nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒01‒17
27 papers chosen by

  1. Hedging Cryptocurrency Options By Matic, Jovanka Lili; Packham, Natalie; Härdle, Wolfgang Karl
  2. A financial risk meter for China By Wang, Ruting; Althof, Michael; Härdle, Wolfgang
  3. Mean-Covariance Robust Risk Measurement By Viet-Anh Nguyen; Soroosh Shafieezadeh Abadeh; Damir Filipović; Daniel Kuhn
  4. CREWS: a CAMELS-based early warning system of systemic risk in the banking sector By Jorge E. Galán
  5. Revisiting the link between systemic risk and competition based on network theory and interbank exposures By Enrique Bátiz-Zuk; José Luis Lara Sánchez
  6. Mesoscopic Structure of the Stock Market and Portfolio Optimization By Sebastiano Michele Zema; Giorgio Fagiolo; Tiziano Squartini; Diego Garlaschelli
  7. Sparse and Stable International Portfolio Optimization and Currency Risk Management By Raphael Burkhardt; Urban Ulrych
  8. Estimation of inter-sector asset correlations By Christian Meyer
  9. Stochastic measure distortions induced by quantile processes for risk quantification and valuation By Holly Brannelly; Andrea Macrina; Gareth W. Peters
  10. Measuring counterparty risk in FMIs By Laine, Tatu; Korpinen, Kasperi
  11. Model Risk in Credit Portfolio Models By Christian Meyer
  12. Portfolio Diversification across U.S. Gateway and Non-Gateway Real Estate Markets By Martin Hoesli; Louis Johner
  13. Volatility and Dependence Models with Applications to U.S. Equity Markets By Pan, Jingwei
  14. Regime Switching Entropic Risk Measures on Crude Oil Pricing By Babacar Seck; Robert J. Elliott
  15. Overview of central banks’ in-house credit assessment systems in the euro area By Laura Auria; Markus Bingmer; Carlos Mateo Caicedo Graciano; Clémence Charavel; Sergio Gavilá; Alessandra Iannamorelli; Aviram Levy; Alfredo Maldonado; Florian Resch; Anna Maria Rossi; Stephan Sauer
  16. Reinforcement Learning with Dynamic Convex Risk Measures By Anthony Coache; Sebastian Jaimungal
  17. RISK IN TIME: The Intertwined Nature of Risk Taking and Time Discounting By Thomas Epper; Helga Fehr-Duda
  18. Ensemble methods for credit scoring of Chinese peer-to-peer loans By Wei Cao; Yun He; Wenjun Wang; Weidong Zhu; Yves Demazeau
  19. Precautionary motives with multiple instruments By Christoph Heinzel; Richard Peter
  20. A model of financial bubbles and drawdowns with non-local behavioral self-referencing By Yannick Malevergne; Didier Sornette; Ran Wei
  21. Disaster resilience and asset prices By Pagano, Marco; Wagner, Christian; Zechner, Josef
  22. Portfolio optimization under mean-CVaR simulation with copulas on the Vietnamese stock exchange By Le, Tuan Anh; Dao, Thi Thanh Binh
  23. Machine Learning for Predicting Stock Return Volatility By Damir Filipović; Amir Khalilzadeh
  24. Robust pricing-hedging duality for multi-action options By Anna Aksamit; Ivan Guo; Shidan Liu; Zhou Zhou
  25. An Analysis of Medium-Term Risks to the Public Finances By Conefrey, Thomas; Hickey, Rónán; Walsh, Graeme
  26. Loan guarantees, bank lending and credit risk reallocation By Altavilla, Carlo; Ellul, Andrew; Pagano, Marco; Polo, Andrea; Vlassopoulos, Thomas
  27. Multivariate Realized Volatility Forecasting with Graph Neural Network By Qinkai Chen; Christian-Yann Robert

  1. By: Matic, Jovanka Lili; Packham, Natalie; Härdle, Wolfgang Karl
    Abstract: The cryptocurrency (CC) market is volatile, non-stationary and non-continuous. Together with liquid derivatives markets, this poses a unique opportunity to study risk management, especially the hedging of options, in a turbulent market. We study the hedge behaviour and effectiveness for the class of affine jump diffusion models and infinite activity Lévy processes. First, market data is calibrated to SVI-implied volatility surfaces to price options. To cover a wide range of market dynamics, we generate Monte Carlo price paths using an SVCJ model (stochastic volatility with correlated jumps) assumption and a close-to-actual-market GARCH-filtered kernel density estimation. In these two markets, options are dynamically hedged with Delta, Delta-Gamma, Delta-Vega and Minimum Variance strategies. Including a wide range of market models allows to understand the trade-off in the hedge performance between complete, but overly parsimonious models, and more complex, but incomplete models. The calibration results reveal a strong indication for stochastic volatility, low jump frequency and evidence of infinite activity. Short-dated options are less sensitive to volatility or Gamma hedges. For longer-date options, good tail risk reduction is consistently achieved with multiple-instrument hedges. This is persistently accomplished with complete market models with stochastic volatility.
