nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒09‒14
thirty-one papers chosen by

  1. Optimizing tail risks using an importance sampling based extrapolation for heavy-tailed objectives By Anand Deo; Karthyek Murthy
  2. Measuring and Managing Carbon Risk in Investment Portfolios By Th\'eo Roncalli; Th\'eo Le Guenedal; Fr\'ed\'eric Lepetit; Thierry Roncalli; Takaya Sekine
  3. Applications of generalized structural equation modeling for enhanced credit risk management By Jose Canals-Cerda
  4. Cryptocurrencies: A Copula Based Approach for Asymmetric Risk Marginal Allocations By Vahidin Jeleskovic; Mirko Meloni; Zahid Irshad Younas
  5. Partially Censored Posterior for robust and efficient risk evaluation By Agnieszka Borowska; Lennart Hoogerheide; Siem Jan Koopman; Herman K. van Dijk
  6. The time function of stock price By Shengfeng Mei; Hong Gao
  7. Risk Measures Estimation Under Wasserstein Barycenter By M. Andrea Arias-Serna; Jean-Michel Loubes; Francisco J. Caro-Lopera
  8. Analytical scores for stress scenarios By Pierre Cohort; Jacopo Corbetta; Ismail Laachir
  9. Pricing and Capital Allocation for Multiline Insurance Firms With Finite Assets in an Imperfect Market By John A. Major; Stephen J. Mildenhall
  10. Value-at-risk — the comparison of state-of-the-art models on various assets By Karol Kielak; Robert Ślepaczuk
  11. Liquidity at risk: Joint stress testing of solvency and liquidity By Rama Cont; Artur Kotlicki; Laura Valderrama
  12. Cálculo y evaluación del riesgo operativo en entidades de salud a partir del enfoque de redes bayesianas By Paola Andrea Vaca González
  13. A Bivariate Compound Dynamic Contagion Process for Cyber Insurance By Jiwook Jang; Rosy Oh
  14. Computation of bonus in multi-state life insurance By Jamaal Ahmad; Kristian Buchardt; Christian Furrer
  15. Understanding Gambling Behavior and Risk Attitudes Using Cryptocurrency-based Casino Blockchain Data By Jonathan Meng; Feng Fu
  16. Portfolio Optimization of 60 Stocks Using Classical and Quantum Algorithms By Jeffrey Cohen; Alex Khan; Clark Alexander
  17. A new framework of analysis of Political Risk in OECD Countries By syed, irfan
  18. Finland; Selected Issues By International Monetary Fund
  19. European Banks and the Covid-19 Crash Test By Jézabel Couppey-Soubeyran; Erica Perego; Fabien Tripier
  20. Connected funds By Fricke, Daniel; Wilke, Hannes
  21. Note on simulation pricing of $\pi$-options By Zbigniew Palmowski; Tomasz Serafin
  22. Capital flows-at-risk: push, pull and the role of policy By Eguren-Martin, Fernando; O'Neill, Cian; Sokol, Andrej; von dem Berge, Lukas
  23. Calm before the storm: an early warning approach before and during the COVID-19 crisis By Islam, Raisul; Volkov, Vladimir
  24. Procyclical asset management and bond risk premia By Barbu, Alexandru; Fricke, Christoph; Mönch, Emanuel
  25. SynthETIC: an individual insurance claim simulator with feature control By Benjamin Avanzi; Gregory Clive Taylor; Melantha Wang; Bernard Wong
  26. Risk Aversion and Optimal Hedge Ratio in Commodities Futures Markets By Willy Kamdem; Willy Domtchueng Kamdem; David Kamdem; Louis Aimé Fono
  27. The Effect of Managers on Systematic Risk By Antoinette Schoar; Kelvin Yeung; Luo Zuo
  28. Survival Pessimism and the Demand for Annuities By Cormac O'Dea; David Sturrock
  29. Uncertainty and Monetary Policy during Extreme Events By Giovanni Pellegrino; Efrem Castelnuovo; Giovanni Caggiano
  30. Numerical Scheme for Game Options in Local Volatility models By Benjamin Gottesman Berdah
  31. When it Rains it Pours: Cascading Uncertainty Shocks By Anthony M. Diercks; Alex Hsu; Andrea Tamoni

  1. By: Anand Deo; Karthyek Murthy
    Abstract: Motivated by the prominence of Conditional Value-at-Risk (CVaR) as a measure for tail risk in settings affected by uncertainty, we develop a new formula for approximating CVaR based optimization objectives and their gradients from limited samples. A key difficulty that limits the widespread practical use of these optimization formulations is the large amount of data required by the state-of-the-art sample average approximation schemes to approximate the CVaR objective with high fidelity. Unlike the state-of-the-art sample average approximations which require impractically large amounts of data in tail probability regions, the proposed approximation scheme exploits the self-similarity of heavy-tailed distributions to extrapolate data from suitable lower quantiles. The resulting approximations are shown to be statistically consistent and are amenable for optimization by means of conventional gradient descent. The approximation is guided by means of a systematic importance-sampling scheme whose asymptotic variance reduction properties are rigorously examined. Numerical experiments demonstrate the superiority of the proposed approximations and the ease of implementation points to the versatility of settings to which the approximation scheme can be applied.
