and exchange rates, we study the vulnerability of these stock market returns to exchange rate and commodity price shocks. Methodologically, we rely on non-parametric structural break tests and we allow for multiple changepoints in the volatilities of the different variables and for distinct breaks in the dependence between the series. This approach allows separating changes in country- and commodity specific risk and changes in the degree of spillover. The return distributions are modeled using a Copula-GARCH model incorporating the estimated changepoints and we measure risk-spillovers with the conditional Value-at-Risk. We find evidence for various changepoints at different points in time, implying changes in risk and spillovers. In particular, there is evidence of increased spillover risk after the outbreak of the global financial crisis in 2008, but conditional risk is also high after the outbreak of Covid-19.
    Keywords: stock markets; commodity prices; changepoint analysis; volatility; dependence modeling; copula; CoVaR.
    JEL: C12 C32 C52 C53
    Date: 2021–12
  8. By: Heinzel Christoph; Richard Peter
    Abstract: Using a unified approach, we show how precautionary saving, self-protection and self-insurance are jointly determined by risk preferences and the preference over the timing of uncertainty resolution. We cover higher-order risk effects and examine both risk averters and risk lovers. When decision-makers use several instruments simultaneously to respond to income risk, substitutive interaction effects arise. We quantify precautionary and substitution effects numerically and discuss the role of instrument interaction for the inference of preference parameters from precautionary motives. Instruments can differ substantially in the size of the precautionary motive and in the susceptibility to substitution effects. This affects their suitability for the identification of precautionary preferences.
    Keywords: Préférences récursives, prudence, comportement de précaution, effets d’interaction, statique comparative.
    JEL: D11 D80 D81 G22
    Date: 2021
  9. By: Sushant Acharya; Keshav Dogra; Sanjay R. Singh (Department of Economics, University of California Davis)
    Abstract: We formalize the idea that the financial sector can be a source of non-fundamental risk. Households’ desire to hedge against price volatility can generate price volatility in equilibrium, even absent fundamental risk. Fearing that asset prices may fall, risk-averse households demand safe assets from leveraged intermediaries, whose issuance of safe assets exposes the economy to self-fulfilling fire sales. Policy can eliminate non-fundamental risk by (i) increasing the supply of publicly backed safe assets, through issuing government debt or bailing out intermediaries, or (ii) reducing the demand for safe assets, through social insurance or by acting as a market maker of last resort.
    Keywords: safe assets, self-fulfilling asset market crashes, liquidity, fire sales
    JEL: D52 D84 E62 G10 G12
    Date: 2021–12–19
  10. By: Peter Reinhard Hansen; Zhuo Huang; Chen Tong; Tianyi Wang
    Abstract: We show that the Realized GARCH model yields close-form expression for both the Volatility Index (VIX) and the volatility risk premium (VRP). The Realized GARCH model is driven by two shocks, a return shock and a volatility shock, and these are natural state variables in the stochastic discount factor (SDF). The volatility shock endows the exponentially affine SDF with a compensation for volatility risk. This leads to dissimilar dynamic properties under the physical and risk-neutral measures that can explain time-variation in the VRP. In an empirical application with the S&P 500 returns, the VIX, and the VRP, we find that the Realized GARCH model significantly outperforms conventional GARCH models.
    Date: 2021–12
  11. By: Sami Mestiri (FSEG Mahdia - Faculté des Sciences Économiques et de Gestion de Mahdia [Univ Monastir] - UM - Université de Monastir - University of Monastir); Sabrine Abdelghani
    Abstract: In this paper, our objective is to study dynamic of volatilities and correlations between the return of Tunisian stock market and commodity prices over the period 2006-2016. We used the DCC-GARCH model to determine the best strategy for hedging a portfolio against the risk of unfavorable price movements in the market. The empirical results show that the volatility of the Tunisian market can be influenced not only by its own past values or by domestic shocks but also by past shocks from international commodity markets. In addition we concluded that, the portfolio selected by a Tunisian investor should be composed mainly of stock assets rather than commodities.