    Keywords: Cryptocurrency options, hedging, bitcoin, digital finance, volatile markets
    JEL: G12
    Date: 2021–11–20
  2. By: Wang, Ruting; Althof, Michael; Härdle, Wolfgang
    Abstract: This paper develops a new risk meter specifically for China - FRM@China - to detect systemic financial risk as well as tail-event (TE) dependencies among major financial institutions (FIs). Compared with the CBOE FIX VIX, which is currently the most popular financial risk measure, FRM@China has less noise. It also emitted a risk signature much earlier than the CBOE FIX VIX index in the 2020 COVID pandemic. In addition, FRM@China uses a single quantile-lasso regression model to allow both the assessment of risk transfer between different sectors in which FIs operate and the prediction of systemic risk. Because the risk indicator in FRM@China is based on penalization terms, its relationship with macro variables are unknown and non-linear. This paper further expands the existing FRM approach by using Shapley values to identify the dynamic contribution of different macro features in this type of "black box" situation. The results show that short-term interest rates and forward guidance are significant risk drivers. This paper considers the interaction among FIs from mainland China, Hong Kong and Taiwan to provide an enhanced regional tool set for regulators to evaluate financial policy responses. All quantlets are available on
    Keywords: FRM (Financial Risk Meter),Lasso Quantile Regression,Financial Network,China,Shapley value
    JEL: C30 C58 G11 G15 G21
    Date: 2021
  3. By: Viet-Anh Nguyen (Ecole Polytechnique Federale de Lausanne - MTEI); Soroosh Shafieezadeh Abadeh (Carnegie Mellon University - David A. Tepper School of Business); Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Daniel Kuhn (École polytechnique fédérale de Lausanne)
    Abstract: We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization.We model uncertainty in terms of the Gelbrich distance on the mean-covariance space, along with prior structural information about the population distribution. Our approach is related to the theory of optimal transport and exhibits superior statistical and computational properties than existing models. We find that, for a large class of risk measures, mean-covariance robust portfolio optimization boils down to the Markowitz model, subject to a regularization term given in closed form. This includes the finance standards, value-at-risk and conditional value-at-risk, and can be solved highly efficiently.
    Keywords: Robust optimization, risk measurement, optimal transport
    Date: 2021–12
  4. By: Jorge E. Galán (Banco de España)
    Abstract: This document proposes an aggregate early-warning indicator of systemic risk in the banking sector. The indicator is derived from a logistic model based on the variables in the CAMELS rating system, originally developed for the US, and complemented with macroeconomic aggregate variables. The model is applied to the Spanish banking sector using bank-level data for a complete financial cycle, from 1999 to 2021. The performance of the model is assessed not only during the last global financial crisis and the subsequent sovereign crisis, but also during the recent Covid-19 shock. The proposed indicator has a macroprudential orientation, which differs from most of previous studies predicting individual bank defaults. The indicator is found to provide accurate early-warning signals of systemic risk in the banking sector within a two-year horizon. In this context, the indicator provides mid-term signals of systemic risk that complement those derived from macrofinancial indicators and from measures of the materialization of risk.
