nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒10‒18
nineteen papers chosen by
Stan Miles
Thompson Rivers University

  1. A time-varying skewness model for Growth-at-Risk By Martin Iseringhausen
  2. Data-driven distributionally robust risk parity portfolio optimization By Giorgio Costa; Roy H. Kwon
  3. ETF Risk Models By Zura Kakushadze; Willie Yu
  4. Volatility indices and implied uncertainty measures of European government bond futures By Jaroslav Baran; Jan Voříšek
  5. The Impact of COVID-19 on Supply Chain Credit Risk By Senay Agca; John Birge; Zi'ang Wang; Jing Wu
  6. Artificial intelligence and systemic risk By Danielsson, Jon; Macrae, Robert; Uthemann, Andreas
  7. Risk-Taking and Tail Events Across Trading Institutions By Brice Corgnet; Camille Cornand; Nobuyuki Hanaki
  8. Intensity of preferences for bivariate risk apportionment By David Crainich; Louis Eeckhoudt; Olivier Le Courtois
  9. Dynamics of Price Volatility Spillover in the U.S. Cat_x001C_fish Market By Surathkal, Prasanna; Omana Sudhakaran, Pratheesh; Dey, Madan M.
  10. Learning from trees: A mixed approach to building early warning systems for systemic banking crises By Carmine Gabriele
  11. On the Stability of Risk Preferences: Measurement Matters By Joop Adema; Till Nikolka; Panu Poutvaara; Uwe Sunde; Joop Age Harm Adema
  12. Liquidity and tail-risk interdependencies in the euro area sovereign bond market By Daragh Clancy; Peter G. Dunne; Pasquale Filiani
  13. Black Scholes Model By Molintas, Dominique Trual
  14. Measuring food price volatility By Traore, Fousseini; Diop, Insa
  15. Bitcoin and traditional currencies during the Covid-19 pandemic period By Chu, Meifen
  16. Cross-hedging with Agricultural Commodities: A Copula-GARCH Approach By Aglasan, Serkan; Wu, Shenan; Goodwin, Barry K.
  17. Evolutionary Foundation for Heterogeneity in Risk Aversion By Heller, Yuval; NEHAMA, Ilan
  18. Shocks and Stability of Risk Preferences By Holden, Stein T.; Tilahun, Mesfin
  19. The time-varying evolution of inflation risks By Korobilis, Dimitris; Landau, Bettina; Musso, Alberto; Phella, Anthoulla

  1. By: Martin Iseringhausen (ESM)
    Abstract: This paper studies macroeconomic risks in a panel of advanced economies based on a stochastic volatility model in which macro-financial conditions shape the predictive growth distribution. We find sizable time variation in the skewness of these distributions, conditional on the macro-financial environment. Tightening financial conditions signal increasing downside risk in the short term, but this link reverses at longer horizons. When forecasting downside risk, the proposed model, on average, outperforms existing approaches based on quantile regression and a GARCH model, especially at short horizons. In forecasting upside risk, it improves the average accuracy across all horizons up to four quarters ahead. The suggested approach can inform policy makers' assessment of macro-financial vulnerabilities by providing a timely signal of shifting risks and a quantification of their magnitude.
    Keywords: Bayesian analysis, downside risk, macro-financial linkages, time variation
    JEL: C11 C23 C53 E44
    Date: 2021–06–10
  2. By: Giorgio Costa; Roy H. Kwon
    Abstract: We propose a distributionally robust formulation of the traditional risk parity portfolio optimization problem. Distributional robustness is introduced by targeting the discrete probabilities attached to each observation used during parameter estimation. Instead of assuming that all observations are equally likely, we consider an ambiguity set that provides us with the flexibility to find the most adversarial probability distribution based on the investor's desired degree of robustness. This allows us to derive robust estimates to parametrize the distribution of asset returns without having to impose any particular structure on the data. The resulting distributionally robust optimization problem is a constrained convex-concave minimax problem. Our approach is financially meaningful and attempts to attain full risk diversification with respect to the worst-case instance of the portfolio risk measure. We propose a novel algorithmic approach to solve this minimax problem, which blends projected gradient ascent with sequential convex programming. By design, this algorithm is highly flexible and allows the user to choose among alternative statistical distance measures to define the ambiguity set. Moreover, the algorithm is highly tractable and scalable. Our numerical experiments suggest that a distributionally robust risk parity portfolio can yield a higher risk-adjusted rate of return when compared against the nominal portfolio.
    Date: 2021–10
  3. By: Zura Kakushadze; Willie Yu
    Abstract: We discuss how to build ETF risk models. Our approach anchors on i) first building a multilevel (non-)binary classification/taxonomy for ETFs, which is utilized in order to define the risk factors, and ii) then building the risk models based on these risk factors by utilizing the heterotic risk model construction of (for binary classifications) or general risk model construction of (for non-binary classifications). We discuss how to build an ETF taxonomy using ETF constituent data. A multilevel ETF taxonomy can also be constructed by appropriately augmenting and expanding well-built and granular third-party single-level ETF groupings.
