nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒12‒13
23 papers chosen by
Stan Miles
Thompson Rivers University

  1. Semi-nonparametric Estimation of Operational Risk Capital with Extreme Loss Events By Heng Z. Chen; Stephen R. Cosslett
  2. Mean-Variance-VaR portfolios: MIQP formulation and performance analysis By Francesco Cesarone; Manuel L Martino; Fabio Tardella
  3. Central Bank Risk Management, Fintech, and Cybersecurity By Mr. Ashraf Khan; Majid Malaika
  4. Non-asymptotic estimation of risk measures using stochastic gradient Langevin dynamics By Jiarui Chu; Ludovic Tangpi
  5. Hedging Cryptocurrency Options By Matic, Jovanka Lili; Packham, Natalie; Härdle, Wolfgang Karl
  6. Online Estimation and Optimization of Utility-Based Shortfall Risk By Arvind S. Menon; Prashanth L. A.; Krishna Jagannathan
  7. Is the empirical out-of-sample variance an informative risk measure for the high-dimensional portfolios? By Taras Bodnar; Nestor Parolya; Erik Thors\'en
  8. On the Stability of Risk Preferences: Measurement Matters By Adema, Joop; Nikolka, Till; Poutvaara, Panu; Sunde, Uwe
  9. Determinants of European Banks’ Default Risk By Nicolas Soenen; Rudi Vander Vennet
  10. On the Risk Efficiency of a Weather Index Insurance Product for the Brazilian Semi-Arid Region By Lavorato, Mateus; Braga, Marcelo José
  11. Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms By Nikitas Stamatopoulos; Guglielmo Mazzola; Stefan Woerner; William J. Zeng
  12. A Universal End-to-End Approach to Portfolio Optimization via Deep Learning By Chao Zhang; Zihao Zhang; Mihai Cucuringu; Stefan Zohren
  13. The Countercyclical Capital Buffer and International Bank Lending: Evidence from Canada By David Chen; Christian Friedrich
  14. Effect of the U.S.--China Trade War on Stock Markets: A Financial Contagion Perspective By Minseog Oh; Donggyu Kim
  15. Is Higher Financial Stress Lurking around the Corner for China? By Jan J. J. Groen; Adam I. Noble
  16. The Impacts of Oil Price Volatility on Financial Stress: Is the COVID-19 Period Different? By Xin Sheng; Won Joong Kim; Rangan Gupta
  17. Dependent Stopping Times By Philip Protter; Alejandra Quintos
  18. Overview of central banks’ in-house credit assessment systems in the euro area By Laura Auria; Markus Bingmer; Carlos Mateo Caicedo Graciano; Clémence Charavel; Sergio Gavilá; Alessandra Iannamorelli; Aviram Levy; Alfredo Maldonado; Florian Resch; Anna Maria Rossi; Stephan Sauer
  19. Cryptocurrencies responses to the Covid-19 waves By amri amamou, souhir
  20. Disaster Risk Financing: Main Concepts and Evidence from EU Member States By Diana Radu
  21. Risk, Uncertainty, and Biofuels’ Supply Chains. By Lee, Yuanyao; Khanna, Madhu
  22. Portfolio optimisation with options By Jonathan Raimana Chan; Thomas Huckle; Antoine Jacquier; Aitor Muguruza
  23. Target date funds and portfolio choice in 401(k) plans By Mitchell, Olivia S.; Utkus, Stephen P.

  1. By: Heng Z. Chen; Stephen R. Cosslett
    Abstract: Operational risk modeling using the parametric models can lead to a counter-intuitive estimate of value at risk at 99.9% as economic capital due to extreme events. To address this issue, a flexible semi-nonparametric (SNP) model is introduced using the change of variables technique to enrich the family of distributions that can be used for modeling extreme events. The SNP models are proved to have the same maximum domain of attraction (MDA) as the parametric kernels, and it follows that the SNP models are consistent with the extreme value theory - peaks over threshold method but with different shape and scale parameters. By using the simulated datasets generated from a mixture of distributions with varying body-tail thresholds, the SNP models in the Fr\'echet and Gumbel MDAs are shown to fit the datasets satisfactorily through increasing the number of model parameters, resulting in similar quantile estimates at 99.9%. When applied to an actual operational risk loss dataset from a major international bank, the SNP models yield a sensible capital estimate that is around 2 to 2.5 times as large as the single largest loss event.