    Keywords: banks, defaults, early-warning performance, macroprudential policy, systemic risk
    JEL: C25 E32 E58 G01 G21
    Date: 2021–11
  5. By: Enrique Bátiz-Zuk; José Luis Lara Sánchez
    Abstract: This paper examines the link between bank competition measures and risk indicators using quarterly interbank exposures data for all banks in Mexico during 2008Q1-2019Q1. The classical literature focuses on disentangling the link between competition and individual bank solvency risk. In this paper, we take one step forward in analyzing the relationship between competition and systemic risk. We use counterfactual bank-level contagion risk indicators as a proxy of systemic risk to assess their relationship with traditional competition measures. Our main finding indicates a negative relationship between the bank-level Lerner index and systemic risk. This means that an increase in competition is associated with an increase in systemic risk. Additionally, we find that the implementation of regulatory reform during the period studied does not affect this relationship.
    JEL: C23 D40 G21 G28 L14 L16 L22
    Date: 2021–12
  6. By: Sebastiano Michele Zema; Giorgio Fagiolo; Tiziano Squartini; Diego Garlaschelli
    Abstract: The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic `equally-weighted' portfolio. In this paper, we exploit clustering techniques derived from Random Matrix Theory (RMT) to study a third, intermediate (mesoscopic) market structure that turns out to be the most stable over time and provides important practical insights from a portfolio management perspective. First, we illustrate the benefits, in terms of predicted and realized risk profiles, of constructing portfolios by filtering out both random and systemic co-movements from the correlation matrix. Second, we redefine the portfolio optimization problem in terms of stock clusters that emerge after filtering. Finally, we propose a new wealth allocation scheme that attaches equal importance to stocks belonging to the same community and show that it further increases the reliability of the constructed portfolios. Results are robust across different time spans, cross-sectional dimensions and set of constraints defining the optimization problem
    Date: 2021–12
  7. By: Raphael Burkhardt (University of Zurich - Department of Banking and Finance); Urban Ulrych (University of Zurich - Department of Banking and Finance; Swiss Finance Institute)
    Abstract: This paper introduces a sparse and stable optimization approach for a multi-currency asset allocation problem. We study the benefits of joint optimization of assets and currencies as opposed to the standard industry practice of managing currency risk via so-called currency overlay strategies. In our setting, a classical mean-variance problem in an international framework is augmented by several extensions that aim at reducing parameter uncertainty related to the input parameters and induce sparsity and stability of the asset and currency weights. These extensions integrate maximal net exposure to foreign currencies, shrinkage of the input parameters, and constraints on the norms of the asset- and currency-weight vectors. The empirical performance of the portfolio optimization strategies based on the proposed regularization techniques and the joint (i.e., asset and currency) optimization is tested out of sample. We demonstrate that the sparse and stable joint optimization approach consistently outperforms the standard currency overlay as well as the equally-weighted and the non-regularized global portfolio benchmarks net of transaction costs. This result shows that the common industry practice of employing currency overlay strategies is suboptimal and can be improved by a joint optimization over assets and currencies.
    Keywords: International Asset Allocation, Currency Risk Management, Currency Overlay, Shrinkage Estimation, Regularization, Mean-Variance Optimization
    JEL: C61 F31 G11 G15
    Date: 2022–01
  8. By: Christian Meyer
    Abstract: Asset correlations are an intuitive and therefore popular way to incorporate event dependence into event risk, e.g., default risk, modeling. In this paper we study the case of estimation of inter-sector asset correlations by separation of cross-sectional dimension and time dimension.