    Date: 2020–08
  2. By: Th\'eo Roncalli; Th\'eo Le Guenedal; Fr\'ed\'eric Lepetit; Thierry Roncalli; Takaya Sekine
    Abstract: This article studies the impact of carbon risk on stock pricing. To address this, we consider the seminal approach of G\"orgen \textsl{et al.} (2019), who proposed estimating the carbon financial risk of equities by their carbon beta. To achieve this, the primary task is to develop a brown-minus-green (or BMG) risk factor, similar to Fama and French (1992). Secondly, we must estimate the carbon beta using a multi-factor model. While G\"orgen \textsl{et al.} (2019) considered that the carbon beta is constant, we propose a time-varying estimation model to assess the dynamics of the carbon risk. Moreover, we test several specifications of the BMG factor to understand which climate change-related dimensions are priced in by the stock market. In the second part of the article, we focus on the carbon risk management of investment portfolios. First, we analyze how carbon risk impacts the construction of a minimum variance portfolio. As the goal of this portfolio is to reduce unrewarded financial risks of an investment, incorporating the carbon risk into this approach fulfils this objective. Second, we propose a new framework for building enhanced index portfolios with a lower exposure to carbon risk than capitalization-weighted stock indices. Finally, we explore how carbon sensitivities can improve the robustness of factor investing portfolios.
    Date: 2020–08
  3. By: Jose Canals-Cerda (Federal Reserve Bank of Philadelphia)
    Abstract: The integration of the generalized structural equation modeling (GSEM) framework to widely used statistical packages like Stata offers significant opportunities for credit risk management. GSEM techniques bring to bear a modular and all-inclusive approach to statistical model building. We illustrate the “game changing” potential of the GSEM framework with an application to credit risk stress testing and loss forecasting for a representative portfolio of mortgages originated over the past 20 years. Specifically, we analyze a representative dataset of USA mortgage loans originated over the past 20 years that includes detailed loan-level information on monthly loan performance and other relevant loan and borrower characteristics. Our analysis and discussion illustrates how GSEM techniques can significantly impact every aspect of a model-driven risk management framework, from model development, documentation, and validation to model production, as well as to other, perhaps less obvious, aspects of model building like model risk management, enhanced team collaboration, minimization of proliferation of disparate datasets within projects, and the promotion of a holistic and collaborative approach to model building.
    Date: 2020–08–20
  4. By: Vahidin Jeleskovic (University of Kassel); Mirko Meloni (University of Kassel); Zahid Irshad Younas (National University of Sciences and Technology)
    Abstract: Given the increasing interest in cryptocurrencies shown by investors and researchers, and the importance of the potential loss scenarios resulting from investment/trading activities, this research provides market operators with a dynamic overview on the short-term portfolio tail risk contribution of six widely-traded cryptocurrencies. Considering the high volatility dynamics of the cryptocurrency market, realized volatility measures computed from different frames (1m, 5m, 15m, 30m, 1h) are included in the estimation of univariate GARCH models, to be used in combination with copula functions for VaR/ES Monte Carlo simulations. Even if results lack data frequency ordinality in terms of out-of-sample goodness, Bitcoin and Litecoin are generally recognized as the safest and riskiest currency respectively on an equally-weighted framework, reflecting how the contribution to portfolio returns is not representative of the real grade of risk diversification.
