nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒08‒14
nineteen papers chosen by
Stan Miles, Thompson Rivers University

  1. A cohort-based Partial Internal Model for demographic risk By Francesco Della Corte; Gian Paolo Clemente; Nino Savelli
  2. Pricing and Hedging Guaranteed Equity Securities By David Lee
  3. Importance Sampling for Minimization of Tail Risks: A Tutorial By Anand Deo; Karthyek Murthy
  4. Systemic Tail Risk: High-Frequency Measurement, Evidence and Implications By Deniz Erdemlioglu; Christopher J. Neely; Xiye Yang
  5. Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data By Ruijun Bu; Degui Li; Oliver Linton; Hanchao Wang
  6. Expectile Quadrangle and Applications By Viktor Kuzmenko; Anton Malandii; Stan Uryasev
  7. Are Basel III requirements up to the task? Evidence from bankruptcy prediction models By Pierre Durand; Gaëtan Le Quang; Arnold Vialfont
  8. A closed form model-free approximation for the Initial Margin of option portfolios By Claude Martini; Arianna Mingone
  9. Portfolio Optimization: A Comparative Study By Jaydip Sen; Subhasis Dasgupta
  10. Liquidity Premium and Liquidity-Adjusted Return and Volatility: illustrated with a Liquidity-Adjusted Mean Variance Framework and its Application on a Portfolio of Crypto Assets By Qi Deng
  11. The contribution of realized covariance models to the economic value of volatility timing By Bauwens, Luc; Xu, Yongdeng
  12. Robust Hedging GANs By Yannick Limmer; Blanka Horvath
  13. Life insurance convexity By Grochola, Nicolaus; Gründl, Helmut; Kubitza, Christian
  14. Valuation of Equity Linked Securities with Guaranteed Return By David Xiao
  15. Currency Risk Premiums: A Multi-horizon Perspective By Mikhail Chernov; Magnus Dahlquist
  16. Are ESG ratings informative to forecast idiosyncratic risk? By Christophe Boucher; Wassim Le Lann; Stéphane Matton; Sessi Tokpavi
  17. The state-dependent impact of changes in bank capital requirements By Lang, Jan Hannes; Menno, Dominik
  18. Implementation of Financial Resilience Against Global Recession Threat Issues By Saputra, Mohammad Fajar; Pandin, Maria Yovita R; Hastungkara, Hanif Dwi
  19. A multi-criteria framework for critical infrastructure systems resilience By Zhuyu Yang; Bruno Barroca; Katia Laffréchine; Alexandre Weppe; Aurélia Bony-Dandrieux; Nicolas Daclin

  1. By: Francesco Della Corte; Gian Paolo Clemente; Nino Savelli
    Abstract: We investigate the quantification of demographic risk in a framework consistent with the market-consistent valuation imposed by Solvency II. We provide compact formulas for evaluating inflows and outflows of a portfolio of insurance policies based on a cohort approach. In this context, we maintain the highest level of generality in order to consider both traditional policies and equity-linked policies: therefore, we propose a market-consistent valuation of the liabilities. In the second step we evaluate the Solvency Capital Requirement of the idiosyncratic risk, linked to accidental mortality, and the systematic risk one, also known as trend risk, proposing a formal closed formula for the former and an algorithm for the latter. We show that accidental volatility depends on the intrinsic characteristics of the policies of the cohort (Sums-at-Risk), on the age of the policyholders and on the variability of the sums insured; trend risk depends both on accidental volatility and on the longevity forecasting model used.
    Date: 2023–07
  2. By: David Lee (BMO)
    Abstract: Equity-linked securities with a guaranteed return gain popularity in financial market. The contract depends on the performance of a basket of equity components averaged over a certain period, and also guarantees the investor a minimum return. This paper presents a new method for valuing the guaranteed equity-linked securities. We compute the security's price, corresponding hedge ratios, and risk sensitivities. The model appears to be accurate over a wide range of underlying security parameters, based on numerical studies.