    Date: 2021–11
  9. By: Holly Brannelly; Andrea Macrina; Gareth W. Peters
    Abstract: We develop a novel stochastic valuation and premium calculation principle based on probability measure distortions that are induced by quantile processes in continuous time. Necessary and sufficient conditions are derived under which the quantile processes satisfy first- and second-order stochastic dominance. The introduced valuation principle relies on stochastic ordering so that the valuation risk-loading, and thus risk premiums, generated by the measure distortion is an ordered parametric family. The quantile processes are generated by a composite map consisting of a distribution and a quantile function. The distribution function accounts for model risk in relation to the empirical distribution of the risk process, while the quantile function models the response to the risk source as perceived by, e.g., a market agent. This gives rise to a system of subjective probability measures that indexes a stochastic valuation principle susceptible to probability measure distortions. We use the Tukey-$gh$ family of quantile processes driven by Brownian motion in an example that demonstrates stochastic ordering. We consider the conditional expectation under the distorted measure as a member of the time-consistent class of dynamic valuation principles, and extend it to the setting where the driving risk process is multivariate. This requires the introduction of a copula function in the composite map for the construction of quantile processes, which presents another new element in the risk quantification and modelling framework based on probability measure distortions induced by quantile processes.
    Date: 2021–12
  10. By: Laine, Tatu; Korpinen, Kasperi
    Abstract: This paper extends traditional payment system simulation analysis to counterparty liquidity risk exposures. The used stress test scenario corresponds to the counterparty stress scenario applied in the BCBS standard "Monitoring tools for intraday liquidity management" (BIS, 2013). This stress scenario is simulated for participants of the Finnish TARGET2 component with the new BoF-PSS3 simulator. Two liquidity deterioration indicators are introduced to quantify counterparty liquidity risk exposures. As comparison of liquidity risk projections to the available liquidity of participants in the system only yields a restricted and system-specific view of the severity of the scenarios, we compare the liquidity risks to high-quality liquid assets (HQLA) available at the group level to assess the overall liquidity risk that participants face in TARGET2. Our results generally comport with the literature and results reported elsewhere. Banking groups are exposed to a liquidity deterioration equivalent from 20 % to 60 % of their respective HQLA in just 0.35 % of the daily scenario observations. The exercise paper demonstrates that our proposed alternative form of payment system analysis can be helpful in banking supervision, micro- and macroprudential analysis, as well as resolution authorities' assessment of the effects of their actions on payment systems.
    Keywords: payment systems,stress testing,liquidity risks,counterparty risks,systemic risk,computer simulation
    Date: 2021
  11. By: Christian Meyer
    Abstract: Model risk in credit portfolio models is a serious issue for banks but has so far not been tackled comprehensively. We will demonstrate how to deal with uncertainty in all model parameters in an all-embracing, yet easy-to-implement way.
    Date: 2021–11
  12. By: Martin Hoesli (University of Geneva - Geneva School of Economics and Management (GSEM); Swiss Finance Institute; University of Aberdeen - Business School); Louis Johner (University of Geneva - Geneva School of Economics and Management)
    Abstract: Using simulation analysis and property-level data for the U.S., we compare performance metrics for portfolios containing varying proportions of gateway and non-gateway markets. Risk-adjusted performance is found to be similar across types of markets. Gateway markets have higher appreciation and total returns, while non-gateway markets exhibit higher income returns even after accounting for capital expenditures. Downside risk appears to be slightly greater for gateway markets than for non-gateway markets; however, full drawdown and recovery lengths tend to be shorter for gateway markets. Systematic risk is found to be constant across types of markets. We show that discriminating between gateway and non-gateway markets is useful for mixed-asset diversification purposes, with the former type of markets appearing in risky portfolios and the latter in low-risk portfolios. By considering a large spectrum of performance metrics in a realistic investment setting, the results should provide investors with valuable information when allocating funds across gateway and non-gateway markets. The paper also provides insights regarding how best to define gateway markets.