    Keywords: cryptocurrency tradiing, tail risk, realized volatility, copula, portfolio optimization.
    JEL: C15 C53 G17
    Date: 2020
  5. By: Agnieszka Borowska (Vrije Universiteit Amsterdam and Tinbergen Institute); Lennart Hoogerheide (Vrije Universiteit Amsterdam and Tinbergen Institute); Siem Jan Koopman (Vrije Universiteit Amsterdam, Tinbergen Institute and CREATES, Aarhus University); Herman K. van Dijk (Tinbergen Institute, Erasmus University Rotterdam and Norges Bank)
    Abstract: A novel approach to inference for a specific region of the predictive distribution is introduced. An important domain of application is accurate prediction of financial risk measures, where the area of interest is the left tail of the predictive density of logreturns. Our proposed approach originates from the Bayesian approach to parameter estimation and time series forecasting, however it is robust in the sense that it provides a more accurate estimation of the predictive density in the region of interest in case of misspecification. The first main contribution of the paper is the novel concept of the Partially Censored Posterior (PCP), where the set of model parameters is partitioned into two subsets: for the first subset of parameters we consider the standard marginal posterior, for the second subset of parameters (that are particularly related to the region of interest) we consider the conditional censored posterior. The censoring means that observations outside the region of interest are censored: for those observations only the probability of being outside the region of interest matters. This quasi-Bayesian approach yields more precise parameter estimation than a fully censored posterior for all parameters, and has more focus on the region of interest than a standard Bayesian approach. The second main contribution is that we introduce two novel methods for computationally efficient simulation: Conditional MitISEM, a Markov chain Monte Carlo method to simulate model parameters from the Partially Censored Posterior, and PCP-QERMit, an Importance Sampling method that is introduced to further decrease the numerical standard errors of the Value-at-Risk and Expected Shortfall estimators. The third main contribution is that we consider the effect of using a timevarying boundary of the region of interest, which may provide more information about the left tail of the distribution of the standardized innovations. Extensive simulation and empirical studies show the ability of the introduced method to outperform standard approaches.
    Keywords: Bayesian inference; censored likelihood; censored posterior; partially censored posterior; misspecification; density forecasting; Markov chain Monte Carlo; importance sampling; mixture of Student’s t; Value-at-Risk; Expected Shortfall.
    Date: 2019–08–09
  6. By: Shengfeng Mei; Hong Gao
    Abstract: This paper tends to define the quantitative relationship between the stock price and time as a time function. Based on the empirical evidence that the log-return of a stock is the series of white noise, a mathematical model of the integral white noise is established to describe the phenomenon of stock price movement. A deductive approach is used to derive the auto-correlation function, displacement formula and power spectral density of the stock price movement, which reveals not only the characteristics and rules of the movement but also the predictability of the stock price. The deductive fundamental is provided for the price analysis, prediction and risk management of portfolio investment.
    Date: 2020–08
  7. By: M. Andrea Arias-Serna; Jean-Michel Loubes; Francisco J. Caro-Lopera
    Abstract: Randomness in financial markets requires modern and robust multivariate models of risk measures. This paper proposes a new approach for modeling multivariate risk measures under Wasserstein barycenters of probability measures supported on location-scatter families. Simple and advanced copulas multivariate Value at Risk models are compared with the derived technique. The performance of the model is also checked in market indices of United States generated by the financial crisis due to COVID-19. The introduced model behaves satisfactory in both common and volatile periods of asset prices, providing realistic VaR forecast in this era of social distancing.
    Date: 2020–08
  8. By: Pierre Cohort; Jacopo Corbetta; Ismail Laachir
    Abstract: In this work, inspired by the Archer-Mouy-Selmi approach, we present two methodologies for scoring the stress test scenarios used by CCPs for sizing their Default Funds. These methodologies can be used by risk managers to compare different sets of scenarios and could be particularly useful when evaluating the relevance of adding new scenarios to a pre-existing set.
