nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒09‒07
38 papers chosen by
Stan Miles
Thompson Rivers University

  1. Bayesian Value-at-Risk Backtesting: The Case of Annuity Pricing By Leung, Melvern; Li, Youwei; Pantelous, Athanasios; Vigne, Samuel
  2. Republic of Armenia; Detailed Assessment of Observance of the Basel Core Principles for Effective Banking Supervision By International Monetary Fund
  3. Increasing systemic risk during the Covid-19 pandemic: A cross-quantilogram analysis of the banking sector By Baumöhl, Eduard; Bouri, Elie; Hoang, Thi-Hong-Van; Shahzad, Syed Jawad Hussain; Výrost, Tomáš
  4. The Risk of Being a Fallen Angel and the Corporate Dash for Cash in the Midst of COVID By Viral V. Acharya; Sascha Steffen
  5. Australia; Financial Sector Assessment Program-Detailed Assessment of Observance-Basel Core Principles For Effective Banking Supervision By International Monetary Fund
  6. Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks By Edmond Lezmi; Jules Roche; Thierry Roncalli; Jiali Xu
  7. Robust pricing and hedging via neural SDEs By Patryk Gierjatowicz; Marc Sabate-Vidales; David \v{S}i\v{s}ka; Lukasz Szpruch; \v{Z}an \v{Z}uri\v{c}
  8. A Stochastic Control Approach to Defined Contribution Plan Decumulation: "The Nastiest, Hardest Problem in Finance" By Peter A. Forsyth
  9. Expected Credit Loss Modeling from a Top-Down Stress Testing Perspective By Marco Gross; Dimitrios Laliotis; Mindaugas Leika; Pavel Lukyantsau
  10. Variance Contracts By Yichun Chi; Xun Yu Zhou; Sheng Chao Zhuang
  11. Omega and Sharpe ratio By Eric Benhamou; Beatrice Guez; Nicolas Paris
  12. Risk Modelling on Liquidations with L\'{e}vy Processes By Aili Zhang; Ping Chen; Shuanming Li; Wenyuan Wang
  13. A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating By Sotirios Sabanis; Ying Zhang
  14. Análisis de la metodología Enterprise Risk Management (ERM) y su aplicación para la optimización de la relación riesgo/retorno en la administración de las reservas internacionales By Varinia Tindal; Denise Salazar
  15. Vulnerable growth in the Euro Area: Measuring the financial conditions By Figueres, Juan Manuel; Jarociński, Marek
  16. Systemic Risk: a Network Approach By Jean-Baptiste Hasse
  17. Approximate Bayesian Computations to fit and compare insurance loss models By Pierre-Olivier Goffard; Patrick Laub
  18. Capital Flows in Risky Times: Risk-On / Risk-Off and Emerging Market Tail Risk By Anusha Chari; Karlye Dilts Stedman; Christian T. Lundblad
  19. Testing Sharpe ratio: luck or skill? By Eric Benhamou; David Saltiel; Beatrice Guez; Nicolas Paris
  20. On the harmonic mean representation of the implied volatility By Stefano De Marco
  21. Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network By Eduardo Ramos-P\'erez; Pablo J. Alonso-Gonz\'alez; Jos\'e Javier N\'u\~nez-Vel\'azquez
  22. Systematic Liquidity Risk Premia By Glenn Boyle; Sanghyun Hong
  23. A Natural Actor-Critic Algorithm with Downside Risk Constraints By Thomas Spooner; Rahul Savani
  24. Culture and portfolios: trust, precautionary savings and home ownership By Fleck, Johannes; Monninger, Adrian
  25. Size-biased risk measures of compound sums By Denuit, Michel
  26. Infectious Disease-Related Uncertainty and the Safe-Haven Characteristic of US Treasury Securities By Rangan Gupta; Sowmya Subramaniam; Elie Bouri; Qiang Ji
  27. Large Bets and Stock Market Crashes By Albert S. Kyle; Anna A. Obizhaeva
  28. How Risky is Australian Household Debt? By Jonathan Kearns; Mike Major; David Norman
  29. COVID-19 and financial markets: Assessing the impact of the coronavirus on the eurozone By D'Orazio, Paola; Dirks, Maximilian W.