    Keywords: Commercial Real Estate; Gateway Markets; Non-Gateway Markets; Diversification; Risk-Adjusted Performance; Downside Risk
    JEL: R33 C63 G11 G23
    Date: 2021–12
  13. By: Pan, Jingwei
    Abstract: The dissertation consists of three studies concerning the research fields of evaluating volatility and correlation forecasts as well as modeling of tail dependence. Based on theoretical discussions and empirical studies the methods for modeling the time-varying volatilities and dependence for the financial market data are evaluated. The first study evaluates the volatility forecasts with the basic generalized conditional autoregressive heteroskedasticity (GARCH) model and its asymmetric extensions. The concepts of loss function and model confidence set (MCS) are introduced. The realized volatility is used as benchmark. The main results of Brownlees et al. (2011) can be confirmed and extended. In particular, the one-step forecasts achieve significantly lower average losses than the multi-step forecasts in times of crises. The difference between the one-step and the multi-step forecasts in pre-crisis times is relatively small. The evaluation results demonstrate the strong forecasting performance of the asymmetric model variants. The second study evaluates the multivariate correlation forecasts. The Baba-Engle-Kraft-Kroner (BEKK) model of Engle and Kroner (1995) is compared with the dynamic conditional correlation (DCC) model of Engle (2002). Using a two-stage estimation method, the DCC model is well suited for large correlation matrices. In contrast, the more flexible BEKK model suffers from the curse of dimensionality. The evaluation is based on the class of asymmetric loss functions proposed by Komunjer and Owyang (2012). The results show that the BEKK model cannot better predict the correlations than the simpler DCC model in the trivariate system. Therefore, the application of the DCC model appears to be superior. The third study leads to a flexible approach which separates the univariate marginal distributions from the joint distribution. The different copula functions are presented and the corresponding tail dependence is calculated. The empirical analysis compares different copula functions with a non-parametric approach and three time-dependent approaches. The results show noticeable reactions of tail dependence to the major financial market events. In addition, the lower tail dependence dominates over time. This can be interpreted in a way that joint losses occur more frequently than joint gains.
    Date: 2021
  14. By: Babacar Seck; Robert J. Elliott
    Abstract: This paper introduces a new type of risk measures, namely regime switching entropic risk measures, and study their applicability through simulations. The state of the economy is incorporated into the entropic risk formulation by using a Markov chain. Closed formulae of the risk measure are obtained for futures on crude oil derivatives. The applicability of these new types of risk measures is based on the study of the risk aversion parameter and the convenience yield. The numerical results show a term structure and a mean-reverting behavior of the convenience yield.
    Date: 2021–12
  15. By: Laura Auria (Deutsche Bundesbank); Markus Bingmer (Deutsche Bundesbank); Carlos Mateo Caicedo Graciano (Banque de France); Clémence Charavel (Banque de France); Sergio Gavilá (Banco de España); Alessandra Iannamorelli (Banca d’Italia); Aviram Levy (Banca d’Italia); Alfredo Maldonado (Banco de España); Florian Resch (Oesterreichische Nationalbank); Anna Maria Rossi (Banca d’Italia); Stephan Sauer (European Central Bank)
    Abstract: The in-house credit assessment systems (ICASs) developed by euro area national central banks (NCBs) are an important source of credit risk assessment within the Eurosystem collateral framework. They allow counterparties to mobilise as collateral the loans (credit claims) granted to non-financial corporations (NFCs). In this way, ICASs increase the usability of non-marketable credit claims that are normally not accepted as collateral in private market repo transactions, especially for small and medium-sized banks that lend primarily to small and medium-sized enterprises (SMEs). This ultimately leads not only to a widened collateral base and an improved transmission mechanism of monetary policy, but also to a lower reliance on external sources of credit risk assessment such as rating agencies. The importance of ICASs is exemplified by the collateral easing measures adopted in April 2020 in response to the coronavirus (COVID-19) crisis. The measures supported the greater use of credit claim collateral and, indirectly, increased the prevalence of ICASs as a source of collateral assessment. This paper analyses in detail the role of ICASs in the context of the Eurosystem’s credit operations, describing the relevant Eurosystem guidelines and requirements in terms of, among other factors, the estimation of default probabilities, the role of statistical models versus expert analysis, input data, validation analysis and performance monitoring. It then presents the main features of each of the ICASs currently accepted by the Eurosystem as credit assessment systems, highlighting similarities and differences.