    Date: 2021–11
  2. By: Francesco Cesarone; Manuel L Martino; Fabio Tardella
    Abstract: Value-at-Risk is one of the most popular risk management tools in the financial industry. Over the past 20 years several attempts to include VaR in the portfolio selection process have been proposed. However, using VaR as a risk measure in portfolio optimization models leads to problems that are computationally hard to solve. In view of this, few practical applications of VaR in portfolio selection have appeared in the literature up to now. In this paper, we propose to add the VaR criterion to the classical Mean-Variance approach in order to better address the typical regulatory constraints of the financial industry. We thus obtain a portfolio selection model characterized by three criteria: expected return, variance, and VaR at a specified confidence level. The resulting optimization problem consists in minimizing variance with parametric constraints on the levels of expected return and VaR. This model can be formulated as a Mixed-Integer Quadratic Programming (MIQP) problem. An extensive empirical analysis on seven real-world datasets demonstrates the practical applicability of the proposed approach. Furthermore, the out-of-sample performance of the optimal Mean-Variance-VaR portfolios seems to be generally better than that of the optimal Mean-Variance and Mean-VaR portfolios.
    Date: 2021–11
  3. By: Mr. Ashraf Khan; Majid Malaika
    Abstract: Based on technical assistance to central banks by the IMF’s Monetary and Capital Markets Department and Information Technology Department, this paper examines fintech and the related area of cybersecurity from the perspective of central bank risk management. The paper draws on findings from the IMF Article IV Database, selected FSAP and country cases, and gives examples of central bank risks related to fintech and cybersecurity. The paper highlights that fintech- and cybersecurity-related risks for central banks should be addressed by operationalizing sound internal risk management by establishing and strengthening an integrated risk management approach throughout the organization, including a dedicated risk management unit, ongoing sensitizing and training of Board members and staff, clear reporting lines, assessing cyber resilience and security posture, and tying risk management into strategic planning.. Given the fast-evolving nature of such risks, central banks could make use of timely and regular inputs from external experts.
    Keywords: IMF Risk Management TA; risk landscape; B. IMF AIV; IMF surveillance; case example; Fintech; Central bank risk management; Cyber risk; Operational risk; Global; Middle East and Central Asia; Asia and Pacific; Western Hemisphere; Eastern Europe
    Date: 2021–04–23
  4. By: Jiarui Chu; Ludovic Tangpi
    Abstract: In this paper we will study the approximation of arbitrary law invariant risk measures. As a starting point, we approximate the average value at risk using stochastic gradient Langevin dynamics, which can be seen as a variant of the stochastic gradient descent algorithm. Further, the Kusuoka's spectral representation allows us to bootstrap the estimation of the average value at risk to extend the algorithm to general law invariant risk measures. We will present both theoretical, non-asymptotic convergence rates of the approximation algorithm and numerical simulations.
    Date: 2021–11
  5. By: Matic, Jovanka Lili; Packham, Natalie; Härdle, Wolfgang Karl
    Abstract: The cryptocurrency (CC) market is volatile, non-stationary and non-continuous. This poses unique challenges for pricing and hedging CC options. We study the hedge behaviour and effectiveness for a wide range of models. First, we calibrate market data to SVI-implied volatility surfaces to price options. To cover a wide range of market dynamics, we generate price paths using two types of Monte Carlo simulations. In the first approach, price paths follow an SVCJ model (stochastic volatility with correlated jumps). The second approach simulates paths from a GARCH-filtered kernel density estimation. In these two markets, options are hedged with models from the class of affine jump diffusions and infinite activity L\'evy processes. Including a wide range of market models allows to understand the trade-off in the hedge performance between complete, but overly parsimonious models, and more complex, but incomplete models. Dynamic Delta, Delta-Gamma, Delta-Vega and minimum variance hedge strategies are applied. The calibration results reveal a strong indication for stochastic volatility, low jump intensity and evidence of infinite activity. With the exception of short-dated options, a consistently good performance is achieved with Delta-Vega hedging in stochastic volatility models. Judging on the calibration and hedging results, the study provides evidence that stochastic volatility is the driving force in CC markets.