    Date: 2020–07
  9. By: John A. Major; Stephen J. Mildenhall
    Abstract: We analyze multiline pricing and capital allocation in equilibrium no-arbitrage markets. Existing theories often assume a perfect complete market, but when pricing is linear, there is no diversification benefit from risk pooling and therefore no role for insurance companies. Instead of a perfect market, we assume a non-additive distortion pricing functional and the principle of equal priority of payments in default. Under these assumptions, we derive a canonical allocation of premium and margin, with properties that merit the name the natural allocation. The natural allocation gives non-negative margins to all independent lines for default-free insurance but can exhibit negative margins for low-risk lines under limited liability. We introduce novel conditional expectation measures of relative risk within a portfolio and use them to derive simple, intuitively appealing expressions for risk margins and capital allocations. We give a unique capital allocation consistent with our law invariant pricing functional. Such allocations produce returns that vary by line, in contrast to many other approaches. Our model provides a bridge between the theoretical perspective that there should be no compensation for bearing diversifiable risk and the empirical observation that more risky lines fetch higher margins relative to subjective expected values.
    Date: 2020–08
  10. By: Karol Kielak (Quantitative Finance Research Group; Faculty of Economic Sciences, University of Warsaw); Robert Ślepaczuk (Quantitative Finance Research Group; Faculty of Economic Sciences, University of Warsaw)
    Abstract: This paper compares different approaches to Value-at-Risk measurement based on parametric and non-parametric approaches. Three portfolios are taken into consideration — the first one containing only stocks from the London Stock Exchange, the second one based on different assets of various origins and the third one consisting of cryptocurrencies. Data used cover the period of more than 20y. In the empirical part of the study, parametric methods based on mean-variance framework are compared with GARCH(1,1) and EGARCH(1,1) models. Different assumptions concerning returns’ distribution are taken into consideration. Adjustment for the fat tails effect is made by using Student t distribution in the analysis. One-day-ahead 95%VaR estimation is then calculated. Thereafter, models are validated using Kupiec and Christoffersen tests and Monte Carlo Simulation for reliable verification of the hypotheses. The overall goal of this paper is to establish if analyzed models accurately estimate Value-at-Risk measure, especially if we take into account assets with various returns distribution characteristics.
    Keywords: risk management, Value-at-Risk, GARCH models, returns distribution, Monte Carlo Simulation, asset class, cryptocurrencies
    JEL: C4 C14 C45 C53 C58 G13
    Date: 2020
  11. By: Rama Cont; Artur Kotlicki; Laura Valderrama
    Abstract: The traditional approach to the stress testing of financial institutions focuses on capital adequacy and solvency. Liquidity stress tests are often applied in parallel to solvency stress tests, based on scenarios which may not be consistent with those used in solvency stress tests. We propose a structural framework for the joint stress testing of solvency and liquidity: our approach exploits the mechanisms underlying the solvency-liquidity nexus to derive relations between solvency shocks and liquidity shocks. These relations are then used to model liquidity and solvency risk in a coherent framework, involving external shocks to solvency and endogenous liquidity shocks. We introduce solvency-liquidity diagrams as a method for analysing the resilience of a balance sheet to the resulting combination of solvency shocks and endogenous liquidity shocks. Finally, we define the concept of 'Liquidity at Risk' which quantifies the liquidity resources required for a financial institution facing a stress scenario.
    Date: 2019–07–02
  12. By: Paola Andrea Vaca González
    Keywords: Riesgo operativo; redes bayesianas; teoría de grafos; entidades de salud; gestión del riesgo; medición del riesgo. Keywords: operational risk; Bayesian networks; graphs theory; health entities; risk management; risk computation.
    JEL: G32 I15 C11 C45
    Date: 2019–07–01
  13. By: Jiwook Jang; Rosy Oh
    Abstract: As corporates and governments become more digital, they become vulnerable to various forms of cyber attack. Cyber insurance products have been used as risk management tools, yet their pricing does not reflect actual risk, including that of multiple, catastrophic and contagious losses. For the modelling of aggregate losses from cyber events, in this paper we introduce a bivariate compound dynamic contagion process, where the bivariate dynamic contagion process is a point process that includes both externally excited joint jumps, which are distributed according to a shot noise Cox process and two separate self-excited jumps, which are distributed according to the branching structure of a Hawkes process with an exponential fertility rate, respectively. We analyse the theoretical distributional properties for these processes systematically, based on the piecewise deterministic Markov process developed by Davis (1984) and the univariate dynamic contagion process theory developed by Dassios and Zhao (2011). The analytic expression of the Laplace transform of the compound process and its moments are presented, which have the potential to be applicable to a variety of problems in credit, insurance, market and other operational risks. As an application of this process, we provide insurance premium calculations based on its moments. Numerical examples show that this compound process can be used for the modelling of aggregate losses from cyber events. We also provide the simulation algorithm for statistical analysis, further business applications and research.