    Keywords: credit assessments, credit risk models, credit claims, ratings, ICAS
    JEL: E58
    Date: 2021–11
  16. By: Anthony Coache; Sebastian Jaimungal
    Abstract: We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time-consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules. We further develop an actor-critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to optimization problems in statistical arbitrage trading and obstacle avoidance robot control.
    Date: 2021–12
  17. By: Thomas Epper (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique, CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux]); Helga Fehr-Duda (University of Zurich, Department of Banking and Finance)
    Abstract: Standard economic models view risk taking and time discounting as two independent dimensions of decision making. However, mounting experimental evidence demonstrates striking parallels in patterns of risk taking and time discounting behavior and systematic interaction effects, which suggests that there may be common underlying forces driving these interactions. Here we show that the inherent uncertainty associated with future prospects together with individuals' proneness to probability weighting generates a unifying framework for explaining a large number of puzzling behavioral regularities: delay-dependent risk tolerance, aversion to sequential resolution of uncertainty, preferences for the timing of the resolution of uncertainty, the differential discounting of risky and certain outcomes, hyperbolic discounting, subadditive discounting, and the order dependence of prospect valuation. Furthermore, all these phenomena can be predicted simultaneously with the same set of preference parameters.
    Keywords: risk preferences,time preferences,preference interaction,increasing risk tolerance
    Date: 2021–12–09
  18. By: Wei Cao (HFUT - Hefei University of Technology); Yun He (HFUT - Hefei University of Technology); Wenjun Wang (HFUT - Hefei University of Technology); Weidong Zhu (HFUT - Hefei University of Technology); Yves Demazeau (LIG - Laboratoire d'Informatique de Grenoble - 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: Risk control is a central issue for Chinese peer-to-peer (P2P) lending services. Although credit scoring has drawn much research interest and the superiority of ensemble models over single machine learning models has been proven, the question of which ensemble model is the best discrimination method for Chinese P2P lending services has received little attention. This study aims to conduct credit scoring by focusing on a Chinese P2P lending platform and selecting the optimal subset of features in order to find the best overall ensemble model. We propose a hybrid system to achieve these goals. Three feature selection algorithms are employed and combined to obtain the top 10 features. Six ensemble models with five base classifiers are then used to conduct comparisons after synthetic minority oversampling technique (SMOTE) treatment of the imbalanced data set. A real-world data set of 33 966 loans from the largest lending platform in China (ie, the Renren lending platform) is used to evaluate performance. The results show that the top 10 selected features can greatly improve performance compared with all features, particularly in terms of discriminating "bad" loans from "good" loans. Moreover, comparing the standard
    Keywords: credit scoring,ensemble learning,feature selection,synthetic minority oversampling technique (SMOTE) treatment,Chinese peer-to-peer (P2P) lending
    Date: 2021
  19. By: Christoph Heinzel (SMART-LERECO - Structures et Marché Agricoles, Ressources et Territoires - AGROCAMPUS OUEST - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Richard Peter (University of Iowa)
    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.
    Abstract: En utilisant une approche unifiée, nous montrons comment les choix de précaution de l'épargne, l'auto-protection et l'auto-assurance sont simultanément déterminés par les préférences face au risque et la préférence pour le moment de la résolution d'incertitude. Nous tenons compte des effets de risque d'ordre élevé et considérons l'aversion face au risque, ainsi que le goût pour le risque. Des effets d'interaction substitutifs se produisent si les décideurs se servent de plusieurs instruments à la fois pour répondre à un risque exogène sur le revenu. Nous quantifions de manière numérique les effets de précaution et de substitution. Nous discutons le rôle de l'interaction entre les instruments pour la détermination des paramètres de préférences à partir des motifs de précaution. Les instruments diffèrent de manière substantielle par rapport à la taille du motif de précaution, ainsi que leur susceptibilité à des effets d'interaction. Ces différences affectent le degré auquel les instruments pourront contribuer à l'identification des préférences pour la précaution.