    Abstract: L'objectif de ce papier est d'étudier la dynamique des volatilités et des corrélations entre le rendements du marché boursier tunisien et les prix des matières premières au cours de la période 2006-2016. Nous avons utilisé le modèle DCC-GARCH pour déterminer la meilleure stratégie de couverture d'un portefeuille contre le risque d'une évolution défavorable des prix sur le marché. Les résultats empiriques montrent que la volatilité du marché tunisien peut être influencée non seulement par ses propres valeurs passées ou par les chocs intérieurs mais aussi par les chocs passés provenant des marchés des matières premières internationaux. En plus nous avons conclue que, le portefeuille sélectionné par un investisseur tunisien doit être composé principalement des actifs boursiers plutôt que des matières premières. Mots clé : volatilité, dynamique des corrélations, DCC-GARCH, GARCH multivarié, stratégie de couverture, poids de portefeuille optimal.
    Keywords: volatilities,dynamics of correlation,DCC-GARCH,multivariate GARCH,hedging strategy,optimal portfolio weight
    Date: 2021–11–17
  12. By: Abhijit Chakraborty; Tobias Reisch; Christian Diem; Stefan Thurner
    Abstract: For centuries, national economies created wealth by engaging in international trade and production. The resulting international supply networks not only increase wealth for countries, but also create systemic risk: economic shocks, triggered by company failures in one country, may propagate to other countries. Using global supply network data on the firm-level, we present a method to estimate a country's exposure to direct and indirect economic losses caused by the failure of a company in another country. We show the network of systemic risk-flows across the world. We find that rich countries expose poor countries much more to systemic risk than the other way round. We demonstrate that higher systemic risk levels are not compensated with a risk premium in GDP, nor do they correlate with economic growth. Systemic risk around the globe appears to be distributed more unequally than wealth. These findings put the often praised benefits for developing countries from globalized production in a new light, since they relate them to the involved risks in the production processes. Exposure risks present a new dimension of global inequality, that most affects the poor in supply shock crises. It becomes fully quantifiable with the proposed method.
    Date: 2021–12
  13. By: Simon Gilchrist; Bin Wei; Vivian Z. Yue; Egon Zakrajšek
    Abstract: In this paper, we study the interplay between sovereign risk and global financial risk. We show that a substantial portion of the comovement among sovereign spreads is accounted for by changes in global financial risk. We construct bond-level sovereign spreads for dollar-denominated bonds issued by more than 50 countries from 1995 to 2020 and use various indicators to measure global financial risk. Through panel regressions and local projection analysis, we find that an increase in global financial risk causes a large and persistent widening of sovereign bond spreads. These effects are strongest when measuring global risk using the excess bond premium, which is a measure of the risk-bearing capacity of US financial intermediaries. The spillover effects of global financial risk are more pronounced for speculative-grade sovereign bonds.
    Keywords: sovereign bonds; CDS; global financial risk; excess bond premium; global financial cycle
    JEL: E43 E44 F33 G12
    Date: 2021–11–24
  14. By: Florian Bourgey (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique, Bloomberg L.P. Quantitative Finance Research - Bloomberg L.P.); Emmanuel Gobet (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Ying Jiao (ISFA - Institut de Science Financière et d'Assurances)
    Abstract: This paper investigates the impact of transition risk on a firm's low-carbon production. As the world is facing global climate changes, the Intergovernmental Panel on Climate Change (IPCC) has set the idealized carbon-neutral scenario around 2050. In the meantime, many carbon reduction scenarios, known as Shared Socioeconomic Pathways (SSPs) have been proposed in the literature for different production sectors in more comprehensive socioeconomic context. In this paper, we consider, on the one hand, a firm that aims to optimize its emission level under the double objectives of maximizing its production profit and respecting the emission mitigation scenarios. Solving the penalized optimization problem provides the optimal emission according to a given SSP benchmark. On the other hand, such transitions affect the firm's credit risk. We model the default time by using the structural default approach. We are particularly concerned with how the adopted strategies by following different SSPs scenarios may influence the firm's default probability.