    Date: 2021–10
  4. By: Jaroslav Baran (ESM); Jan Voříšek (independent researcher)
    Abstract: Implied volatility and other forward-looking measures of option-implied uncertainty help investors carefully evaluate market sentiment and expectations. We construct several measures of implied uncertainty in European government bond futures. In the first part, we create new volatility indices, which reflect market pricing of subsequently realised volatility of underlying bond futures. We express volatility indices in both price and basis points, the latter being more intuitive to interpret; we document their empirical properties, and discuss their possible applications. In the second part, we fit the volatility smile using the SABR model, and recover option-implied probability distribution of possible outcomes of bond futures prices. We analyse shapes of the implied distribution, track its quantiles over time, calculate its skewness and kurtosis, and infer probabilities of a given upside or downside move in the price of bond futures or in the yield of their underlying CTD bond. We illustrate these complementary measures throughout the note using Bund futures as an example, and show the results for Schatz, Bobl, OAT, and BTP futures in the annex. Such forward-looking measures help market participants quantify the degree of future market uncertainty and thoroughly assess what risks are priced in.
    Keywords: bond futures, market expectations, options, probability density function, SABR, VIX, volatility index
    JEL: C13 G13 G14 G17
    Date: 2020–05–13
  5. By: Senay Agca (George Washington University); John Birge (University of Chicago); Zi'ang Wang (Chinese University of Hong Kong); Jing Wu (Chinese University of Hong Kong)
    Abstract: Global supply chains expose firms to multi-regional risks, but also provide benefits by creating a buffer against local shocks. The COVID-19 pandemic and its differential impact on different parts of the world provide an opportunity for insight into supply chain credit risk, and how operational and structural characteristics of global supply chains affect this risk. In this paper, we examine supply chain credit risk during different phases of the COVID-19 pandemic by focusing on Credit Default Swap (CDS) spreads and US-China supply chain links. CDS spreads reflect both the probability of default and expected loss given default, and are available with daily frequency, which allows the assessment of supply chain partners' credit risk in a timely manner. We find that CDS spreads for firms with China supply chain partners increase with the economic shutdown in China during the pandemic, and the spreads go down when the economic activity resumed with the re-opening in China. We consider Swift, Even Flow (SEF) and Social Network Theories (SNT) within our context. Supporting SEF theory, we find that the impact of pandemic-related disruptions to even flow of goods and materials reflected in supply chain credit risk is mitigated for firms with lower inventory turnover and those with better ability to work with longer lead times and operating cycles. Examining supply chain structural characteristics through SNT reveals that spatial and horizontal complexity, as well as network centrality (degree, closeness, betweenness, information) mitigate the impact of supply chain vulnerabilities on supply chain credit risk.
    Keywords: Supply Chains, Credit Risk, CDS, COVID-19, Pandemic
    JEL: E21 E51 F23 G12 G14 G23 G32 L11
    Date: 2021
  6. By: Danielsson, Jon; Macrae, Robert; Uthemann, Andreas
    Abstract: Artificial intelligence (AI) is rapidly changing how the financial system is operated, taking over core functions for both cost savings and operational efficiency reasons. AI will assist both risk managers and the financial authorities. However, it can destabilize the financial system, creating new tail risks and amplifying existing ones due to procyclicality, unknown-unknowns, the need for trust, and optimization against the system.
    Keywords: ES/K002309/1; EP/P031730/1; UKRI fund
    JEL: F3 G3
    Date: 2021–08–28
  7. By: Brice Corgnet (emlyon business school); Camille Cornand (GATE Lyon Saint-Étienne - Groupe d'analyse et de théorie économique - CNRS - Centre National de la Recherche Scientifique - Université de Lyon - UJM - Université Jean Monnet [Saint-Étienne] - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UL2 - Université Lumière - Lyon 2 - ENS Lyon - École normale supérieure - Lyon); Nobuyuki Hanaki (Osaka University [Osaka])
    Abstract: We study the reaction of investors to tail events across trading institutions. We conduct experiments in which investors bid on a financial asset that delivers a small positive reward in more than 99% of the cases and a large loss otherwise. The baseline treatment uses a repeated BDM mechanism whereas the market treatment replaces the uniform draw of the BDM mechanism by a uniform draw over the bids of the other participants. Our design is such that bids should not differ across treatments in normal times while allowing for potential differences to emerge after tail events have occurred. We find that markets tend to exacerbate the reaction of investors to tail losses and we attribute this effect to emotions.