    Keywords: Hedging, cryptocurrencies, digital finance, bitcoin, options
    JEL: G12
    Date: 2021–11–20
  6. By: Arvind S. Menon; Prashanth L. A.; Krishna Jagannathan
    Abstract: Utility-Based Shortfall Risk (UBSR) is a risk metric that is increasingly popular in financial applications, owing to certain desirable properties that it enjoys. We consider the problem of estimating UBSR in a recursive setting, where samples from the underlying loss distribution are available one-at-a-time. We cast the UBSR estimation problem as a root finding problem, and propose stochastic approximation-based estimations schemes. We derive non-asymptotic bounds on the estimation error in the number of samples. We also consider the problem of UBSR optimization within a parameterized class of random variables. We propose a stochastic gradient descent based algorithm for UBSR optimization, and derive non-asymptotic bounds on its convergence.
    Date: 2021–11
  7. By: Taras Bodnar; Nestor Parolya; Erik Thors\'en
    Abstract: The main contribution of this paper is the derivation of the asymptotic behaviour of the out-of-sample variance, the out-of-sample relative loss, and of their empirical counterparts in the high-dimensional setting, i.e., when both ratios $p/n$ and $p/m$ tend to some positive constants as $m\to\infty$ and $n\to\infty$, where $p$ is the portfolio dimension, while $n$ and $m$ are the sample sizes from the in-sample and out-of-sample periods, respectively. The results are obtained for the traditional estimator of the global minimum variance (GMV) portfolio, for the two shrinkage estimators introduced by \cite{frahm2010} and \cite{bodnar2018estimation}, and for the equally-weighted portfolio, which is used as a target portfolio in the specification of the two considered shrinkage estimators. We show that the behaviour of the empirical out-of-sample variance may be misleading is many practical situations. On the other hand, this will never happen with the empirical out-of-sample relative loss, which seems to provide a natural normalization of the out-of-sample variance in the high-dimensional setup. As a result, an important question arises if this risk measure can safely be used in practice for portfolios constructed from a large asset universe.
    Date: 2021–11
  8. By: Adema, Joop (University of Munich); Nikolka, Till (German Youth Institute (DJI)); Poutvaara, Panu (University of Munich); Sunde, Uwe (University of Munich)
    Abstract: We exploit the unique design of a repeated survey experiment among students in four countries to explore the stability of risk preferences in the context of the COVID-19 pandemic. Relative to a baseline before the pandemic, we find that self-assessed willingness to take risks decreased while the willingness to take risks in an incentivized lottery task increased, for the same sample of respondents. These findings suggest domain specificity of preferences that is partly reflected in the different measures.
    Keywords: stability of risk preferences, measurement of risk aversion, COVID-19
    JEL: D12 D91 G50
    Date: 2021–09
  9. By: Nicolas Soenen; Rudi Vander Vennet (-)
    Abstract: Using bank CDS spreads, we examine three types of determinants of Euro Area bank default risk in the period 2008-2019: bank characteristics related to new regulation, the bank-sovereign nexus and the monetary policy stance. We find that Basel 3 regulation improves the banks’ risk profile since higher capital ratios and more stable deposit funding contribute significantly to lower CDS spreads. We confirm the persistence of the bank-sovereign interconnectedness and find that sovereign default risk is transmitted to bank risk with an amplification factor. The ECB monetary policy stance is neutral with respect to bank risk, hence we find no evidence of perceived excessive risk-taking behavior.