    Keywords: Recursive preferences,Prudence,Precautionary behavior,Interaction effects,Comparative statics,Préférences récursives,Comportement de précaution,Effets d’interaction,Statique comparative
    Date: 2021–12–17
  20. By: Yannick Malevergne (Université Paris I Panthéon-Sorbonne - Laboratoire PRISM; Labex ReFi); Didier Sornette (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Swiss Finance Institute; Southern University of Science and Technology; Tokyo Institute of Technology); Ran Wei (ETH Zurich)
    Abstract: We propose a novel class of models in which the crash hazard rate is determined by a function of a non-local estimation of mispricing. Rooted in behavioral finance, the non-local estimation embodies in particular the characteristic of "anchoring" on past price levels and the "probability judgment" about the likelihood of a crash as a function of the self-referential mispricing, enabling us to disentangle the risk-return relationship from its instantaneous connection. By describing drawdowns and crashes as market regimes with correlated negative jumps clustering over a finite period of time, our model provides a solution to the problem plaguing most crash jump models, which are in general rejected in calibrations of real financial time series because they assume that crashes occur in a single large negative jump, which is counterfactual. The model estimation is implemented on synthetic time series and real markets, shedding light on the estimation of the "true" expected return, which is usually confounded by the entanglement between volatility and jump risks. Estimated from the daily time series of three stock indexes, the hidden expected return exhibits a secular increase over time and tends to be larger than the realized return, suggesting that financial markets have been overall underpriced.
    Keywords: financial markets, bubbles, mispricing, faster-than-exponential growth, drawdowns, crashes, behavioral price anchoring, expected return
    JEL: C40 C51 G01 G17
    Date: 2021–12
  21. By: Pagano, Marco; Wagner, Christian; Zechner, Josef
    Abstract: Using the pandemic as a laboratory, we show that asset markets assign a time- varying price to firms' disaster risk exposure. In 2020 the cross-section of realized and expected stock returns reflected firms' different exposure to the pandemic, as measured by their vulnerability to social distancing. Realized and expected return differentials initially widened and then narrowed, but disaster exposure still commanded a risk premium in December 2020. When inferred from market outcomes, resilience correlates not only with social distancing, but also with cash and environmental ratings. However, vulnerability to social distancing is the only characteristic that identifies persistently scarred firms.
    Keywords: asset pricing,rare disasters,social distance,resilience,pandemics
    JEL: G01 G11 G12 G13 G14 Q51 Q54
    Date: 2021
  22. By: Le, Tuan Anh; Dao, Thi Thanh Binh
    Abstract: This paper studies how to construct and compare various optimal portfolio frame-works for investors in the context of the Vietnamese stock market. The aim of the study is to help investors to find solutions for constructing an optimal portfolio strategy using modern investment frameworks in the Vietnamese stock market. The study contains a census of the top 43 companies listed on the Ho Chi Minh stock exchange (HOSE) over the ten-year period from July 2010 to January 2021. Optimal portfolios are constructed using Mean-Variance Framework, Mean-CVaR Framework under different copula simulations. Two-thirds of the data from 26/03/2014 to 27/1/2021 consists of the data of Vietnamese stocks during the COVID-19 recession, which caused depression globally; however, the results obtained during this period still provide a consistent outcome with the results for other periods. Furthermore, by randomly attempting different stocks in the research sample, the results also perform the same outcome as previous analyses. At about the same CvaR level of about 2.1%, for example, the Gaussian copula portfolio has daily Mean Return of 0.121%, the t copula portfolio has 0.12% Mean Return, while Mean-CvaR with the Raw Return portfolio has a lower Return at 0.103%, and the last portfolio of Mean-Variance with Raw Return has 0.102% Mean Return. Empirical results for all 10 portfolio levels showed that CVaR copula simulations significantly outperform the historical Mean-CVaR framework and Mean-Variance framework in the context of the Vietnamese stock exchange.