    Keywords: Tail events,trading institutions,experimental finance,emotions and risk
    Date: 2021–09–29
  8. By: David Crainich (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique); Louis Eeckhoudt; Olivier Le Courtois
    Abstract: Bivariate risk apportionment is the preference for dispersing risks associated with two aspects of individuals' well-being into different states of the world. In this paper, we propose an intensity measure of this preference by extending to the bivariate case the concept of marginal rate of substitution between risks of different orders introduced in the univariate case by Liu and Meyer (2013). We show that the intensity measure of the preference for bivariate risk apportionment is characterized by bivariate risk attitudes in the sense of Ross. The usefulness of our measures to understand economic choices is illustrated by the analysis of two specific decisions: savings under environmental risk and medical treatment in the presence of diagnostic risks.
    Keywords: Bivariate utility function,Increase in bivariate risks,Risk apportionment,Comparative risk aversion,Ross risk aversion
    Date: 2020–05
  9. By: Surathkal, Prasanna; Omana Sudhakaran, Pratheesh; Dey, Madan M.
    Keywords: Agribusiness, Marketing, Risk and Uncertainty
    Date: 2021–08
  10. By: Carmine Gabriele (ESM)
    Abstract: Banking crises can be extremely costly. The early detection of vulnerabilities can help prevent or mitigate those costs. We develop an early warning model of systemic banking crises that combines regression tree technology with a statistical algorithm (CRAGGING) to improve its accuracy and overcome the drawbacks of previously used models. Our model has a large set of desirable features. It provides endogenously-determined critical thresholds for a set of useful indicators, presented in the intuitive form of a decision tree structure. Our framework takes into account the conditional relations between various indicators when setting early warning thresholds. This facilitates the production of accurate early warning signals as compared to the signals from a logit model and from a standard regression tree. Our model also suggests that high credit aggregates, both in terms of volume and as compared to a long-term trend, as well as low market risk perception, are amongst the most important indicators for predicting the build-up of vulnerabilities in the banking sector.
    Keywords: Early warning system, banking crises, regression tree, ensemble methods
    JEL: C40 G01 G21 E44 F37
    Date: 2019–10–30
  11. By: Joop Adema; Till Nikolka; Panu Poutvaara; Uwe Sunde; Joop Age Harm Adema
    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
  12. By: Daragh Clancy (ESM); Peter G. Dunne (Central Bank of Ireland); Pasquale Filiani (Central Bank of Ireland)
    Abstract: The likelihood of severe contractions in an asset's liquidity can feed back to the ex-ante risks faced by the individual providers of such liquidity. These self-reinforcing effects can spread to other assets through informational externalities and hedging relations. We explore whether such interdependencies play a role in amplifying tensions in European sovereign bond markets and are a source of cross-market spillovers. Using high-frequency data from the inter-dealer market, we find significant own- and cross-market effects that amplify liquidity contractions in the Italian and Spanish bond markets during times of heightened risk. The German Bund's safe-haven status exacerbates these amplification effects. We provide evidence of a post-crisis dampening of cross-market effects following crisis-era changes to euro area policies and institutional architecture. We identify a structural break in Italy's cross-market conditional correlation during rising political tensions in 2018, which significantly reduced liquidity. Overall, our findings demonstrate potential for the provision of liquidity across sovereign markets to be vulnerable to sudden fractures, with possible implications for euro area economic and financial stability.
    Keywords: Liquidity; Tail risks; Feedback loops; Spillovers
    JEL: G01 G15 F36
    Date: 2019–11–08
  13. By: Molintas, Dominique Trual
    Abstract: Black-Scholes is a pricing model applied as the reference in the derivation of fair price—or the theoretical value for a call or a put option. A call is defined as the decision to buy actual stock at a set price, defined as the strike price; and by a scheduled expiration date. A put option is defined as the opportunity contract providing the owner the right but not the obligation, to sell an exact amount of underlying security at a stated price within a specific time frame. The call or put option in the Black Scholes model is based on six variables: strike price and underlying stock price, time and type of option, volatility and risk-free rate. The application of the model assumes that these stock or securities recognise its corresponding custom derivatives held to expiration. It is sufficient to state that the Black-Scholes treats a call option as an informal agreement defined as a forward contract with expectation to deliver stock at a contractual price, otherwise indicative in the strike price. Typically the Black-Scholes model is utilised to price European options (y p) that represents investment options in a selection of financial assets earning risk-free interest rates. In strictness, the model presents the option price as a function of stock price volatility: High volatility is tantamount a high premium price on the option.