  30. Internal control and banking risk management: the case of Moroccan banks By Afafe Hertouch; Mustapha Achibane
  31. Small Business Survival Capabilities and Policy Effectiveness: Evidence from Oakland By Robert P. Bartlett III; Adair Morse
  32. Big Data links from Climate to Commodity Production Forecasts and Risk Management By Paulina Concha Larrauri; Upmanu Lall
  33. Property Insurance By Valentina Avramescu
  34. Life-Cycle Welfare Losses from Rules-of-Thumb Asset Allocation By Fabio C. Bagliano; Carolina Fugazza; Giovanna Nicodano
  35. Uncertainty-Aware Lookahead Factor Models for Quantitative Investing By Lakshay Chauhan; John Alberg; Zachary C. Lipton
  36. Corporate pandemic insurance By Pierre Picard
  37. Disaster impacts and financing: Local insights from the Philippines By Arlan Brucal; Viktor Roezer; Denyse S. Dookie; Rebecca Byrnes; Majah-Leah V. Ravago; Faye Cruz; Gemma Narisma
  38. Quantum Pricing with a Smile: Implementation of Local Volatility Model on Quantum Computer By Kazuya Kaneko; Koichi Miyamoto; Naoyuki Takeda; Kazuyoshi Yoshino

  1. By: Leung, Melvern; Li, Youwei; Pantelous, Athanasios; Vigne, Samuel
    Abstract: We propose a new Unconditional Coverage backtest for VaR-forecasts under a Bayesian framework that significantly minimise the direct and indirect effects of p-hacking or other biased outcomes in decision-making, in general. Especially, after the global financial crisis of 2007-09, regulatory demands from Basel III and Solvency II have required a more strict assessment setting for the internal financial risk models. Here, we employ linear and nonlinear Bayesianised variants of two renowned mortality models to put the proposed backtesting technique into the context of annuity pricing. In this regard, we explore whether the stressed longevity scenarios are enough to capture the experienced liability over the foretasted time horizon. Most importantly, we conclude that our Bayesian decision theoretic framework quantitatively produce a strength of evidence favouring one decision over the other.
    Keywords: Bayesian decision theory; Value-at-Risk; Backtesting; Annuity pricing; Longevity risk
    JEL: C11 C12 C44 G13 G17 G22 G23
    Date: 2019–11
  2. By: International Monetary Fund
    Abstract: This detailed assessment of observance has been conducted against the standard issued by the Basel Committee on Banking Supervision in 2012. The report also highlights that the Central Bank of Armenia has made significant progress in its approach to banking supervision with adoption of the risk-based program (RBS) framework and addressing gaps in the regulatory framework identified in the 2012 Basel Core Principles assessment. Improvements have been made in the regulatory regime regarding requirements for risk management, stress testing, corporate governance, country risk and consolidated supervision. Although the supervisory regime has recently transitioned from a rules-based to an RBS, there is a need for continued refinement of the program for more granular assessments of firms’ capital needs. The process for conducting risk assessments of each firm has identified a need for building a stronger and more structured (system-wide) understanding of the level and magnitude of risk and the risk management capabilities across banking firms.
    Keywords: Armenia;Middle East;Bank credit;Financial crises;Central banks;Financial institutions;Financial services;CBA,related party,LBB,bank 's board,LCB
    Date: 2019–02–05
  3. By: Baumöhl, Eduard; Bouri, Elie; Hoang, Thi-Hong-Van; Shahzad, Syed Jawad Hussain; Výrost, Tomáš
    Abstract: Over the last few decades, large banks worldwide have become more interconnected. As a result, the failure of one can trigger the failure of many. In finance, this phenomenon is often known as financial contagion, which can act like a domino effect. In this paper, we show an unprecedented increase in bank interconnectedness during the outbreak of the Covid-19 pandemic. We measure how extreme negative stock market returns from one bank can spill over to the other banks within the network. Our contribution relies on the establishment of a new systemic risk index based on the cross-quantilogram approach of Han et al. (2016). The results indicate that the systemic risk and the density of the spillover network among 83 banks in 24 countries have never been as high as during the Covid-19 pandemic – much higher than during the 2008 global financial crisis. Furthermore, we find that US banks are the most important risk transmitters, and Asian banks are the most important risk receivers. In contrast, European banks were strong risk transmitters during the European sovereign debt crisis. These findings may help investors, portfolio managers and policymakers adapt their investment strategies and macroprudential policies in this context of uncertainty.
    Keywords: Systemic risk,Banks,Covid-19 pandemic,Cross-quantilogram,Financial networks
    JEL: G01 G15 G21 G28 C21
    Date: 2020
  4. By: Viral V. Acharya; Sascha Steffen
    Abstract: Data on firm-loan-level daily credit line drawdowns in the United States expose a corporate “dash for cash” induced by the COVID-19 pandemic. In the first phase of the crisis, which was characterized by extreme precaution and heightened aggregate risk, all firms drew down bank credit lines and raised cash levels. In the second phase, which followed the adoption of stabilization policies, only the highest-rated firms switched to capital markets to raise cash. Consistent with the risk of becoming a fallen angel, the lowest-quality BBB-rated firms behaved more similarly to non-investment grade firms. The observed corporate behavior reveals the significant impact of credit risk on corporate cash holdings.
    JEL: G01 G14 G32 G35
    Date: 2020–07
  5. By: International Monetary Fund
    Abstract: This Detailed Assessment of Observance report specifies Base Core Principles (BCP) for effective banking supervision in Australia. An assessment of the effectiveness of banking supervision requires a review of the legal framework, and a detailed examination of the policies and practices of the institution(s) responsible for banking regulation and supervision. In line with the BCP methodology, the assessment focused on banking supervision and regulation in Australia and did not cover the specificities of regulation and supervision of other financial institutions. The assessment has made use of five categories to determine compliance: compliant; largely compliant, materially noncompliant, noncompliant, and non-applicable. The report insists that Australian Prudential Regulation Authority (APRA) should put more focus on assessing the various components of firms’ Internal Capital Adequacy Assessment Process and other firm-wide stress testing practices. A periodic more comprehensive assessment of banks’ risk management and governance frameworks will further enhance APRA’s supervisory approach.