    Keywords: Climate risk,transition risk,credit risk,Shared Socioeconomic Pathways,carbon emission reduction,optimal production profit
    Date: 2021–11–30
  15. By: Farmer, J. Doyne; Kleinnijenhuis, Alissa; Wetzer, Thom; Wiersema, Garbrand
    Abstract: Currently financial stress test simulations that take into account multiple interacting contagion mechanisms are conditional on a specific, subjectively imposed stress-scenario. Eigenvalue-based approaches, in contrast, provide a scenario-independent measure of systemic stability, but only handle a single contagion mechanism. We develop an eigenvalue-based approach that gives the best of both worlds, allowing analysis of multiple, interacting contagion channels without the need to impose a subjective stress scenario. This allows us to demonstrate that the instability due to interacting channels can far exceed that of the sum of the individual channels acting alone. We derive an analytic formula in the limit of a large number of institutions that gives the instability threshold as a function of the relative size and intensity of contagion channels, providing valuable insights into financial stability whilst requiring very little data to be calibrated to real financial systems.
    Keywords: Financial Stability, Systemic Risk, Interacting Contagion Channels, Financial Contagion, Multiplex Networks, Stress Test, Liquidity-Solvency Nexus
    JEL: G01 G17 G18 G21 G23 G28
    Date: 2020–01
  16. By: Miryana Grigorova (School of Mathematics - University of Leeds - University of Leeds); Marie-Claire Quenez (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UP - Université de Paris); Agnès Sulem (MATHRISK - Mathematical Risk Handling - UPEM - Université Paris-Est Marne-la-Vallée - ENPC - École des Ponts ParisTech - Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique)
    Abstract: We study the superhedging prices and the associated superhedging strategies for American options in a non-linear incomplete market model with default. The points of view of the seller and of the buyer are presented. The underlying market model consists of a risk-free asset and a risky asset driven by a Brownian motion and a compensated default martingale. The portfolio processes follow non-linear dynamics with a non-linear driver f. We give a dual representation of the seller's (superhedging) price for the American option associated with a completely irregular payoff $(\xi_t)$ (not necessarily càdlàg) in terms of the value of a non-linear mixed control/stopping problem. The dual representation involves a suitable set of equivalent probability measures, which we call f-martingale probability measures. We also provide two infinitesimal characterizations of the seller's price process: in terms of the minimal supersolution of a constrained reflected BSDE and in terms of the minimal supersolution of an optional reflected BSDE. Under some regularity assumptions on $\xi$, we also show a duality result for the buyer's price in terms of the value of a non-linear control/stopping game problem.
    Keywords: Control problems with non-linear expectation,Constrained reflected BSDE,f-expectation,Non-linear pricing,Incomplete markets,American options,Optimal stopping with non-linear expectation,Non-linear optional decomposition,Pricing-hedging duality
    Date: 2021
  17. By: Christophe HURLIN; Christophe PERIGNON; Sébastien SAURIN
    Keywords: , Discrimination, Credit markets, Machine Learning, Artificial intelligence
    Date: 2021
  18. By: Knapp, S.; van de Velden, M.
    Abstract: To manage and pre-empt incident risks effectively by maritime stakeholders, predicted incident probabilities at ship level have different application aspects such as enhanced targeting for ship inspections, improved domain awareness and improving risk exposure assessments for strategic planning and asset allocations to manage risk exposure. Using a unique and comprehensive global dataset from 2014 to 2020 of 1.2 million observations, this study explores 144 model variants from the field of machine learning (18 random forest variants for 8 incident endpoints of interest) with the aim to enhance prediction capabilities to be used in maritime applications. An additional point of interest is to determine and highlight the relative importance of over 500 evaluated covariates. The results differ for each endpoint of interest and confirm that random forest methods improve prediction capabilities, based on a full year of out of sample evaluation. Targeting the top 10% most risky vessels would lead to an improvement of predictions by 2.7 to 4.9 compared to random selection. Balanced random forests and random forests with balanced training variants outperform regular random forests where the end selection of the variants also depends on the aggregation type and use of probabilities in the application areas of interest. The most important covariate groups to predict incident risk are related to beneficial ownership, the safety management company, size and age of the vessel and the importance of these factors is similar across the endpoint of interest considered here
    Keywords: ship specific risk, safety quality, reducing false negative events, risk exposure estimation, machine learning, case weighting, subsampling, random forest, sampling, evaluation metrics, top decile lift, variable importance, machine learning
    Date: 2021–12–13
  19. By: Maelle Vaille (Larefi - Laboratoire d'analyse et de recherche en économie et finance internationales - Université Montesquieu - Bordeaux 4)
    Date: 2021
  20. By: Francisco Amaral (Macro Finance Lab, University of Bonn); Martin Dohmen (Macro Finance Lab,University of Bonn); Sebastian Kohl (MPIfG Cologne); Moritz Schularick (Macro Finance Lab, University of Bonn, Sciences Po Paris, and Federal Reserve Bank of New York)
    Abstract: We study long-term returns on residential real estate in 27 “superstar” cities in 15 countries over 150 years. We find that total returns in superstar cities are close to 100 basis points lower per year than in the rest of the country. House prices tend to grow faster in the superstars, but rent returns are substantially greater outside the big agglomerations, resulting in higher long-run total returns. The excess returns outside the superstars can be rationalized as a compensation for risk, especially for higher co-variance with income growth and lower liquidity. Superstar real estate is comparatively safe.