    Keywords: Guaranteed equity security hedge ratio risk sensitivity asset pricing derivative valuation risk management JEL Classification: E44 G21 G12 G24 G32 G33 G18 G28, Guaranteed equity security, hedge ratio, risk sensitivity, asset pricing, derivative valuation, risk management
    Date: 2023–06–25
  3. By: Anand Deo; Karthyek Murthy
    Abstract: This paper provides an introductory overview of how one may employ importance sampling effectively as a tool for solving stochastic optimization formulations incorporating tail risk measures such as Conditional Value-at-Risk. Approximating the tail risk measure by its sample average approximation, while appealing due to its simplicity and universality in use, requires a large number of samples to be able to arrive at risk-minimizing decisions with high confidence. This is primarily due to the rarity with which the relevant tail events get observed in the samples. In simulation, Importance Sampling is among the most prominent methods for substantially reducing the sample requirement while estimating probabilities of rare events. Can importance sampling be used for optimization as well? If so, what are the ingredients required for making importance sampling an effective tool for optimization formulations involving rare events? This tutorial aims to provide an introductory overview of the two key ingredients in this regard, namely, (i) how one may arrive at an importance sampling change of measure prescription at every decision, and (ii) the prominent techniques available for integrating such a prescription within a solution paradigm for stochastic optimization formulations.
    Date: 2023–07
  4. By: Deniz Erdemlioglu; Christopher J. Neely; Xiye Yang
    Abstract: We develop a new framework to measure market-wide (systemic) tail risk in the cross-section of high-frequency stock returns. We estimate the time-varying jump intensities of asset prices and introduce a testing approach that identifies multi-asset tail risk based on the release times of scheduled news announcements. Using high-frequency data on individual U.S. stocks and sector-specific ETF portfolios, we find that most of the FOMC announcements create systemic left tail risk, but there is no evidence that macro announcements do so. The magnitude of the tail risk induced by Fed news varies over the business cycle, peaks during the global financial crisis and remains high over different phases of unconventional monetary policy. We use our approach to construct a Fed-induced systemic tail risk (STR) indicator. STR helps explain the pre-FOMC announcement drift and significantly increases variance risk premia, particularly for the meetings without press conferences.
    Keywords: time-varying tail risk; high-frequency data; Federal Open Market Committee (FOMC) news; monetary policy announcements; cojumps; systemic risk; jump intensity
    JEL: C12 C14 C22 C32 C58 G12 G14
    Date: 2023–07–20
  5. By: Ruijun Bu; Degui Li; Oliver Linton; Hanchao Wang
    Abstract: In this paper, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for a large number of assets. We first combine classic nonparametric kernel-based smoothing with a generalised shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the approximate sparsity commonly used in the literature. The uniform consistency property is derived for the proposed spot volatility matrix estimator with convergence rates comparable to the optimal minimax one. For the high-frequency data contaminated by microstructure noise, we introduce a localised pre-averaging estimation method that reduces the effective magnitude of the noise. We then use the estimation tool developed in the noise-free scenario, and derive the uniform convergence rates for the developed spot volatility matrix estimator. We further combine the kernel smoothing with the shrinkage technique to estimate the time-varying volatility matrix of the high-dimensional noise vector. In addition, we consider large spot volatility matrix estimation in time-varying factor models with observable risk factors and derive the uniform convergence property. We provide numerical studies including simulation and empirical application to examine the performance of the proposed estimation methods in finite samples.
    Date: 2023–07
  6. By: Viktor Kuzmenko; Anton Malandii; Stan Uryasev
    Abstract: The paper explores the concept of the \emph{expectile risk measure} within the framework of the Fundamental Risk Quadrangle (FRQ) theory. According to the FRQ theory, a quadrangle comprises four stochastic functions associated with a random variable: ``error'', ``regret'', ``risk'', and ``deviation''. These functions are interconnected through a stochastic function known as the ``statistic''. Expectile is a risk measure that, similar to VaR (quantile) and CVaR (superquantile), can be employed in risk management. While quadrangles based on VaR and CVaR statistics are well-established and widely used, the paper focuses on the recently proposed quadrangles based on expectile. The aim of this paper is to rigorously examine the properties of these Expectile Quadrangles, with particular emphasis on a quadrangle that encompasses expectile as both a statistic and a measure of risk.