    Keywords: Black-Scholes model, strike price, volatility , risk-free rate, stock price volatility
    JEL: E47 G12
    Date: 2021–04–17
  14. By: Traore, Fousseini; Diop, Insa
    Abstract: Over the past two decades, the prices of agricultural commodities have experienced large and unpredictable fluctuations that have attracted the attention of researchers, policymakers and the media to better understand the mechanisms that govern this phenomenon. It is therefore important to acquire basic tools to assess the level of price volatility to warn of abnormal movements. The main objective of this technical note is to provide an overview of this literature in constant evolution, and tools for measuring food price volatility. The tools developed in this technical note help understand the complexity of measuring volatility and the caution required in their use. Thus, the application of these tools requires their adaptation to the nature of the data generating process and the use of appropriate tests and criteria in order to choose the best approach.
    Keywords: food prices, price volatility, tools, agricultural products, commodities, food price volatility,
    Date: 2021
  15. By: Chu, Meifen
    Abstract: The objective of this study is to examine the movement of Bitcoin and the traditional currencies (USD, EURO, GBP and CNY) and the Bitcoin’s hedging of the traditional currencies. First, this paper observes the Bitcoin and four traditional currency exchange series: the USD, EURO, GBP and CNY. Second, it examines the fluctuation patterns of each series by using wavelet transform analysis, Third, a wavelet coherence analysis is applied to examine the interdependence between the Bitcoin and the four traditional currencies. The phase pattern analysis results indicate that the Bitcoin may not act as a hedging currency to replace the traditional currencies during the Covid-19 crisis. Another interesting result shows the rapid increasing number of the World Covid-19 Deaths (CovidDeaths) may not be the critical reason for the hyper price of the Bitcoin. The massive quantitative easing (QE) may be considered as the key reason for the soar-up of the Bitcoin price.
    Keywords: Bitcoin, Traditional currencies, Covid-19, CovidDeaths, Hedging feature, Wavelet Analysis
    JEL: C1
    Date: 2021–04–05
  16. By: Aglasan, Serkan; Wu, Shenan; Goodwin, Barry K.
    Keywords: Agricultural Finance, Agribusiness, Risk and Uncertainty
    Date: 2021–08
  17. By: Heller, Yuval; NEHAMA, Ilan
    Abstract: We examine evolutionary basis for risk aversion with respect to aggregate risk. We study populations in which agents face choices between aggregate risk and idiosyncratic risk. We show that the choices that maximize the long-run growth rate are induced by a heterogeneous population in which the least and most risk averse agents are indifferent between aggregate risk and obtaining its linear and harmonic mean for sure, respectively. Moreover, an approximately optimal behavior can be induced by a simple distribution according to which all agents have constant relative risk aversion, and the coefficient of relative risk aversion is uniformly distributed between zero and two.
    Keywords: Evolution of preferences, risk interdependence, long-run growth rate.
    JEL: D81
    Date: 2021–10–13
  18. By: Holden, Stein T. (Centre for Land Tenure Studies, Norwegian University of Life Sciences); Tilahun, Mesfin (Centre for Land Tenure Studies, Norwegian University of Life Sciences)
    Abstract: While economists in the past tended to assume that individual preferences, including risk preferences, are stable over time, a recent literature has developed that indicate that risk preferences respond to shocks. This paper combines survey data and field experiments with three different tools that facilitated elicitation of dis-aggregated measures of risk preferences, including utility curvature, probability weighting and loss aversion. By treating the recent shocks as natural experiments, the study assessed the sensitivity of each of these risk preference measures to the recent idiosyncratic and covariate (drought) shocks among a sample of resource-poor young adults living in a semi-arid rural environment in Sub-Saharan Africa. The results show that the dis-aggregated risk preference measures revealed substantial shock effects that were undetected when relying on a tool that elicited only one single measure of risk tolerance. Both the timing and covariate nature of the shocks affected the dis-aggregated measures of risk preferences differently, pointing towards the need for further studies of this kind in different contexts.
    Keywords: Covariate shocks; Idiosyncratic shocks; Stability of risk preference parameters; Field experiment; Ethiopia
    JEL: C93 D81
    Date: 2021–10–14
  19. By: Korobilis, Dimitris; Landau, Bettina; Musso, Alberto; Phella, Anthoulla
    Abstract: This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting inflation with the ability of quantile regression to model flexibly the whole distribution of inflation. In order to make our approach accessible and empirically relevant for forecasting, we derive an efficient Gibbs sampler by transforming the state-space form of the TVP quantile regression into an equivalent high-dimensional regression form. An application of this methodology points to a good forecasting performance of quantile regressions with TVPs augmented with specific credit and money-based indicators for the prediction of the conditional distribution of inflation in the euro area, both in the short and longer run, and specifically for tail risks. JEL Classification: C11, C22, C52, C53, C55, E31, E37, E51
    Keywords: Bayesian shrinkage, euro area, Horseshoe, inflation tail risks, MCMC, quantile regression, time-varying parameters
    Date: 2021–10

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