    Keywords: bank default risk, CDS spreads, monetary policy, sovereign risk
    JEL: G21 G32 E52
    Date: 2021–11
  10. By: Lavorato, Mateus; Braga, Marcelo José
    Keywords: Risk and Uncertainty
    Date: 2021–08
  11. By: Nikitas Stamatopoulos; Guglielmo Mazzola; Stefan Woerner; William J. Zeng
    Abstract: We introduce a quantum algorithm to compute the market risk of financial derivatives. Previous work has shown that quantum amplitude estimation can accelerate derivative pricing quadratically in the target error and we extend this to a quadratic error scaling advantage in market risk computation. We show that employing quantum gradient estimation algorithms can deliver a further quadratic advantage in the number of the associated market sensitivities, usually called greeks. By numerically simulating the quantum gradient estimation algorithms on financial derivatives of practical interest, we demonstrate that not only can we successfully estimate the greeks in the examples studied, but that the resource requirements can be significantly lower in practice than what is expected by theoretical complexity bounds. This additional advantage in the computation of financial market risk lowers the estimated logical clock rate required for financial quantum advantage from Chakrabarti et al. [Quantum 5, 463 (2021)] by a factor of 50, from 50MHz to 1MHz, even for a modest number of greeks by industry standards (four). Moreover, we show that if we have access to enough resources, the quantum algorithm can be parallelized across 30 QPUs for the same overall runtime as the serial execution if the logical clock rate of each device is ~30kHz, same order of magnitude as the best current estimates of feasible target clock rates of around 10kHz. Throughout this work, we summarize and compare several different combinations of quantum and classical approaches that could be used for computing the market risk of financial derivatives.
    Date: 2021–11
  12. By: Chao Zhang; Zihao Zhang; Mihai Cucuringu; Stefan Zohren
    Abstract: We propose a universal end-to-end framework for portfolio optimization where asset distributions are directly obtained. The designed framework circumvents the traditional forecasting step and avoids the estimation of the covariance matrix, lifting the bottleneck for generalizing to a large amount of instruments. Our framework has the flexibility of optimizing various objective functions including Sharpe ratio, mean-variance trade-off etc. Further, we allow for short selling and study several constraints attached to objective functions. In particular, we consider cardinality, maximum position for individual instrument and leverage. These constraints are formulated into objective functions by utilizing several neural layers and gradient ascent can be adopted for optimization. To ensure the robustness of our framework, we test our methods on two datasets. Firstly, we look at a synthetic dataset where we demonstrate that weights obtained from our end-to-end approach are better than classical predictive methods. Secondly, we apply our framework on a real-life dataset with historical observations of hundreds of instruments with a testing period of more than 20 years.
    Date: 2021–11
  13. By: David Chen; Christian Friedrich
    Abstract: We examine the impact of the recently introduced Basel III countercyclical capital buffer (CCyB) on foreign lending activities of Canadian banks. Using panel data for the six largest Canadian banks and their foreign activities in up to 94 countries, we explore the variation in CCyB rates across countries to overcome the identification challenge associated with limited time-series evidence on the use of the CCyB in individual jurisdictions. Our main sample focuses on the period from 2013Q2 to 2019Q3, when CCyB rates experienced a prolonged tightening cycle. We show that in response to a 1-percentage-point tightening announcement in a foreign CCyB, the growth rate of cross-border lending between Canadian banks’ head offices and borrowers in CCyB-implementing countries decreases by between 12 and 17 percentage points. Most importantly, due to the CCyB’s unique reciprocity rule, which also subjects foreign banks to domestic regulation, the direction of this effect differs from that of other forms of foreign capital regulation that have been previously examined in the literature. When investigating the underlying transmission channels of a CCyB change, we find that, in particular, large banks are more able than small banks to shield their cross-border lending against the impact of foreign CCyB changes. Finally, when focusing on the loosening cycle in CCyB rates that emerged in early 2020, we show that our findings on the differential effects for large and small banks also carry over to the COVID-19 episode—a time when various jurisdictions rapidly released their CCyBs to stabilize their banks’ lending activities.