    Date: 2023–06
  7. By: Pierre Durand; Gaëtan Le Quang; Arnold Vialfont
    Date: 2023
  8. By: Claude Martini; Arianna Mingone
    Abstract: Central clearing counterparty houses (CCPs) play a fundamental role in mitigating the counterparty risk for exchange traded options. CCPs cover for possible losses during the liquidation of a defaulting member's portfolio by collecting initial margins from their members. In this article we analyze the current state of the art in the industry for computing initial margins for options, whose core component is generally based on a VaR or Expected Shortfall risk measure. We derive an approximation formula for the VaR at short horizons in a model-free setting. This innovating formula has promising features and behaves in a much more satisfactory way than the classical Filtered Historical Simulation-based VaR in our numerical experiments. In addition, we consider the neural-SDE model for normalized call prices proposed by [Cohen et al., arXiv:2202.07148, 2022] and obtain a quasi-explicit formula for the VaR and a closed formula for the short term VaR in this model, due to its conditional affine structure.
    Date: 2023–06
  9. By: Jaydip Sen; Subhasis Dasgupta
    Abstract: Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.
    Date: 2023–07
  10. By: Qi Deng
    Abstract: We establish innovative measures of liquidity premium Beta on both asset and portfolio levels, and corresponding liquidity-adjusted return and volatility, for selected crypto assets. We develop liquidity-adjusted ARMA-GARCH/EGARCH representation to model the liquidity-adjusted return of individual assets, and liquidity-adjusted VECM/VAR-DCC/ADCC structure to model the liquidity-adjusted variance of portfolio. Both models exhibit improved predictability at high liquidity, which affords a liquidity-adjusted mean-variance (LAMV) framework a clear advantage over its regular mean variance (RMV) counterpart in portfolio performance.
    Date: 2023–06
  11. By: Bauwens, Luc ((Université catholique de Louvain, CORE, Belgium); Xu, Yongdeng (Cardiff Business School)
    Abstract: Realized covariance models specify the conditional expectation of a realized covariance matrix as a function of past realized covariance matrices through a GARCH-type structure. We compare the forecasting performance of several such models in terms of economic value, measured through economic loss functions, on two datasets. Our empirical results indicate that the (HEAVY-type) models that use realized volatilities yield economic value and significantly surpass the (GARCH) models that use only daily returns for daily and weekly horizons. Among the HEAVY-type models, for a dataset of twenty-nine stocks, those that are specified to capture the heterogeneity of the dynamics of the individual conditional variance processes and to allow these to differ from the correlation processes (namely, DCC-type models) are more beneficial than the models that impose the same dynamics to the variance and covariance processes (namely, BEKK-type models), whereas for the dataset of three assets, the different models perform similarly. Finally, using a directly rescaled intra-day covariance to estimate the full-day covariance provides more economic value than using the overnight returns, as the latter tend to yield noisy estimators of the overnight covariance, impairing their predictive capacity.
    Keywords: volatility timing, realized volatility, high-frequency data, forecasting
    JEL: G11 G17 C32 C58
    Date: 2023–07
  12. By: Yannick Limmer; Blanka Horvath
    Abstract: The availability of deep hedging has opened new horizons for solving hedging problems under a large variety of realistic market conditions. At the same time, any model - be it a traditional stochastic model or a market generator - is at best an approximation of market reality, prone to model-misspecification and estimation errors. This raises the question, how to furnish a modelling setup with tools that can address the risk of discrepancy between anticipated distribution and market reality, in an automated way. Automated robustification is currently attracting increased attention in numerous investment problems, but it is a delicate task due to its imminent implications on risk management. Hence, it is beyond doubt that more activity can be anticipated on this topic to converge towards a consensus on best practices. This paper presents a natural extension of the original deep hedging framework to address uncertainty in the data generating process via an adversarial approach inspired by GANs to automate robustification in our hedging objective. This is achieved through an interplay of three modular components: (i) a (deep) hedging engine, (ii) a data-generating process (that is model agnostic permitting a large variety of classical models as well as machine learning-based market generators), and (iii) a notion of distance on model space to measure deviations between our market prognosis and reality. We do not restrict the ambiguity set to a region around a reference model, but instead penalize deviations from the anticipated distribution. Our suggested choice for each component is motivated by model agnosticism, allowing a seamless transition between settings. Since all individual components are already used in practice, we believe that our framework is easily adaptable to existing functional settings.
