nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒04‒06
eighteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Tail Risk Measurement In Crypto-Asset Markets By Daniel Felix Ahelegbey; Paolo Giudici; Fatemeh Mojtahedi
  2. Risk Spillover between Bitcoin and Conventional Financial Markets: An Expectile-Based Approach By Yue-Jun Zhang; Elie Bouri; Rangan Gupta
  3. Convex Risk Measures based on Divergence By Paul Dommel; Alois Pichler
  4. Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds * By Elena Dumitrescu; Sullivan Hué; Christophe Hurlin; Sessi Tokpavi
  5. Non-asymptotic rates for the estimation of risk measures By Daniel Bartl; Ludovic Tangpi
  6. Spatial Dependence and Space-Time Trend in Extreme Events By Einmahl, John; Ferreira, Ana; de Haan, Laurens; Neves, C.; Zhou, C.
  7. Managing the Downside of Active and Passive Strategies: Convexity and Fragilities By Raphaël Douady
  8. Risk and Financial Management Article Systemic Risk Indicators Based on Nonlinear PolyModel By Xingxing Ye; Raphaël Douady
  9. Forecasting Stock Market Recessions in the US: Predictive Modeling using Different Identification Approaches By Felix Haase; Matthias Neuenkirch
  10. Network-Based Measures of Systemic Risk in Korea By Jaewon Choi; Jieun Lee
  11. Cryptocurrency Trading: A Comprehensive Survey By Fan Fang; Carmine Ventre; Michail Basios; Hoiliong Kong; Leslie Kanthan; Lingbo Li; David Martinez-Regoband; Fan Wu
  12. A New Volatility Model: GQARCH-Ito Model By Yuan, Huiling; Zhou, Yong; Xu, Lu; Sun, Yulei; Cui, Xiangyu
  13. Higher-Order Income Risk over the Business Cycle By Christopher Busch; Alexander Ludwig
  14. Risk exposure and risk awareness as a factor of farms resilience in Poland By Adam Wąs; Piotr Sulewski; Paweł Kobus
  15. Measuring the Cost of Regulation: A Text-Based Approach By Charles W. Calomiris; Harry Mamaysky; Ruoke Yang
  16. Optimally Solving Banks' Legacy Problems By Anatoli Segura; Javier Suarez
  17. Philosophical issues related to risks and values By Kinouchi, Renato
  18. Stablecoins as a crypto safe haven? Not all of them! By Baumöhl, Eduard; Vyrost, Tomas

  1. By: Daniel Felix Ahelegbey (Università di Pavia); Paolo Giudici (Università di Pavia); Fatemeh Mojtahedi (Sari Agricultural Sciences and Natural Resources University)
    Abstract: The paper examines the relationships among market assets during stressful times, using two recently proposed econometric modeling techniques for tail risk measurement: the extreme downside hedge (EDH) and the extreme downside correlation (EDC). We extend both measures taking into account the sensitivity of asset’s return to innovations not only from the overall market index, but also from its components, by means of network modelling. Applying our proposal to the cryptocurrencies market, we find that crypto-assets can be clustered in two groups: speculative assets, such as Bitcoin, which are mainly “givers” of tail contagion; and technical assets, such as Ethereum, which are mainly “receivers” of contagion.
    Keywords: Crypto-assets, Extreme downside hedge, Extreme downside correlation, Network Models, Systematic risk, Systemic risk.
    JEL: C31 C58 G01 G12
    Date: 2020–03
  2. By: Yue-Jun Zhang (Business School, Hunan University, Changsha 410082, China; Center for Resource and Environmental Management, Hunan University, Changsha 410082, China); Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: We challenge the existing literature that points to the detachment of Bitcoin from the global financial system. We use daily data from August 17, 2011 - February 14, 2020 and apply a risk spillover approach based on expectiles. Results show reasonable evidence to imply the existence of downside risk spillover between Bitcoin and four assets (equities, bonds, currencies, and commodities), which seems to be time dependent. Our main findings have implications for participants in both the Bitcoin and the traditional financial markets for the sake of asset allocation, and risk management. For policy makers, our findings suggest that Bitcoin should be monitored carefully for the sake of financial stability.