    Date: 2020–06
  14. By: Jamaal Ahmad; Kristian Buchardt; Christian Furrer
    Abstract: We consider computation of market values of bonus payments in multi-state with-profit life insurance. The bonus scheme consists of additional benefits bought according to a dividend strategy that depends on the past realization of financial risk, the current individual insurance risk, the number of additional benefits currently held, and so-called portfolio-wide means describing the shape of the insurance business. We formulate numerical procedures that efficiently combine simulation of financial risk with more analytical methods for the outstanding insurance risk. Special attention is given to the case where the number of additional benefits bought only depends on the financial risk.
    Date: 2020–07
  15. By: Jonathan Meng; Feng Fu
    Abstract: The statistical concept of Gambler's Ruin suggests that gambling has a large amount of risk. Nevertheless, gambling at casinos and gambling on the Internet are both hugely popular activities. In recent years, both prospect theory and lab-controlled experiments have been used to improve our understanding of risk attitudes associated with gambling. Despite theoretical progress, collecting real-life gambling data, which is essential to validate predictions and experimental findings, remains a challenge. To address this issue, we collect publicly available betting data from a \emph{DApp} (decentralized application) on the Ethereum Blockchain, which instantly publishes the outcome of every single bet (consisting of each bet's timestamp, wager, probability of winning, userID, and profit). This online casino is a simple dice game that allows gamblers to tune their own winning probabilities. Thus the dataset is well suited for studying gambling strategies and the complex dynamic of risk attitudes involved in betting decisions. We analyze the dataset through the lens of current probability-theoretic models and discover empirical examples of gambling systems. Our results shed light on understanding the role of risk preferences in human financial behavior and decision-makings beyond gambling.
    Date: 2020–08
  16. By: Jeffrey Cohen; Alex Khan; Clark Alexander
    Abstract: We continue to investigate the use of quantum computers for building an optimal portfolio out of a universe of 60 U.S. listed, liquid equities. Starting from historical market data, we apply our unique problem formulation on the D-Wave Systems Inc. D-Wave 2000Q (TM) quantum annealing system (hereafter called D-Wave) to find the optimal risk vs return portfolio. We approach this first classically, then using the D-Wave, to select efficient buy and hold portfolios. Our results show that practitioners can use either classical or quantum annealing methods to select attractive portfolios. This builds upon our prior work on optimization of 40 stocks.
    Date: 2020–08
  17. By: syed, irfan
    Abstract: The motivation behind this paper is to present another model of investigation that – mulling over current ideas of political risk and present day speculations of globalization – incorporates in a thorough system the more conventional factors of political risk with another transnational variable. Political Risk Analysis (PRA) is the expository control that attempts to make a sensible system of data on the risk profile for undertakings working and putting resources into far off nations. Political Risk Analysis, by its own one of a kind definition, centers around non – commercial risks, that is, risks emerging from the socio – political environment of a given Country. The idea of political risk and the investigation methodology embraced and utilized in PRA are incredibly heterogeneous, fluctuating significantly one case at a time case. Anyway a typical example can be recognized. In pretty much every definition or operational idea of political risk, the spotlight depends as a rule on the interior measurement. The models created by both open and private offices and institutions tend in certainty to put together their models with respect to factors and markers inward to the nation object of the examination. As we would like to think this methodology is restricted. In the present globalized and regularly evolving world, we feel that in any political risk examination model it is major to incorporate a transnational point of view. A transnational variable ought to likewise be made so as to supplement the national variable by weighting the impacts of the universal and worldwide measurement on nearby and national socio-political occasions.