    Date: 2023–07
  13. By: Grochola, Nicolaus; Gründl, Helmut; Kubitza, Christian
    Abstract: Life insurers sell savings contracts with surrender options, which allow policyholders to prematurely receive guaranteed surrender values. These surrender options move toward the money when interest rates rise. Hence, higher interest rates raise surrender rates, as we document empirically by exploiting plausibly exogenous variation in monetary policy. Using a calibrated model, we then estimate that surrender options would force insurers to sell up to 2% of their investments during an enduring interest rate rise of 25 bps per year. We show that these fire sales are fueled by surrender value guarantees and insurers’ long-term investments. JEL Classification: G22, E44, E52, G52
    Keywords: Interest Rates, Life Insurance, Liquidity Risk, Surrender Options, Systemic Risk
    Date: 2023–07
  14. By: David Xiao
    Abstract: Equity-linked securities with a guaranteed return become very popular in financial markets ether as investment instruments or life insurance policies. The contract pays off a guaranteed amount plus a payment linked to the performance of a basket of equities averaged over a certain period. This paper presents a new model for valuing equity-linked securities. Our study shows that the security price can be replicated by the sum of the guaranteed amount plus the price of an Asian style option on the basket. Analytical formulas are derived for the security price and corresponding hedge ratios. The model appears to be accurate over a wide range of underlying security parameters according to numerical studies. Finally, we use our model to value a segregated fund with a guarantee at maturity.
    Date: 2023–06
  15. By: Mikhail Chernov; Magnus Dahlquist
    Abstract: We review the literature on multi-horizon currency risk premiums. We show how the multi-horizon implications arise from the classic present-value relationship. We further show how these implications manifest themselves in the interaction between bond and currency risk premiums. This link is strengthened by explicitly accounting for stochastic discount factors. Information about currency risk premiums at different horizons presents a wealth of new evidence and challenges for existing models.
    JEL: E43 E52 F31 G12 G15
    Date: 2023–06
  16. By: Christophe Boucher; Wassim Le Lann (UO - Université d'Orléans, LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne); Stéphane Matton; Sessi Tokpavi
    Abstract: Sustainable investing is growing fast and investors are increasingly integrating environmental, social, and governance (ESG) criteria. However, ESG ratings are derived using heterogeneous methodologies and can be quite divergent across providers, which suggests the need for a formal statistical procedure to evaluate their accuracy. This paper develops a backtesting procedure that evaluates how well these extra-financial metrics help in predicting a company's idiosyncratic risk. Technically, the inference is based on extending the conditional predictive ability test of Giacomini and White (2006) to a panel data setting. We apply our methodology to the forecasting of stock returns idiosyncratic volatility and compare two ESG rating systems from Sustainalytics and Asset4 across three investment universes (Europe, North America, and the Asia-Pacific region). The results show that the null hypothesis of no informational content in ESG ratings is strongly rejected in Europe, whereas results appear mixed in the other regions. Furthermore, the predictive accuracy gains are higher when considering the environmental dimension of ESG ratings. Importantly, applying the test only to firms over which there is a high degree of consensus between the ESG rating agencies leads to higher predictive accuracy gains for all three universes. Beyond providing insights into the accuracy of each of the ESG rating systems, this last result suggests that information gathered from several ESG rating providers should be cross-checked before ESG is integrated into investment processes.