    Keywords: Bitcoin, financial markets, asset classes, downside risk spillover, expectile VaR, CAR-ARCHE
    Date: 2020–03
  3. By: Paul Dommel; Alois Pichler
    Abstract: Risk measures connect probability theory or statistics to optimization, particularly to convex optimization. They are nowadays standard in applications of finance and in insurance involving risk aversion. This paper investigates a wide class of risk measures on Orlicz spaces. The characterizing function describes the decision maker's risk assessment towards increasing losses. We link the risk measures to a crucial formula developed by Rockafellar for the Average Value-at-Risk based on convex duality, which is fundamental in corresponding optimization problems. We characterize the dual and provide complementary representations.
    Date: 2020–03
  4. By: Elena Dumitrescu; Sullivan Hué; Christophe Hurlin (University of Orleans - LEO); Sessi Tokpavi (LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique)
    Abstract: Decision trees and related ensemble methods like random forest are state-of-the-art tools in the field of machine learning for credit scoring. Although they are shown to outperform logistic regression, they lack interpretability and this drastically reduces their use in the credit risk management industry, where decision-makers and regulators need transparent score functions. This paper proposes to get the best of both worlds, introducing a new, simple and interpretable credit scoring method which uses information from decision trees to improve the performance of logistic regression. Formally, rules extracted from various short-depth decision trees built with couples of predictive variables are used as predictors in a penalized or regularized logistic regression. By modeling such univariate and bivariate threshold effects, we achieve significant improvement in model performance for the logistic regression while preserving its simple interpretation. Applications using simulated and four real credit defaults datasets show that our new method outperforms traditional logistic regressions. Moreover, it compares competitively to random forest, while providing an interpretable scoring function. JEL Classification: G10 C25, C53
    Keywords: Credit scoring,Machine Learning,Risk management,Interpretability,Econometrics
    Date: 2020–03–13
  5. By: Daniel Bartl; Ludovic Tangpi
    Abstract: Consider the problem of computing the riskiness $\rho(F(S))$ of a financial position $F$ written on the underlying $S$ with respect to a general law invariant risk measure $\rho$; for instance, $\rho$ can be the average value at risk. In practice the true distribution of $S$ is typically unknown and one needs to resort to historical data for the computation. In this article we investigate rates of convergence of $\rho(F(S_N))$ to $\rho(F(S))$, where $S_N$ is distributed as the empirical measure of $S$ with $N$ observations. We provide (sharp) non-asymptotic rates for both the deviation probability and the expectation of the estimation error. Our framework further allows for hedging, and the convergence rates we obtain depend neither on the dimension of the underlying stocks nor on the number of options available for trading.
    Date: 2020–03
  6. By: Einmahl, John (Tilburg University, Center For Economic Research); Ferreira, Ana; de Haan, Laurens; Neves, C.; Zhou, C.
    Abstract: The statistical theory of extremes is extended to observations that are non-stationary and not indepen- dent. The non-stationarity over time and space is controlled via the scedasis (tail scale) in the marginal distributions. Spatial dependence stems from multivariate extreme value theory. We establish asymptotic theory for both the weighted sequential tail empirical process and the weighted tail quantile process based on all observations, taken over time and space. The results yield two statistical tests for homoscedastic- ity in the tail, one in space and one in time. Further, we show that the common extreme value index can be estimated via a pseudo-maximum likelihood procedure based on pooling all (non-stationary and dependent) observations. Our leading example and application is rainfall in Northern Germany.
    Keywords: Multivariate extreme value statistics; non-identical distributions; sequential tail empirical process; testing
    JEL: C12 C13 C14
    Date: 2020
  7. By: Raphaël Douady (CES - Centre d'économie de la Sorbonne - CNRS - Centre National de la Recherche Scientifique - UP1 - Université Panthéon-Sorbonne)
    Abstract: Question of the day: how to manage a large (or small) portfolio in low interest rate conditions, while equity markets bear significant draw-down risk? More generally, how to build an "antifragile" portfolio that can weather the most extreme market scenarios without impacting long-term performances? Do active strategies systematically create or increase already existing market instabilities? By analyzing in depth markets behavior during past speculative bubbles and credit crises, we aim at addressing these questions. Our goal is to describe as faithfully as possible the major mechanisms at stake, avoiding the trap of mapping the complexity of financial markets into a single mathematical model, which would necessarily be wrong at some point. Starting from Minsky's "Financial Instability Hypothesis", we try to disentangle the complex relation between dynamics and randomness, including the presence of "fat tails". We provide methods to monitor the evolving probability of a forthcoming crisis through the measurement of "market instability". Scalable investment strategies result from the application of these methods. 2
    Date: 2019–11–01
  8. By: Xingxing Ye; Raphaël Douady (CES - Centre d'économie de la Sorbonne - CNRS - Centre National de la Recherche Scientifique - UP1 - Université Panthéon-Sorbonne)
    Abstract: The global financial market has become extremely interconnected as it demonstrates strong nonlinear contagion in times of crisis. As a result, it is necessary to measure financial systemic risk in a comprehensive and nonlinear approach. By establishing a large set of risk factors as the main bones of the financial market network and applying nonlinear factor analysis in the form of so-called PolyModel, this paper proposes two systemic risk indicators that can prognosticate the advent and trace the development of financial crises. Through financial network analysis, theoretical simulation, empirical data analysis and final validation, we argue that the indicators suggested in this paper are proved to be very effective in forecasting and tracing the financial crises from 1998 to 2017. The economic benefit of the indicator is evidenced by the enhancement of a protective put/covered call strategy on major stock markets.