    Keywords: Bank credit;Central banks;Access to international capital markets;Related party lending;Bank capital;APRA,bank act,Adi,bank 's board,FSSA
    Date: 2019–02–21
  6. By: Edmond Lezmi; Jules Roche; Thierry Roncalli; Jiali Xu
    Abstract: This article explores the use of machine learning models to build a market generator. The underlying idea is to simulate artificial multi-dimensional financial time series, whose statistical properties are the same as those observed in the financial markets. In particular, these synthetic data must preserve the probability distribution of asset returns, the stochastic dependence between the different assets and the autocorrelation across time. The article proposes then a new approach for estimating the probability distribution of backtest statistics. The final objective is to develop a framework for improving the risk management of quantitative investment strategies, in particular in the space of smart beta, factor investing and alternative risk premia.
    Date: 2020–07
  7. By: Patryk Gierjatowicz; Marc Sabate-Vidales; David \v{S}i\v{s}ka; Lukasz Szpruch; \v{Z}an \v{Z}uri\v{c}
    Abstract: Mathematical modelling is ubiquitous in the financial industry and drives key decision processes. Any given model provides only a crude approximation to reality and the risk of using an inadequate model is hard to detect and quantify. By contrast, modern data science techniques are opening the door to more robust and data-driven model selection mechanisms. However, most machine learning models are "black-boxes" as individual parameters do not have meaningful interpretation. The aim of this paper is to combine the above approaches achieving the best of both worlds. Combining neural networks with risk models based on classical stochastic differential equations (SDEs), we find robust bounds for prices of derivatives and the corresponding hedging strategies while incorporating relevant market data. The resulting model called neural SDE is an instantiation of generative models and is closely linked with the theory of causal optimal transport. Neural SDEs allow consistent calibration under both the risk-neutral and the real-world measures. Thus the model can be used to simulate market scenarios needed for assessing risk profiles and hedging strategies. We develop and analyse novel algorithms needed for efficient use of neural SDEs. We validate our approach with numerical experiments using both local and stochastic volatility models.
    Date: 2020–07
  8. By: Peter A. Forsyth
    Abstract: We pose the decumulation strategy for a Defined Contribution (DC) pension plan as a problem in optimal stochastic control. The controls are the withdrawal amounts and the asset allocation strategy. We impose maximum and minimum constraints on the withdrawal amounts, and impose no-shorting no-leverage constraints on the asset allocation strategy. Our objective function measures reward as the expected total withdrawals over the decumulation horizon, and risk is measured by Expected Shortfall (ES) at the end of the decumulation period. We solve the stochastic control problem numerically, based on a parametric model of market stochastic processes. We find that, compared to a fixed constant withdrawal strategy, with minimum withdrawal set to the constant withdrawal amount, the optimal strategy has a significantly higher expected average withdrawal, at the cost of a very small increase in ES risk. Tests on bootstrapped resampled historical market data indicate that this strategy is robust to parametric model misspecification.
    Date: 2020–08
  9. By: Marco Gross; Dimitrios Laliotis; Mindaugas Leika; Pavel Lukyantsau
    Abstract: The objective of this paper is to present an integrated tool suite for IFRS 9- and CECL-compatible estimation in top-down solvency stress tests. The tool suite serves as an illustration for institutions wishing to include accounting-based approaches for credit risk modeling in top-down stress tests.
    Date: 2020–07–03
  10. By: Yichun Chi; Xun Yu Zhou; Sheng Chao Zhuang
    Abstract: We study the design of an optimal insurance contract in which the insured maximizes her expected utility and the insurer limits the variance of his risk exposure while maintaining the principle of indemnity and charging the premium according to the expected value principle. We derive the optimal policy semi-analytically, which is coinsurance above a deductible when the variance bound is binding. This policy automatically satisfies the incentive-compatible condition, which is crucial to rule out ex post moral hazard. We also find that the deductible is absent if and only if the contract pricing is actuarially fair. Focusing on the actuarially fair case, we carry out comparative statics on the effects of the insured's initial wealth and the variance bound on insurance demand. Our results indicate that the expected coverage is always larger for a wealthier insured, implying that the underlying insurance is a normal good, which supports certain recent empirical findings. Moreover, as the variance constraint tightens, the insured who is prudent cedes less losses, while the insurer is exposed to less tail risk.
    Date: 2020–08
  11. By: Eric Benhamou (LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - CNRS - Centre National de la Recherche Scientifique); Beatrice Guez; Nicolas Paris (APHP - Assistance publique - Hôpitaux de Paris (AP-HP))
    Abstract: Omega ratio, defined as the probability-weighted ratio of gains over losses at a given level of expected return, has been advocated as a better performance indicator compared to Sharpe and Sortino ratio as it depends on the full return distribution and hence encapsulates all information about risk and return. We compute Omega ratio for the normal distribution and show that under some distribution symmetry assumptions , the Omega ratio is oversold as it does not provide any additional information compared to Sharpe ratio. Indeed, for returns that have elliptic distributions , we prove that the optimal portfolio according to Omega ratio is the same as the optimal portfolio according to Sharpe ratio. As elliptic distributions are a weak form of symmetric distributions that generalized Gaussian distributions and encompass many fat tail distributions, this reduces tremendously the potential interest for the Omega ratio.