    Keywords: Backtesting, ESG ratings, Idiosyncratic realised volatility, Test of equal predictive power, Panel data, Consensus ESG ratings
    Date: 2023–06–24
  17. By: Lang, Jan Hannes; Menno, Dominik
    Abstract: Based on a non-linear equilibrium model of the banking sector with an occasionally-binding equity issuance constraint, we show that the economic impact of changes in bank capital requirements depends on the state of the macro-financial environment. In ”normal” states where banks do not face problems to retain enough profits to satisfy higher capital requirements, the impact on bank loan supply works through a ”pricing channel” which is small: around 0.1% less loans for a 1pp increase in capital requirements. In ”bad” states where banks are not able to come up with sufficient equity to satisfy capital requirements, the impact on loan supply works through a ”quantity channel”, which acts like a financial accelerator and can be very large: up to 10% more loans for a capital requirement release of 1pp. Compared to existing DSGE models with a banking sector, which usually feature a constant lending response of around 1%, our state-dependent impact is an order of magnitude lower in ”normal” states and an order of magnitude higher in ”bad” states. Our results provide a theoretical justification for building up a positive countercyclical capital buffer in ”normal” macro-financial environments. JEL Classification: D21, E44, E51, G21, G28
    Keywords: Bank capital requirements, dynamic stochastic equilibrium model, financial accelerator, global solution methods, loan supply
    Date: 2023–07
  18. By: Saputra, Mohammad Fajar; Pandin, Maria Yovita R; Hastungkara, Hanif Dwi
    Abstract: The economic recession or global recession itself can be understood as a condition that explains that the economy of one country is not doing well, this can be seen from the Gross Domestic Product (GDP) which shows a negative side, rising unemployment, and economic growth which shows a positive direction. negative in two consecutive quarters. Good risk management is also important for building strength or resilience in financial institutions. Discusses the challenges in building strength or resilience in financial institutions and financial markets. Building strength or resilience in financial institutions and financial markets is critical to ensuring overall economic stability and health.
    Date: 2023–06–17
  19. By: Zhuyu Yang (LAB'URBA - LAB'URBA - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel, LATTS - Laboratoire Techniques, Territoires et Sociétés - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - Université Gustave Eiffel); Bruno Barroca (LAB'URBA - LAB'URBA - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel); Katia Laffréchine (LAB'URBA - LAB'URBA - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel); Alexandre Weppe (LSR - Laboratoire des Sciences des Risques - IMT - MINES ALES - IMT - MINES ALES - IMT - Institut Mines-Télécom [Paris]); Aurélia Bony-Dandrieux (LSR - Laboratoire des Sciences des Risques - IMT - MINES ALES - IMT - MINES ALES - IMT - Institut Mines-Télécom [Paris]); Nicolas Daclin (LSR - Laboratoire des Sciences des Risques - IMT - MINES ALES - IMT - MINES ALES - IMT - Institut Mines-Télécom [Paris])
    Abstract: Critical infrastructure systems (CISs) play an essential role in modern society, as they are important for maintaining critical social functions, economic organisation, and national defence. Recently, CISs resilience has gained popularity in both academic and policy filed facing increased natural or technological disasters. Resilience assessments have become convenient and common tools for disaster management, as assessment results provide useful information to CIS managers. However, CISs resilience assessment is facing challenges of being practical to use in operational risk management. Although there are many existing assessments for CISs resilience, some shortcomings relating to assessment criteria, which cannot turn resilience useful in practical operation, are frequent in their assessment process. Existing assessments are based on different definitions, which makes criteria generalization difficult. Besides, these assessments are not comprehensive enough. Especially, few assessments address both the cost, effectiveness, and safety of optimisation actions. Moreover, most of the suggested criteria are not specific enough for being used for practical CISs risk management in real cases. This article develops therefore a multi-criteria framework (MCF) for CISs resilience, consisting of general criteria and a guide for defining specific sub-criteria. In this MCF, the side effects, cascading effects and costbenefit in resilience scenarios are considered indispensable for CISs resilience assessment. The paper also presents an example of the application of the developed guide through two detailed scenarios, one on a single infrastructural system affected by a natural disaster, and the other addressing the interdependence of this infrastructural system and an urban healthcare system. The designed MCF contributes to the operationalisation and comprehensiveness of CISs resilience assessments.
    Keywords: Critical infrastructure systems, Multi-criteria, Resilience, Disaster management, Resilience assessment
    Date: 2023–09

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