    Keywords: housing returns, housing risk, superstar cities, regional housing markets
    JEL: G10 G12 N90 R21 R31
    Date: 2021–12
  21. By: Marina Lovchikova; Johannes Matschke
    Abstract: Capital flows into emerging markets are volatile and associated with risks. A common prescription is to impose counter-cyclical capital controls that tighten during economic booms to mitigate future sudden-stop dynamics, but it has been challenging to document such patterns in the data. Instead, we show that emerging markets tighten their capital controls in response to volatility in international financial markets and elevated risk aversion. We develop a model in which this behavior arises from a desire to manipulate the risk premium. When investors are more risk-averse or markets are volatile, investors require a high marginal compensation to hold risky emerging market debt. Regulators are able to exploit this tight link and raise capital inflow controls, thereby lowering the risk premium and reducing the overall cost of debt. We emphasize that risk premium manipulations via capital controls are only optimal from the perspective of the individual emerging market, but not from a global perspective. This suggests that the use of capital controls may impose costs in an international context.
    Keywords: Capital Controls; Risk Aversion; Risk Premium; Volatility
    JEL: F36 F38 F41
    Date: 2021–09–08
  22. By: Sebastian Gryglewicz (Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE)); Simon Mayer (University of Chicago - Booth School of Business); Erwan Morellec (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute)
    Abstract: We study a dynamic moral hazard problem in which a bank originates a pool of loans that it sells to competitive investors via securitization. The bank controls the default risk of the loans by screening at origination and monitoring after origination, but it is subject to moral hazard. The optimal contract between the bank and investors can be implemented via a time-decreasing stake within the loan pool, so the bank's monitoring incentives decrease and default risk increases over time. We find that screening and monitoring have positive incentive synergies and are complements. Credit ratings distort incentives, potentially increasing credit risk, and are particularly beneficial for high quality and short-maturity loans.
    Keywords: Corporate Bonds, Securitization, CLOs, Debt Maturity, Banking, Dynamic Contracting
    Date: 2021–10
  23. By: Farmer, J. Doyne; Kleinnijenhuis, Alissa; Goodhart, Charles
    Abstract: The 2007-2008 financial crisis forced governments to choose between the unattractive alternatives of either bailing out a systemically important bank (SIB) or allowing it to fail disruptively. Bail-in has been put forward as an alternative that potentially addresses the too-big-to-fail and contagion risk problems simultaneously. Though its efficacy has been demonstrated for smaller idiosyncratic SIB failures, its ability to maintain stability in cases of large SIB failures and system-wide crises remains untested. This paper's novelty is to assess the financial-stability implications of bail-in design, explicitly accounting for the multilayered networked nature of the financial system. We present a model of the European financial system that captures all five of the prevailing contagion channels. We demonstrate that it is essential to understand the interaction of multiple contagion mechanisms and that financial institutions other than banks play an important role. Our results indicate that stability hinges on the bank-specific and structural bail-in design. On one hand, a well designed bail-in buttresses financial resilience, but on the other hand, an ill-designed bail-in tends to exacerbate financial distress, especially in system-wide crises and when there are large SIB failures. Our analysis suggests that the current bail-in design may be in the region of instability. While policy makers can fix this, the political economy incentives make this unlikely.