    Keywords: Omega ratio,Sharpe ratio,normal distribution,elliptical distribution
    Date: 2020–07–01
  12. By: Aili Zhang; Ping Chen; Shuanming Li; Wenyuan Wang
    Abstract: It has been decades since the academic world of ruin theory defined the insolvency of an insurance company as the time when its surplus falls below zero. This simplification, however, needs careful adaptions to imitate the real-world liquidation process. Inspired by Broadie et al. (2007) and Li et al. (2020), this paper uses a three-barrier model to describe the financial stress towards bankruptcy of an insurance company. The financial status of the insurer is divided into solvent, insolvent and liquidated three states, where the insurer's surplus process at the state of solvent and insolvent is modelled by two spectrally negative L\'{e}vy processes, which have been taken as good candidates to model insurance risks. We provide a rigorous definition of the time of liquidation ruin in this three-barrier model. By adopting the techniques of excursions in the fluctuation theory, we study the joint distribution of the time of liquidation, the surplus at liquidation and the historical high of the surplus until liquidation, which generalizes the known results on the classical expected discounted penalty function in Gerber and Shiu (1998). The results have semi-explicit expressions in terms of the scale functions and the L\'{e}vy triplets associated with the two underlying L\'{e}vy processes. The special case when the two underlying L\'{e}vy processes coincide with each other is also studied, where our results are expressed compactly via only the scale functions. The corresponding results have good consistency with the existing literatures on Parisian ruin with (or without) a lower barrier in Landriault et al. (2014), Baurdoux et al. (2016) and Frostig and Keren-Pinhasik (2019). Besides, numerical examples are provided to illustrate the underlying features of liquidation ruin.
    Date: 2020–07
  13. By: Sotirios Sabanis; Ying Zhang
    Abstract: A new approach in stochastic optimization via the use of stochastic gradient Langevin dynamics (SGLD) algorithms, which is a variant of stochastic gradient decent (SGD) methods, allows us to efficiently approximate global minimizers of possibly complicated, high-dimensional landscapes. With this in mind, we extend here the non-asymptotic analysis of SGLD to the case of discontinuous stochastic gradients. We are thus able to provide theoretical guarantees for the algorithm's convergence in (standard) Wasserstein distances for both convex and non-convex objective functions. We also provide explicit upper estimates of the expected excess risk associated with the approximation of global minimizers of these objective functions. All these findings allow us to devise and present a fully data-driven approach for the optimal allocation of weights for the minimization of CVaR of portfolio of assets with complete theoretical guarantees for its performance. Numerical results illustrate our main findings.
    Date: 2020–07
  14. By: Varinia Tindal; Denise Salazar (Banco Central de Bolivia)
    Abstract: Enterprise Risk Management (ERM) es una metodología de gestión de riesgos que permite identificar riesgos y oportunidades para optimizar la creación de valor desde un punto de vista integral. El presente trabajo realiza una comparación entre la gestión de riesgos tradicional y el ERM, describe los fundamentos en los que se basa esta metodología y se hace una revisión de las capacidades, principios y componentes que se requieren para la aplicación del ERM. Finalmente, se realiza un análisis de la implementación de esta metodología en la administración de las reservas internacionales del Banco Central de Bolivia (BCB) y una revisión de los límites de riesgo.
    Keywords: Bolivia, ERM, reservas internacionales, gestión de riesgo
    Date: 2018–12
  15. By: Figueres, Juan Manuel; Jarociński, Marek
    Abstract: This paper examines which measures of financial conditions are informative about the tail risks to output growth in the euro area. The Composite Indicator of Systemic Stress (CISS) is more informative than indicators focusing on narrower segments of financial markets or their simple aggregation in the principal component. Conditionally on the CISS one can reproduce for the euro area the stylized facts known from the US, such as the strong negative correlation between conditional mean and conditional variance that generates stable upper quantiles and volatile lower quantiles of output growth. JEL Classification: C12, E37, E44
    Keywords: downside risk, macro-financial linkages, quantile regression
    Date: 2020–08
  16. By: Jean-Baptiste Hasse (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université)
    Abstract: We propose a new measure of systemic risk based on interconnectedness, defined as the level of direct and indirect links between financial institutions in a correlation-based network. Deriving interconnectedness in terms of risk, we empirically show that within a financial network, indirect links are strengthened during systemic events. The relevance of our measure is illustrated at both local and global levels. Our framework offers policymakers a useful toolbox for exploring the real-time topology of the complex structure of dependencies in financial systems and for measuring the consequences of regulatory decisions.