    Keywords: Gaussian copula, t copula, simulation, Mean-CVaR, Mean-Variance, portfolio optimization, Vietnam
    JEL: C61 G11 G17
    Date: 2021
  23. By: Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Amir Khalilzadeh (Ecole Polytechnique Fédérale de Lausanne)
    Abstract: We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as double the ones reported in the literature. We further show that machine learning methods can capture the stylized facts about volatility without relying on any assumption about the distribution of stock returns. Finally, we show that our long short-term memory model outperforms other models by properly carrying information from the past predictor values.
    Keywords: Volatility Prediction, Volatility Clustering, LSTM, Neural Networks, Regression Trees.
    JEL: C51 C52 C53 C58 G17
    Date: 2021–12
  24. By: Anna Aksamit; Ivan Guo; Shidan Liu; Zhou Zhou
    Abstract: We aim to generalize the duality results of arXiv:1604.05517v2 to the case of exotic options that allow the buyer to choose some action from an action space, countable or uncountable, at each time step in the setup of arXiv:1305.6008v3. By introducing an enlarged canonical space, we reformulate the superhedging problem for such exotic options as a problem for European options. Then in a discrete time market with the presence of finitely many statically traded liquid options, we prove the pricing-hedging duality for such exotic options as well as the European pricing-hedging duality in the enlarged space.
    Date: 2021–11
  25. By: Conefrey, Thomas (Central Bank of Ireland); Hickey, Rónán (Central Bank of Ireland); Walsh, Graeme (Central Bank of Ireland)
    Abstract: As the COVID-19 pandemic abates, pressure on the public finances in Ireland should ease but projected strong growth in public spending will see persistent deficits and high government debt through to 2025. At the same time, there is uncertainty as to the scale of future long-term spending pressures combined with the risk of lower government revenue from corporation tax. Against this backdrop, this Letter examines risks to the public finances from further debt-funded expenditure increases, lower tax revenue or a negative external growth shock. The analysis suggests that permanent increases in current spending should be balanced with revenue-raising measures elsewhere in the budget. A permanent loss of corporation tax combined with a negative external shock could increase government debt to over 115 per cent of modified national income (GNI*) by 2025.
    Date: 2021–09
  26. By: Altavilla, Carlo; Ellul, Andrew; Pagano, Marco; Polo, Andrea; Vlassopoulos, Thomas
    Abstract: We investigate whether government credit guarantee schemes, extensively used at the onset of the Covid-19 pandemic, led to substitution of non-guaranteed with guaranteed credit rather than fully adding to the supply of lending. We study this issue using a unique euro-area credit register data, matched with supervisory bank data, and establish two main findings. First, guaranteed loans were mostly extended to small but comparatively creditworthy firms in sectors severely affected by the pandemic, borrowing from large, liquid and well-capitalized banks. Second, guaranteed loans partially substitute pre-existing non-guaranteed debt. For firms borrowing from multiple banks, the substitution mainly arises from the lending behavior of the bank extending guaranteed loans. Substitution was highest for funding granted to riskier and smaller firms in sectors more affected by the pandemic, and borrowing from larger and stronger banks. Overall, the evidence indicates that government guarantees contributed to the continued extension of credit to relatively creditworthy firms hit by the pandemic, but also benefited banks' balance sheets to some extent.
    Keywords: loan guarantees,bank lending,COVID-19 pandemic,substitution,credit risk
    JEL: G18 G21 E63 H12 H81
    Date: 2021
  27. By: Qinkai Chen; Christian-Yann Robert
    Abstract: The existing publications demonstrate that the limit order book data is useful in predicting short-term volatility in stock markets. Since stocks are not independent, changes on one stock can also impact other related stocks. In this paper, we are interested in forecasting short-term realized volatility in a multivariate approach based on limit order book data and relational data. To achieve this goal, we introduce Graph Transformer Network for Volatility Forecasting. The model allows to combine limit order book features and an unlimited number of temporal and cross-sectional relations from different sources. Through experiments based on about 500 stocks from S&P 500 index, we find a better performance for our model than for other benchmarks.
    Date: 2021–12

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.