    Keywords: Too big to fail, resolution, bail-in, liquidation, insolvency law, financial crisis, contagion, financial networks, failure, default, bail-out, banks, systemically important banks, loss absorption requirements, bail-in debt, bail-in debt pricing, political economy
    Date: 2021–08
  24. By: Bin Wei
    Abstract: I generalize the long-run risks (LRR) model of Bansal and Yaron (2004) by incorporating recursive smooth ambiguity aversion preferences from Klibanoff et al. (2005, 2009) and time-varying ambiguity. Relative to the Bansal-Yaron model, the generalized LRR model is as tractable but more flexible due to its separation of ambiguity aversion from both risk aversion and the intertemporal elasticity of substitution. This three-way separation allows the model to further account for the variance premium puzzle besides the puzzles of the equity premium, the risk-free rate, and the return predictability. Specifically, the model matches reasonably well key asset-pricing moments with risk aversion under 5. Model calibration shows that the ambiguity aversion channel accounts for 77 percent of the variance premium and 40 percent of the equity premium.
    Keywords: smooth ambiguity aversion; long-run risks; equity premium puzzle; risk-free rate puzzle; variance premium puzzle; return predictability
    JEL: G12 G13 D81 E44
    Date: 2021–09–08
  25. By: Christoph Görtz (Department of Economics, University of Birmingham, UK; Rimini Centre for Economic Analysis); Mallory Yeromonahos (Department of Economics, University of Birmingham, UK)
    Abstract: A large literature suggests that the expected equity risk premium is countercyclical. Using a variety of different measures for this risk premium, we document that it also exhibits growth asymmetry, i.e. the risk premium rises sharply in recessions and declines much more gradually during the following recoveries. We show that a model with recursive preferences, in which agents cannot perfectly observe the state of current productivity, can generate the observed asymmetry in the risk premium. Key for this result are endogenous fluctuations in uncertainty which induce procyclical variations in agent's nowcast accuracy. In addition to matching moments of the risk premium, the model is also successful in generating the growth asymmetry in macroeconomic aggregates observed in the data, and in matching the cyclical relation between quantities and the risk premium.
    Keywords: Risk Premium, Business cycles, Bayesian Learning, Asymmetry, Uncertainty, Nowcasting
    JEL: E2 E3 G1
    Date: 2021–12
  26. By: Christoph Carnehl; Johannes Schneider
    Abstract: We study the tradeoff between fundamental risk and time. A time-constrained agent has to solve a problem. She dynamically allocates effort between implementing a risky initial idea and exploring alternatives. Discovering an alternative implies progress that has to be converted to a solution. As time runs out, the chances of converting it in time shrink. We show that the agent may return to the initial idea after having left it in the past to explore alternatives. Our model helps explain so-called false starts. The agent takes risks early on to quickly arrive at a solution, sacrificing the prospects of alternatives.
    Date: 2021–11
  27. By: Zineb El Filali Ech-Chafiq (DAO - Données, Apprentissage et Optimisation - LJK - Laboratoire Jean Kuntzmann - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes, Natixis); Pierre Henry-Labordere (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique, Natixis); Jérôme Lelong (DAO - Données, Apprentissage et Optimisation - LJK - Laboratoire Jean Kuntzmann - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)
    Abstract: The value of an American option is the maximized value of the discounted cash flows from the option. At each time step, one needs to compare the immediate exercise value with the continuation value and decide to exercise as soon as the exercise value is strictly greater than the continuation value. We can formulate this problem as a dynamic programming equation, where the main difficulty comes from the computation of the conditional expectations representing the continuation values at each time step. In (Longstaff and Schwartz, 2001), these conditional expectations were estimated using regressions on a finite-dimensional vector space (typically a polynomial basis). In this paper, we follow the same algorithm; only the conditional expectations are estimated using Regression trees or Random forests. We discuss the convergence of the LS algorithm when the standard least squares regression is replaced with regression trees. Finally, we expose some numerical results with regression trees and random forests. The random forest algorithm gives excellent results in high dimensions.
    Keywords: Regression trees,Random forests,Bermudan options,Optimal stopping
    Date: 2021–11–19

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