    Keywords: validation,PolyModel,nonlinear regression,network,financial indicator,systemic risk crisis,Indicators,Poly-Model,Systemic Risk,Nonlinear
    Date: 2019–03
  9. By: Felix Haase; Matthias Neuenkirch
    Abstract: Stock market recessions are often early warning signals for financial or economic crises. Hence, forecasting bear markets is important for investors, policymakers, and economic agents in general. In our two-step procedure, we first identify stock market regimes in the US using three different techniques (Markov-switching models, dating rules, and a naïve moving average). Second, we predict recessions in the S&P 500 with the help of several modeling approaches, utilizing the information of 92 macro-financial variables. Our results suggest that several variables are suitable for forecasting recessions in stock markets in-sample and out-of-sample. Our early warning models for the US equity market, in particular those using principal components to aggregate the information in the macro-financial variables, provide a statistical improvement over several benchmarks. In addition, these generate economic value by boosting returns, improving the sharp ratio and the omega, and substantially reducing drawdowns.
    Keywords: Dating Algorithms, Markov-Switching Models, Predictions, Principal Component Analysis, Specific-to-General Approach, Stock Market Recessions
    JEL: C53 G11 G17
    Date: 2020
  10. By: Jaewon Choi (Gies College of Business, University of Illinois/College of Business, Yonsei University); Jieun Lee (Economic Research Institute, Bank of Korea)
    Abstract: We estimate systemic risk in the Korean economy using the econometric measures of commonality and connectedness applied to stock returns. To assess potential systemic risk concerns arising from the high concentration of the economy in large business groups and a few export-oriented sectors, we perform three levels of estimation using individual stocks, business groups, and industry returns. Our results show that the measures perform well over our sample period by indicating heightened levels of commonality and interconnectedness during crisis periods. In out-of-sample tests, we show that the measures can predict future losses in the stock market during the crises. We also provide the recent readings of our measures, both at the market, chaebol, and industry levels. The measures indicate systemic risk is currently not a major concern in Korea, as they tend to be at the lowest level since 1998. Systemic risk within-chaebols or within-industries overall has not significantly increased in the recent sub-period. In contrast, commonality within the finance industry has not subsided, which we interpret as capturing the interconnectedness endemic to the finance industry, rather than indicating a heightened systemic risk within the banking sector.
    Keywords: Systemic risk, Network analysis, Korean economy
    JEL: G11 G14 G23
    Date: 2020–03–26
  11. By: Fan Fang; Carmine Ventre; Michail Basios; Hoiliong Kong; Leslie Kanthan; Lingbo Li; David Martinez-Regoband; Fan Wu
    Abstract: Since the inception of cryptocurrencies, an increasing number of financial institutions are gettinginvolved in cryptocurrency trading. It is therefore important to summarise existing research papersand results on cryptocurrency trading. This paper provides a comprehensive survey of cryptocurrencytrading research, by covering 118 research papers on various aspects of cryptocurrency trading (e.g.,cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return,crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper alsoanalyses datasets, research trends and distribution among research objects (contents/properties) andtechnologies, concluding with promising opportunities in cryptocurrency trading
    Date: 2020–03
  12. By: Yuan, Huiling; Zhou, Yong; Xu, Lu; Sun, Yulei; Cui, Xiangyu
    Abstract: Volatility asymmetry is a hot topic in high-frequency financial market. In this paper, we propose a new econometric model, which could describe volatility asymmetry based on high-frequency historical data and low-frequency historical data. After providing the quasi-maximum likelihood estimators for the parameters, we establish their asymptotic properties. We also conduct a series of simulation studies to check the finite sample performance and volatility forecasting performance of the proposed methodologies. And an empirical application is demonstrated that the new model has stronger volatility prediction power than GARCH-It\^{o} model in the literature.