    Keywords: Credit risk management; Financial institutions; Financial stability; Financial system regulation and policies; International topics
    JEL: E32 F21 F32 G28
    Date: 2021–11
  14. By: Minseog Oh; Donggyu Kim
    Abstract: In this paper, we investigate the effect of the U.S.--China trade war on stock markets from a financial contagion perspective, based on high-frequency financial data. Specifically, to account for risk contagion between the U.S. and China stock markets, we develop a novel jump-diffusion process. For example, we consider three channels for volatility contagion--such as integrated volatility, positive jump variation, and negative jump variation--and each stock market is able to affect the other stock market as an overnight risk factor. We develop a quasi-maximum likelihood estimator for model parameters and establish its asymptotic properties. Furthermore, to identify contagion channels and test the existence of a structural break, we propose hypothesis test procedures. From the empirical study, we find evidence of financial contagion from the U.S. to China and evidence that the risk contagion channel has changed from integrated volatility to negative jump variation.
    Date: 2021–11
  15. By: Jan J. J. Groen; Adam I. Noble
    Abstract: Despite China’s tighter financial policies and the Evergrande troubles, Chinese financial stress measures have been remarkably stable around average levels. Chinese financial conditions, though, are affected by global markets, making it likely that low foreign financial stress conditions are blurring the state of Chinese financial markets. In this post, we parse out the domestic component of a Chinese financial stress measure to evaluate the downside risk to future economic activity.
    Keywords: financial conditions; growth-at-risk; China
    JEL: E2 G1
    Date: 2021–11–23
  16. By: Xin Sheng (Lord Ashcroft International Business School, Anglia Ruskin University, Chelmsford, United Kingdom); Won Joong Kim (Department of Economics, Konkuk University, Seoul, Republic of Korea); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: Utilising both high frequency (daily) and monthly data this study analyses the effects of oil price volatility on financial stress with various measures. The study places a special focus on comparing the pattern of these effects during the Great Recession period and the COVID-19 recession period. Using the local projection approach, the paper finds that oil price volatility has a positive and persistent effect on financial stress. However, the magnitude and the degree of persistency of oil price volatility impacts on financial stress are much greater for the Great Recession period than for the COVID-19 recession period. A possible explanation for this result would be that the COVID-19 is better thought of as a “natural disaster†in which companies in stress were not being mismanaged. Another explanation would be that active intervention by the government through monetary and fiscal channels reduces the sensitivity of financial instability to oil price volatility during the COVID-19 period.
    Keywords: Oil price volatility, financial stress index (FSI), local projection, impulse response, global financial crisis (GFC), COVID-19 recession
    JEL: G01 G10 Q41
    Date: 2021–12
  17. By: Philip Protter; Alejandra Quintos
    Abstract: Stopping times are used in applications to model random arrivals. A standard assumption in many models is that the stopping times are conditionally independent, given an underlying filtration. This is a widely useful assumption, but there are circumstances where it seems to be unnecessarily strong. We use a modified Cox construction along with the bivariate exponential introduced by Marshall & Olkin (1967) to create a family of stopping times, which are not necessarily conditionally independent, allowing for a positive probability for them to be equal. We indicate applications to modeling Covid-19 contagion (and epidemics in general), civil engineering, and to credit risk.
    Date: 2021–11
  18. By: Laura Auria (Bundesbank); Markus Bingmer (Bundesbank); Carlos Mateo Caicedo Graciano (Banque de France); Clémence Charavel (Banque de France); Sergio Gavilá (Banco de España); Alessandra Iannamorelli (Banca d'Italia); Aviram Levy (Banca d'Italia); Alfredo Maldonado (Banco de España); Florian Resch (Oesterreichische Nationalbank); Anna Maria Rossi (Banca d'Italia); Stephan Sauer (European Central Bank)
    Abstract: The in-house credit assessment systems (ICASs) developed by euro area national central banks (NCBs) are an important source of credit risk assessment within the Eurosystem collateral framework. They allow counterparties to mobilise as collateral the loans (credit claims) granted to non-financial corporations (NFCs). In this way, ICASs increase the usability of non-marketable credit claims that are normally not accepted as collateral in private market repo transactions, especially for small and medium-sized banks that lend primarily to small and medium-sized enterprises (SMEs). This ultimately leads not only to a widened collateral base and an improved transmission mechanism of monetary policy, but also to a lower reliance on external sources of credit risk assessment such as rating agencies. The importance of ICASs is exemplified by the collateral easing measures adopted in April 2020 in response to the coronavirus (COVID-19) crisis. The measures supported the greater use of credit claim collateral and, indirectly, increased the prevalence of ICASs as a source of collateral assessment. This paper analyses in detail the role of ICASs in the context of the Eurosystem’s credit operations, describing the relevant Eurosystem guidelines and requirements in terms of, among other factors, the estimation of default probabilities, the role of statistical models versus expert analysis, input data, validation analysis and performance monitoring. It then presents the main features of each of the ICASs currently accepted by the Eurosystem as credit assessment systems, highlighting similarities and differences.