    Keywords: political risk governance institutional quality risk country
    JEL: O5 O52 P5
    Date: 2020–03–03
  18. By: International Monetary Fund
    Abstract: This Selected Issues paper summarizes Nordea’s operations and business model; the macroeconomic and prudential implications of the move; and policy responses taken so far. The IMF staff’s assessment is that banking supervision in the euro area has improved significantly following the creation of the Single Supervisory Mechanism, which should mitigate potential risks from Nordea’s move; meanwhile, the Nordic authorities have done much, in conjunction with the European Central Bank, to ensure that potential gaps and fragmentation across national jurisdictions are avoided. The resolution framework is designed to prevent taxpayers having to bail out banks, but is new, and work on building the crisis preparedness of euro area banks is still under way. The banking union is not yet complete, details of the backstop for the Single Resolution Fund need to be finalized and a common euro area deposit insurance should be made fully operational. At the same time, Nordea is also operating in non-euro area member states—maintaining cooperation between euro area and noneuro area institutions remains important.
    Keywords: Banking sector;Financial institutions;Systemic risk;Risk management;Bank supervision;Summing up;Macroprudential policies and financial stability;Financial services;Financial systems;Central banks;Nordea,credit institution,euro area,ECB,debt security
    Date: 2019–01–15
  19. By: Jézabel Couppey-Soubeyran; Erica Perego; Fabien Tripier
    Abstract: European banks are stronger today than they were on the eve of the 2007-2008 financial crisis, thanks to the reforms that have taken place since then. But will they be strong enough in the face of a health crisis closer to the Great Depression of the 1930s than the stress scenarios envisaged by the European banking Authority for 2020? Access to central bank liquidity probably eliminates the risk of bank illiquidity, but it is not unthinkable that a bank insolvency crisis would have to be managed. The non-repayment of one in five loans would be enough to exhaust the current level of capital. The resolution mechanism would then have to be mobilised, which is unlikely to be sufficient in a context where, according to the European Systemic Risk Board, the risk of simultaneous defaults is increasing sharply. This would leave the possible mobilisation of the European Stability Mechanism. If this complement proves insufficient, a sovereign debt crisis in the euro area could re-emerge.
    Date: 2020
  20. By: Fricke, Daniel; Wilke, Hannes
    Abstract: Investment funds are highly connected with each other, but also with the broader financial system. In this paper, we quantify potential vulnerabilities arising from funds' connectedness. While previous work exclusively focused on indirect connections (overlapping asset portfolios) between investment funds, we develop a macroprudential stress test that also includes direct connections (cross-holdings of fund shares). In our application for German investment funds, we find that these direct connections are very important from a financial stability perspective. Our main result is that the German fund sector's aggregate vulnerability can be substantial and tends to increase over time, suggesting that the fund sector can amplify adverse developments in global security markets. We also highlight spillover risks to the broader financial system, since fund sector losses would be largely borne by fund investors from the financial sector. Overall, we make an important step towards a more financial-system-wide view on fund sector vulnerabilities.
    Keywords: asset management,investment funds,systemic risk,fire sales,liquidity risk,cross-holdings,spillover effects
    JEL: G10 G11 G23
    Date: 2020
  21. By: Zbigniew Palmowski; Tomasz Serafin
    Abstract: In this work, we adapt a Monte Carlo algorithm introduced by Broadie and Glasserman (1997) to price a $\pi$-option. This method is based on the simulated price tree that comes from discretization and replication of possible trajectories of the underlying asset's price. As a result this algorithm produces lower and upper bounds that converge to the true price with the increasing depth of the tree. Under specific parametrization, this $\pi$-option is related to relative maximum drawdown and can be used in the real-market environment to protect a portfolio against volatile and unexpected price drops. We also provide some numerical analysis.
    Date: 2020–07
  22. By: Eguren-Martin, Fernando (Bank of England); O'Neill, Cian (Bank of England); Sokol, Andrej (European Central Bank); von dem Berge, Lukas (Bank of England)
    Abstract: We characterise the probability distribution of capital flows for a panel of emerging market economies conditional on information contained in financial asset prices, with a focus on ‘tail’ events. Our framework, based on the quantile regression methodology, allows for a separate role of push and pull-type factors, and offers insights into the term-structure of these effects. We find that both push and pull factors have heterogeneous effects across the distribution of capital flows, with the strongest reactions in the left tail. Also, the effect of changes in pull factors is more persistent than that of push factors. Finally, we explore the role of policy, and find that macroprudential and capital flow management measures are associated with changes in the distribution of capital flows.