    Keywords: Financial networks,Interconnectedness,Systemic risk,Spillover
    Date: 2020–06
  17. By: Pierre-Olivier Goffard (UCBL - Université Claude Bernard Lyon 1 - Université de Lyon, ISFA - Institut de Science Financière et d'Assurances, LSAF - Laboratoire de Sciences Actuarielles et Financières - EA2429 - ISFA - Institut de Science Financière et d'Assurances); Patrick Laub (UCBL - Université Claude Bernard Lyon 1 - Université de Lyon, ISFA - Institut de Science Financière et d'Assurances, LSAF - Laboratoire de Sciences Actuarielles et Financière - ISFA - Institut de Science Financière et d'Assurances)
    Abstract: Approximate Bayesian Computation (ABC) is a statistical learning technique to calibrate and select models by comparing observed data to simulated data. This technique bypasses the use of the likelihood and requires only the ability to generate synthetic data from the models of interest. We apply ABC to fit and compare insurance loss models using aggregated data. We present along the way how to use ABC for the more common claim counts and claim sizes data. A state-of-the-art ABC implementation in Python is proposed. It uses sequential Monte Carlo to sample from the posterior distribution and the Wasserstein distance to compare the observed and synthetic data. MSC 2010 : 60G55, 60G40, 12E10.
    Keywords: Bayesian statistics,approximate Bayesian computation,likelihood- free inference,risk management
    Date: 2020–07–06
  18. By: Anusha Chari; Karlye Dilts Stedman; Christian T. Lundblad
    Abstract: This paper characterizes the implications of risk-on/risk-off shocks for emerging market capital flows and returns. We document that these shocks have important implications not only for the median of emerging markets flows and returns but also for the left tail. Further, while there are some differences in the effects across bond vs. equity markets and flows vs. asset returns, the effects associated with the worst realizations are generally larger than on the median realization. We apply our methodology to the COVID-19 shock to examine the pattern of flow and return realizations: the sizable risk-off nature of this shock engenders reactions that reside deep in the left tail of most relevant emerging market quantities.
    Keywords: Capital flows; Emerging markets; risk-on/risk-off; COVID-19; Tail risk; Quantile regression
    JEL: F32 G15 G23
    Date: 2020–07–01
  19. By: Eric Benhamou (LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - CNRS - Centre National de la Recherche Scientifique); David Saltiel (ULCO - Université du Littoral Côte d'Opale); Beatrice Guez; Nicolas Paris (CRIL - Centre de Recherche en Informatique de Lens - UA - Université d'Artois - CNRS - Centre National de la Recherche Scientifique)
    Abstract: Sharpe ratio (sometimes also referred to as information ratio) is widely used in asset management to compare and benchmark funds and asset managers. It computes the ratio of the (excess) net return over the strategy standard deviation. However, the elements to compute the Sharpe ratio, namely, the expected returns and the volatilities are unknown numbers and need to be estimated statistically. This means that the Sharpe ratio used by funds is likely to be error prone because of statistical estimation errors. In this paper, we provide various tests to measure the quality of the Sharpe ratios. By quality, we are aiming at measuring whether a manager was indeed lucky of skillful. The test assesses this through the statistical significance of the Sharpe ratio. We not only look at the traditional Sharpe ratio but also compute a modified Sharpe insensitive to used Capital. We provide various statistical tests that can be used to precisely quantify the fact that the Sharpe is statistically significant. We illustrate in particular the number of trades for a given Sharpe level that provides statistical significance as well as the impact of auto-correlation by providing reference tables that provides the minimum required Sharpe ratio for a given time period and correlation. We also provide for a Sharpe ratio of 0.5, 1.0, 1.5 and 2.0 the skill percentage given the auto-correlation level. JEL classification: C12, G11.
    Keywords: Sharpe ratio,Student distribution,compounding effect on Sharpe,Wald test,T-test,Chi square test
    Date: 2020–07–01
  20. By: Stefano De Marco
    Abstract: It is well know that, in the short maturity limit, the implied volatility approaches the integral harmonic mean of the local volatility with respect to log-strike, see [Berestycki et al., Asymptotics and calibration of local volatility models, Quantitative Finance, 2, 2002]. This paper is dedicated to a complementary model-free result: an arbitrage-free implied volatility in fact is the harmonic mean of a positive function for any fixed maturity. We investigate the latter function, which is tightly linked to Fukasawa's invertible map $f_{1/2}$ [Fukasawa, The normalizing transformation of the implied volatility smile, Mathematical Finance, 22, 2012], and its relation with the local volatility surface. It turns out that the log-strike transformation $z = f_{1/2}(k)$ defines a new coordinate system in which the short-dated implied volatility approaches the arithmetic (as opposed to harmonic) mean of the local volatility. As an illustration, we consider the case of the SSVI parameterization: in this setting, we obtain an explicit formula for the volatility swap from options on realized variance.