    Keywords: credit assessments, credit risk models, credit claims, ratings, ICAS
    JEL: E58
    Date: 2021–11
  19. By: amri amamou, souhir
    Abstract: We investigate in this paper the evolution of the dynamic relationship between Covid-19 cases and cryptocurrency markets. Furthermore, we examine their sensitivity to the second wave period. Using a DCC-garch model, our findings show different sensitivities between cryptocurrency markets to the Covid-19 pandemic. Besides, we emphasize that the sensitivity of transaction volume in the cryptocurrency markets to the number of covid-19 cases is negatively and significantly affected by the second wave of the pandemic. Then, we underline a suspicious perception of the hedging power of the cryptocurrency market in the covid-19 period.
    Keywords: cryptocurrency markets, Covid-19, second wave period
    JEL: C1 C32 G15
    Date: 2021–11–27
  20. By: Diana Radu
    Abstract: Natural disasters have caused, and will continue to cause, significant losses in the EU Member States. Moreover, climate change is expected to amplify the frequency and intensity of most natural disasters. Governments step-in to cover the disasters-related costs such as emergency relief, recovery and reconstruction. Public authorities also act as insurer of last resort, in particular in those countries where insurance coverage is low. They make payments for legal commitments to cover the costs of disasters, and when there is a moral obligation to provide financial assistance. Natural disasters and climate change thus represent a real and increasing challenge for public finances, adding to fiscal sustainability issues such as a high debt level and an ageing population. There is little evidence on how EU Member States pre-arrange disaster financing and on past disasters financing. This discussion paper aims to provide an overview of relevant concepts for the design of a disaster risk financing strategy. It provides evidence from EU and Member States on disaster financing with a view to inform the debate on strengthening disaster financial resilience.
    JEL: Q54 Q58 H12 H60
    Date: 2021–10
  21. By: Lee, Yuanyao; Khanna, Madhu
    Keywords: Risk and Uncertainty, Resource /Energy Economics and Policy
    Date: 2021–08
  22. By: Jonathan Raimana Chan; Thomas Huckle; Antoine Jacquier; Aitor Muguruza
    Abstract: We develop a new analysis for portfolio optimisation with options, tackling the three fundamental issues with this problem: asymmetric options' distributions, high dimensionality and dependence structure. To do so, we propose a new dependency matrix, built upon conditional probabilities between options' payoffs, and show how it can be computed in closed form given a copula structure of the underlying asset prices. The empirical evidence we provide highlights that this approach is efficient, fast and easily scalable to large portfolios of (mixed) options.
    Date: 2021–11
  23. By: Mitchell, Olivia S.; Utkus, Stephen P.
    Abstract: Target date funds in corporate retirement plans grew from $5B in 2000 to $734B in 2018, partly because federal regulation sanctioned these as default investments in automatic enrollment plans. We show that adopters delegated pension investment decisions to fund managers selected by plan sponsors. Including these funds in retirement saving menus raised equity shares, boosted bond exposures, curtailed cash/company stock holdings, and reduced idiosyncratic risk. The adoption of low-cost target date funds may enhance retirement wealth by as much as 50 percent over a 30-year horizon.
    Keywords: automatic enrollment,pension,portfolio allocation,endorsement effect,default effect,retirement saving
    JEL: D12 D14 D91 G41 G51 J32
    Date: 2021

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