    Keywords: Capital flows; sudden stops; capital flight; retrenchment; capital flow surges; push versus pull; capital controls; macroprudential policy; financial conditions indices; quantile regression
    JEL: F32 F34 G15
    Date: 2020–08–07
  23. By: Islam, Raisul (Tasmanian School of Business & Economics, University of Tasmania); Volkov, Vladimir (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: This paper develops a means of visualising the vulnerability of complex systems of financial interactions resulting from the changing risk tolerance of investors. The investors’ risk behavior contributes to the buildup of vulnerability in crisis and in calm periods. We show how both time-varying risk tolerance and spillover indices can be translated into two-dimensional information transmission and crisis transmission maps, respectively. Taken together, the information transmission maps have the advantage of highlighting potential crisis transmission pathways in the crisis transmission maps. These maps provide clear visualization showing information transmission predates crisis transmission drawing from conditional signed spillover and risk tolerance indices computed from equity market data for 31 global markets between 1998 and 2020. We examine if investors’ risk preference induces a crisis and to what extent such a predictor may be related to a pandemic. Furthermore, we take a special look at the Covid-19 pandemic and its impact on the dynamics of systemic crisis transmission.
    Keywords: Systemic risk, networks, COVID-19
    JEL: C3 C32 C45 C53 D85 G10
    Date: 2020
  24. By: Barbu, Alexandru; Fricke, Christoph; Mönch, Emanuel
    Abstract: Institutional funds have concentrated ownership by a few institutional investors, infrequent outflows and essentially no leverage. Yet using unique granular data on the bond holdings of institutional funds, we show that their trading behavior is strongly procyclical: they actively move into higher yielding, longer duration and lower rated securities in response to lower in-terest rates, and vice versa. Institutional funds' risk-taking increases when interest rates turn negative, particularly in funds with explicit minimum return guarantees. Their trading has large and persistent price impact. We provide evidence that this procyclical behavior is driven by career concerns among institutional fund managers.
    Keywords: institutional funds,institutional accounts,procyclical asset management,portfolio rebalancing,price impact,demand pressures,asset price volatility,career concerns
    JEL: G11 G23 E43
    Date: 2020
  25. By: Benjamin Avanzi; Gregory Clive Taylor; Melantha Wang; Bernard Wong
    Abstract: A simulator of individual claim experience called SynthETIC is described. It is publicly available, open source and fills a gap in the non-life actuarial toolkit. It simulates, for each claim, occurrence, notification, the timing and magnitude of individual partial payments, and closure. Inflation, including (optionally) superimposed inflation, is incorporated in payments. Superimposed inflation may occur over calendar or accident periods. The claim data are summarized by accident and payment "periods" whose duration is an arbitrary choice (e.g. month, quarter, etc.) available to the user. The code is structured as eight modules (occurrence, notification, etc.), any one or more of which may be re-designed according to the user's requirements. The default version is loosely calibrated to resemble a specific (but anonymous) Auto Bodily Injury portfolio, but the general structure is suitable for most lines of business, with some amendment of modules. The structure of the simulator enables the inclusion of a number of important dependencies between the variables related to an individual claim, e.g. dependence of notification delay on claim size, of the size of a partial payment on the sizes of those preceding, etc. The user has full control of the mechanics of the evolution of an individual claim. As a result, the complexity of the data set generated (meaning the level of difficulty of analysis) may be dialled anywhere from extremely simple to extremely complex. At the extremely simple end would be chain-ladder-compatible data, and so the alternative data structures available enable proposed loss reserving models to be tested against more challenging data sets. Indeed, the user may generate a collection of data sets that provide a spectrum of complexity, and the collection may be used to present a model under test with a steadily increasing challenge.
    Date: 2020–08
  26. By: Willy Kamdem (Université de Douala); Willy Domtchueng Kamdem (Université de Douala); David Kamdem (Université de Dschang); Louis Aimé Fono (Université de Douala)
    Abstract: In this paper, our main objective is to show that the determination of the optimal hedge ratio for a raw material producer, who is submitted to income risk, depends on the type of its utility function. More precisely, we maximize the expected utility of wealth for the following four utility functions : quadratic, exponential, power and expo-power. We then derive an explicit formula of the optimal hedge ratio when using the first two functions and an implicit function when the agent's preferences are modeled by power or expo-power utility functions. The results obtained are then applied to data on quantity and prices collected from the NCCB and ICCO from 1980 to 2013. The implementation with some Matlab programs provides the estimated value of the optimal hedge ratio for a Cameroonian cocoa producer around 80% for quadratic and exponential utility functions, and 87.9% for power utility and between 50% and 65% for the expo-power utility function.