    Date: 2020–07
  21. By: Eduardo Ramos-P\'erez; Pablo J. Alonso-Gonz\'alez; Jos\'e Javier N\'u\~nez-Vel\'azquez
    Abstract: Currently, legal requirements demand that insurance companies increase their emphasis on monitoring the risks linked to the underwriting and asset management activities. Regarding underwriting risks, the main uncertainties that insurers must manage are related to the premium sufficiency to cover future claims and the adequacy of the current reserves to pay outstanding claims. Both risks are calibrated using stochastic models due to their nature. This paper introduces a reserving model based on a set of machine learning techniques such as Gradient Boosting, Random Forest and Artificial Neural Networks. These algorithms and other widely used reserving models are stacked to predict the shape of the runoff. To compute the deviation around a former prediction, a log-normal approach is combined with the suggested model. The empirical results demonstrate that the proposed methodology can be used to improve the performance of the traditional reserving techniques based on Bayesian statistics and a Chain Ladder, leading to a more accurate assessment of the reserving risk.
    Date: 2020–08
  22. By: Glenn Boyle (University of Canterbury); Sanghyun Hong (University of Canterbury)
    Abstract: This paper examines the β4 liquidity risk premium documented in Acharya and Pedersen (2005). We decompose this premium into two components: the covariation of liquidity costs with (i) market dividend growth shocks and (ii) shocks to the variance of market returns. In 1963-2017 US stock market data, the former is approximately three times larger than the latter. Liquidity volatility is primarily incorporated in stock prices via its common variation with business, rather than financial, shocks.
    Keywords: Liquidity Risk; Asset Pricing
    JEL: G00 G12
    Date: 2020–08–01
  23. By: Thomas Spooner; Rahul Savani
    Abstract: Existing work on risk-sensitive reinforcement learning - both for symmetric and downside risk measures - has typically used direct Monte-Carlo estimation of policy gradients. While this approach yields unbiased gradient estimates, it also suffers from high variance and decreased sample efficiency compared to temporal-difference methods. In this paper, we study prediction and control with aversion to downside risk which we gauge by the lower partial moment of the return. We introduce a new Bellman equation that upper bounds the lower partial moment, circumventing its non-linearity. We prove that this proxy for the lower partial moment is a contraction, and provide intuition into the stability of the algorithm by variance decomposition. This allows sample-efficient, on-line estimation of partial moments. For risk-sensitive control, we instantiate Reward Constrained Policy Optimization, a recent actor-critic method for finding constrained policies, with our proxy for the lower partial moment. We extend the method to use natural policy gradients and demonstrate the effectiveness of our approach on three benchmark problems for risk-sensitive reinforcement learning.
    Date: 2020–07
  24. By: Fleck, Johannes; Monninger, Adrian
    Abstract: This paper shows that individual beliefs on the effectiveness of formal and informal sources of risk sharing determine financial precautionary behavior. We present empirical evidence demonstrating that higher trust in public insurance systems reduces net liquid wealth while higher trust in communal insurance increases it. This dichotomy is consistent with theories on access to private risk sharing networks. Moreover, we find that both types of trust associate positively with the probability to take on financial risk for the purpose of becoming a homeowner and the related loan-to-value ratio. Our findings are robust across a wide range of econometric controls and specifications. JEL Classification: D14, D31, E71, G5
    Keywords: household saving, portfolio liquidity, public and communal insurance
    Date: 2020–08
  25. By: Denuit, Michel
    Date: 2019–01–01
  26. By: Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Sowmya Subramaniam (Indian Institute of Management Lucknow, Prabandh Nagar off Sitapur Road, Lucknow, Uttar Pradesh 226013, India); Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Qiang Ji (Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China)
    Abstract: Using daily data from November 1985 to July 2020, we analyse the impact of a daily newspaper-based index of uncertainty associated with infectious diseases (EMVID) on the level, slope and curvature factors derived from the term structure of interest rates of the US covering maturities of 1 year to 30 years. Results from nonlinearity and structural break tests indicate the misspecification of the linear causality model and point to the suitability of applying a time-varying model that is robust to misspecification due to nonlinearity and regime change. We thus use a dynamic conditional correlation-multivariate generalised autoregressive conditional heteroskedasticity (DCC-MGARCH) framework and the results indicate significant predictability of the three latent factors from the EMVID index at each point of the entire sample, and also provide evidence of instantaneous spillover. Finally, we comprehensively determine the safe-haven characteristic of the US Treasury market by analysing the signs of the underlying time-varying conditional correlation between the level, slope and curvature factors and the EMVID index. Results show that US treasuries with long-term maturities as captured by the level factor are consistently negatively correlated with the EMVID index, i.e., they act as a safe-haven, with the slope factor (medium-term maturities) following this trend since 2007, and the slope factor (short-term maturities) also showing signs of a safe-haven since May of 2020. Overall, the findings provide reasonable evidence to imply that US Treasury securities can hedge the risks associated with the financial market in the wake of the current COVID-19 pandemic.
    Keywords: Yield Curve Factors, Financial Market Uncertainty, Infectious Diseases, COVID-19, Time-Varying Granger Causality
    JEL: C22 C32 E43 D80 G12
    Date: 2020–08
  27. By: Albert S. Kyle (University of Maryland); Anna A. Obizhaeva (New Economic School)
    Abstract: For five stock market crashes, we compare price declines with predictions from market microstructure invariance. During the 1987 crash and the 2008 sales by Société Générale, prices fell by magnitudes similar to predictions from invariance. Larger-than-predicted temporary price declines during 1987 and 2010 flash crashes suggest rapid selling exacerbates transitory price impact. Smaller-than-predicted price declines for the 1929 crash suggest slower selling stabilized prices and less integration made markets more resilient. Quantities sold in the three largest crashes indicate fatter tails or larger variance than the log-normal distribution estimated from portfolio transitions data.