    Date: 2020–03–27
  13. By: Christopher Busch; Alexander Ludwig
    Abstract: We extend the canonical income process with persistent and transitory risk to shock distributions with left-skewness and excess kurtosis, to which we refer as higher-order risk. We estimate our extended income process by GMM for household data from the United States. We find countercyclical variance and procyclical skewness of persistent shocks. All shock distributions are highly leptokurtic. The existing tax and transfer system reduces dispersion and left-skewness of shocks. We then show that in a standard incomplete-markets life-cycle model, first, higher-order risk has sizable welfare implications, which depend crucially on risk attitudes of households; second, higher-order risk matters quantitatively for the welfare costs of cyclical idiosyncratic risk; third, higher-order risk has non-trivial implications for the degree of self-insurance against both transitory and persistent shocks.
    Keywords: labor income risk, business cycle, GMM estimation, skewness, persistent and transitory income shocks, risk attitudes, life-cycle model
    JEL: D31 E24 E32 H31 J31
    Date: 2020–03
  14. By: Adam Wąs; Piotr Sulewski; Paweł Kobus
    Keywords: Farm Management
    Date: 2020–03–28
  15. By: Charles W. Calomiris; Harry Mamaysky; Ruoke Yang
    Abstract: We derive a measure of firm-level regulatory costs from the text of corporate earnings calls. We then use this measure to study the effect of regulation on companies’ operating fundamentals and cost of capital. We find that higher regulatory cost results in slower sales growth, an effect which is mitigated for large firms. Furthermore, we find a one-standard deviation increase in our preferred measure of regulatory cost is associated with an increase in firms’ cost of capital of close to 3% per year. These findings suggest that regulatory risk is a major cost to firms, but the largest firms are able to manage that risk better.
    JEL: G18 G38 K2 L51
    Date: 2020–03
  16. By: Anatoli Segura (Bank of Italy); Javier Suarez (CEMFI, Centro de Estudios Monetarios y Financieros)
    Abstract: We characterize policy interventions directed to minimize the cost to the deposit guarantee scheme and the taxpayers of banks with legacy problems. Non-performing loans (NPLs) with low and risky returns create a debt overhang that induces bank owners to forego profitable lending opportunities. NPL disposal requirements can restore the incentives to undertake new lending but, as they force bank owners to absorb losses, can also make them prefer the bank being resolved. For severe legacy problems, combining NPL disposal requirements with positive transfers is optimal and involves no conflict between minimizing the cost to the authority and maximizing overall surplus.
    Keywords: Non performing loans, deposit insurance, debt overhang, optimal intervention, state aid.
    JEL: G01 G20 G28
    Date: 2019–04
  17. By: Kinouchi, Renato
    Abstract: This paper begins with the assumption that the concept of risk implies an entanglement between facts and values. This is not an arbitrary assumption since it can directly be deduced from the standard notion of risk. The value-ladenness of risk raises at least two further issues: the first one concerns the scales adopted to evaluate the severity of risks; the second concerns the commensurability/comparability of risks to human health and the environment. Some additional light is shed on those issues whether the models used in risk analysis were understood as fictions limited by the values that they can include. From this point of view, controversies on the limited scope of standard risk assessments are not only descriptive but also evaluative.
    Keywords: commensurability; comparability; fiction; models; risk; values
    JEL: F3 G3
    Date: 2018–12–01
  18. By: Baumöhl, Eduard; Vyrost, Tomas
    Abstract: We test the safe haven properties of the largest stablecoins (USDT, USDC, TUSD, PAX, DAI, GUSD) against the standard “nonstable” coins (BTC, ETH, XRP, BCH, LTC). Our dataset comprises high-frequency 1-minute data calculated as volume-weighted averages across 18 exchanges where these cryptocurrencies are traded, thus capturing the entire price movement around the world. Using a quantile coherency cross-spectral measure, we find that only TUSD, PAX, and GUSD can serve as safe havens.
    Keywords: cryptocurrencies,stablecoins,quantile dependence,cross-spectral analysis,diversification,safe haven
    JEL: G11 G15 F31
    Date: 2020

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