    Date: 2020
  27. By: Antoinette Schoar; Kelvin Yeung; Luo Zuo
    Abstract: Tracking the movement of top managers across firms, we document the importance of manager-specific fixed effects in explaining heterogeneity in firm exposures to systematic risk. These differences in systematic risk are partially explained by managers’ corporate strategies, such as their preferences for internal growth and financial conservatism. Managers’ early-career experiences of starting their first job in a recession also contribute to differential loadings on systematic risk. These effects are more pronounced for smaller firms. Overall, our results suggest that managerial styles have important implications for asset prices.
    JEL: G12 G30
    Date: 2020–07
  28. By: Cormac O'Dea; David Sturrock
    Abstract: The "annuity puzzle" refers to the fact that annuities are rarely purchased despite the longevity insurance they provide. Most explanations for this puzzle assume that individuals have accurate expectations about their future survival. We provide evidence that individuals misperceive their mortality risk, and study the demand for annuities in a setting where annuities are priced by insurers on the basis of objectively-measured survival probabilities but in which individuals make purchasing decisions based on their own subjective survival probabilities. Subjective expectations have the capacity to explain significant rates of non-annuitization, yielding a quantitatively important explanation for the annuity puzzle.
    JEL: D14 D84 D91 J14
    Date: 2020–08
  29. By: Giovanni Pellegrino (Department of Economics and Business Economics, Aarhus University); Efrem Castelnuovo (University of Padova and University of Melbourne); Giovanni Caggiano (Monash University and University of Padova)
    Abstract: How damaging are uncertainty shocks during extreme events such as the great recession and the Covid-19 outbreak? Can monetary policy limit output losses in such situations? We use a nonlinear VAR framework to document the large response of real activity to a financial uncertainty shock during the great recession. We replicate this evidence with an estimated DSGE framework featuring a concept of uncertainty comparable to that in our VAR. We employ the DSGE model to quantify the impact on real activity of an uncertainty shock under different Taylor rules estimated with normal times vs. great recession data (the latter associated with a stronger response to output). We find that the uncertainty shock-induced output loss experienced during the 2007-09 recession could have been twice as large if policymakers had not responded aggressively to the abrupt drop in output in 2008Q3. Finally, we use our estimated DSGE framework to simulate different paths of uncertainty associated to different hypothesis on the evolution of the coronavirus pandemic. We find that: i) Covid-19-induced uncertainty could lead to an output loss twice as large as that of the great recession, ii) aggressive monetary policy moves could reduce such loss by about 50%.
    Keywords: Uncertainty shock, nonlinear IVAR, nonlinear DSGE framework, minimum-distance estimation, great recession, Covid-19
    JEL: C22 E32 E52
    Date: 2020–08–31
  30. By: Benjamin Gottesman Berdah
    Abstract: In this paper we introduce a numerical method for optimal stopping in the framework of one dimensional diffusion. We use the Skorokhod embedding in order to construct recombining tree approximations for diffusions with general coefficients. This technique allows us to determine convergence rates and construct nearly optimal stopping times which are optimal at the same rate. Finally, we demonstrate the efficiency of our scheme with several examples of game options.
    Date: 2020–07
  31. By: Anthony M. Diercks; Alex Hsu; Andrea Tamoni
    Abstract: We empirically document that serial uncertainty shocks are (1) common in the data and (2) have an increasingly stronger impact on the macroeconomy. In other words, a series of bad (positive) uncertainty shocks exacerbates the economic decline significantly. From a theoretical perspective, these findings are puzzling: existing benchmark models do not deliver the observed amplification. We show analytically that a state dependent precautionary motive with respect to uncertainty shocks is required. Our derivations suggest that the state dependent precautionary motive only shows up at fourth order approximations or higher. Fundamentally, in DSGE models solved with perturbations, agents have always possessed a state dependent precautionary motive but typical solution methods were hiding this fact. Future studies need to consider solving the model via fourth (or higher) order perturbation in order to avoid understating the effect of uncertainty shocks that occur in succession.
    Keywords: Dynamic Equilibrium Economies; Stochastic Volatility; Perturbation
    JEL: C63 C68 E37
    Date: 2020–08–21

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.