    Keywords: Finance, market microstructure, invariance, crashes, liquidity, price impact, market depth, systemic risk
    JEL: G01 G28 N22
    Date: 2020–08
  28. By: Jonathan Kearns (Reserve Bank of Australia); Mike Major (Reserve Bank of Australia); David Norman (Reserve Bank of Australia)
    Abstract: Household indebtedness has increased substantially over several decades and across a range of countries. It is commonly cited as a major risk to numerous countries, including Australia. We consider how much risk this debt poses to Australia by asking three questions: (i) what accounts for the rise in household debt-to-income ratios and its level in Australia relative to other countries?; (ii) what losses might the Australian banking system suffer from these exposures in the event of a severe stress?; and (iii) how does household debt affect the sensitivity of consumption in Australia to severe economic shocks? Our results suggest that risks arising from Australian household indebtedness are more subtle than sometimes conveyed. In particular: fundamental factors (higher real incomes, a fall in nominal interest rates, financial liberalisation and household ownership of the rental stock) mostly account for the current level of household debt; banks appear resilient to a severe downturn thanks to moderate loan-to-valuation ratios on residential mortgages; and the distribution of debt does not appear to heighten wealth effects on consumption. However, there are risks. Our model cannot account for the increase in debt over the past four or five years. In addition, we demonstrate that a large but plausible fall in asset prices could lead to a substantial fall in consumption and that the increase in indebtedness over the past decade has slightly increased the potential loss of consumption during periods of financial stress.
    Keywords: household debt; financial stability; stress testing; marginal propensity to consume; household survey data
    JEL: C15 D31 E44
    Date: 2020–08
  29. By: D'Orazio, Paola; Dirks, Maximilian W.
    Abstract: COVID-19 has quickly emerged as a novel risk, generating feverish behavior among investors, and posing unprecedented challenges for policymakers. The empirical analysis provides evidence for a significant negative effect on stock markets of COVID-19-related measures announced in the Euro Area from January 1st, 2020 to May 17th, 2020. Further negative effects are detected for movements in bond yields, EU volatility index, Google trends, and infection rates. Health measures have, instead, a significant positive effect, while fiscal policy announcements are not significant.
    Keywords: Coronavirus,COVID-19,investor behavior,stock market volatility,containment policies,policy announcements,fiscal policy
    JEL: E44 G15
    Date: 2020
  30. By: Afafe Hertouch (UIT - Université Ibn Tofaïl); Mustapha Achibane (UIT - Université Ibn Tofaïl)
    Date: 2020
  31. By: Robert P. Bartlett III; Adair Morse
    Abstract: Using unique City of Oakland data during COVID-19, we document that small business survival capabilities vary by firm size as a function of revenue resiliency, labor flexibility, and committed costs. Nonemployer businesses rely on low cost structures to survive 73% declines in own-store foot traffic. Microbusinesses (1-to-5 employees) depend on 14% greater revenue resiliency. Enterprises (6-to-50 employees) have twice-as-much labor flexibility, but face 11%-to-22% higher residual closure risk from committed costs. Finally, inconsistent with the spirit of Chetty-Friedman-Hendren-Sterner (2020) and Granja-Makridis-Yannelis-Zwick (2020), PPP application success increased medium-run survival probability by 20.5%, but only for microbusinesses, arguing for size-targeting of policies.
    JEL: E61 G38 H32 J65 L26
    Date: 2020–07
  32. By: Paulina Concha Larrauri; Upmanu Lall
    Abstract: Frozen concentrated orange juice (FCOJ) is a commodity traded in the International Commodity Exchange. The FCOJ future price volatility is high because the world's orange production is concentrated in a few places, which results in extreme sensitivity to weather and disease. Most of the oranges produced in the United States are from Florida. The United States Department of Agriculture (USDA) issues orange production forecasts on the second week of each month from October to July. The October forecast in particular seems to affect FCOJ price volatility. We assess how a prediction of the directionality and magnitude of the error of the USDA October forecast could affect the decision making process of multiple FCOJ market participants, and if the "production uncertainty" of the forecast could be reduced by incorporating other climate variables. The models developed open up the opportunity to assess the application of the resulting probabilistic forecasts of the USDA production forecast error on the trading decisions of the different FCOJ stakeholders, and to perhaps consider the inclusion of climate predictors in the USDA forecast.
    Date: 2020–07
  33. By: Valentina Avramescu (Dimitrie Cantemir Christian University of Bucharest, Romania,)
    Abstract: The paper presents the topic regarding the insurance of assets, defining the insurance agreement and the notion of "asset" in the legal sense, the principles underlying the insurance, the classification of the assets, the distribution of the insurances according to their object, as well as the object of the insurance of assets mentioned in the insurance agreement. Also, the specific elements of this type of agreement can be found in the paper: the period of insurance of assets, the beginning and termination of liability, the insured interest- a condition imposed on the insurance agreement arising from the principle of damage compensation, the insured risk, that future and possible event, the conditions that an event must fulfil and also the insured case.
    Keywords: insurance, movable assets, immovable assets, insured risk, insured case
    Date: 2020–04
  34. By: Fabio C. Bagliano (Department of Economics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino, Italy); Carolina Fugazza (Department of Economics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino, Italy); Giovanna Nicodano (Department of Economics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino, Italy)
    Abstract: How should workers invest over the life-cycle? Should they follow some typical prescriptions ("rules of thumb") in personal finance implying higher equity investments when young? We show that the answer hinges on the risk of long-term unemployment spells, entailing permanent declines in workers' future earnings prospects. Absent unemployment risk, extant prescriptions deliver portfolios that are close to optimal, implying negligible welfare losses. They instead lead to sizable welfare losses (3-9% of annual consumption) when the risk of human capital depreciation following long-term unemployment is considered and realistically calibrated to the U.S. labor market. These losses stem from excess risk taking when young investors face uncertainty about future labor and pension incomes. This result points to a new design for pension plans offered by long-term institutional investors.
    Keywords: welfare, life-cycle portfolio choice, unemployment risk, long term unemployment, age rules.
    JEL: E21 G11
    Date: 2020–09
  35. By: Lakshay Chauhan; John Alberg; Zachary C. Lipton
    Abstract: On a periodic basis, publicly traded companies report fundamentals, financial data including revenue, earnings, debt, among others. Quantitative finance research has identified several factors, functions of the reported data that historically correlate with stock market performance. In this paper, we first show through simulation that if we could select stocks via factors calculated on future fundamentals (via oracle), that our portfolios would far outperform standard factor models. Motivated by this insight, we train deep nets to forecast future fundamentals from a trailing 5-year history. We propose lookahead factor models which plug these predicted future fundamentals into traditional factors. Finally, we incorporate uncertainty estimates from both neural heteroscedastic regression and a dropout-based heuristic, improving performance by adjusting our portfolios to avert risk. In retrospective analysis, we leverage an industry-grade portfolio simulator (backtester) to show simultaneous improvement in annualized return and Sharpe ratio. Specifically, the simulated annualized return for the uncertainty-aware model is 17.7% (vs 14.0% for a standard factor model) and the Sharpe ratio is 0.84 (vs 0.52).
    Date: 2020–07
  36. By: Pierre Picard (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - CNRS - Centre National de la Recherche Scientifique - X - École polytechnique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique)
    Date: 2020–07–03
  37. By: Arlan Brucal (The Grantham Research Institute on Climate Change and the Environment (GRICCE)); Viktor Roezer (The Grantham Research Institute on Climate Change and the Environment (GRICCE)); Denyse S. Dookie (The Grantham Research Institute on Climate Change and the Environment (GRICCE)); Rebecca Byrnes (The Grantham Research Institute on Climate Change and the Environment (GRICCE)); Majah-Leah V. Ravago (Economics Department, Ateneo de Manila University); Faye Cruz (Manila Observatory); Gemma Narisma (Manila Observatory and Physics Department, Ateneo de Manila University)
    Abstract: The Philippines is a country with high exposure to natural hazards and with limited resources for dealing with them. It is therefore vital that available funding for disaster preparedness and relief is allocated based on accurate forecasts and evidence. Disaster Risk Managers play an integral role in the delivery of disaster preparedness and relief. A 2016–17 survey of Disaster Risk Managers identified important differences in how Managers perceive risk and their levels of preparedness across the country in light of differing storm impacts since 2009. Pre-disaster preparedness receives less funding than post-disaster relief. Greater financing for preparedness, based on an improved understanding of Disaster Risk Managers’ perceptions and needs and better communication of future climate risk, is needed in order to help vulnerable communities more effectively before a disaster occurs.
    Keywords: Disaster risk management, local governance, Philippines, hydrometeorological hazards
    JEL: Q54 Q58 H84
    Date: 2020–08
  38. By: Kazuya Kaneko; Koichi Miyamoto; Naoyuki Takeda; Kazuyoshi Yoshino
    Abstract: Applications of the quantum algorithm for Monte Carlo simulation to pricing of financial derivatives have been discussed in previous papers. However, up to now, the pricing model discussed in such papers is Black-Scholes model, which is important but simple. Therefore, it is motivating to consider how to implement more complex models used in practice in financial institutions. In this paper, we then consider the local volatility (LV) model, in which the volatility of the underlying asset price depends on the price and time. We present two types of implementation. One is the register-per-RN way, which is adopted in most of previous papers. In this way, each of random numbers (RNs) required to generate a path of the asset price is generated on a separated register, so the required qubit number increases in proportion to the number of RNs. The other is the PRN-on-a-register way, which is proposed in the author's previous work. In this way, a sequence of pseudo-random numbers (PRNs) generated on a register is used to generate paths of the asset price, so the required qubit number is reduced with a trade-off against circuit depth. We present circuit diagrams for these two implementations in detail and estimate required resources: qubit number and T-count.
    Date: 2020–07

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