nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒12‒20
24 papers chosen by
Stan Miles
Thompson Rivers University

  1. A Factor Model for Cryptocurrency Returns By Daniele Bianchi; Mykola Babiak
  2. The role of systemic risk spillovers in the transmission of Euro Area monetary policy By Skouralis, Alexandros
  3. Portfolio optimization with idiosyncratic and systemic risks for financial networks By Yajie Yang; Longfeng Zhao; Lin Chen; Chao Wang; Jihui Han
  4. A transformer-based model for default prediction in mid-cap corporate markets By Kamesh Korangi; Christophe Mues; Cristi\'an Bravo
  5. The Parameter Sensitivities of a Jump-diffusion Process in Basic Credit Risk Analysis By Bin Xie; Weiping Li
  6. A Lifecycle Approach to Insurance Solvency By Yuechen Dai; Tonghui Xu
  7. A General Surplus Decomposition Principle in Life Insurance By Julian Jetses; Marcus C. Christiansen
  8. A CBA of APC: analysing approaches to procyclicality reduction in CCP initial margin models By Murphy, David; Vause, Nicholas
  9. Mesoscopic Structure of the Stock Market and Portfolio Optimization By Sebastiano Michele Zema; Giorgio Fagiolo; Tiziano Squartini; Diego Garlaschelli
  10. Risk measures beyond frictionless markets By Maria Arduca; Cosimo Munari
  11. Optimal index insurance and basis risk decomposition: an application to Kenya By Matthieu Stigler; David Lobell
  12. On a Markovian game model for competitive insurance pricing By Claire Mouminoux; Christophe Dutang; Stéphane Loisel; Hansjoerg Albrecher
  13. M&A and Cybersecurity Risk: Empirical Evidence By Gabriele Lattanzio; Jerome Taillard
  14. Macroprudential Policy, Bank Competition and Bank Risk in East Asia By E Philip Davis; Ka Kei Chan; Dilruba Karim
  15. Precautionary motives with multiple instruments By Heinzel, Christoph; Peter, Richard
  16. Gene-Edited or Genetically Modified Food? the Impacts of Risk and Ambiguity on Chinese Consumers’ Willingness to Pay By Yu, Jianyu
  17. Sovereign Risk and Financial Risk By Simon Gilchrist; Bin Wei; Vivian Z. Yue; Egon Zakrajšek
  18. Risk Taking and Skewness Seeking Behavior in a Demographically Diverse Population* By Douadia Bougherara; Lana Friesen; Céline Nauges
  19. Lapse-based insurance By Gottlieb, Daniel; Smetters, Kent
  20. On the systemic nature of global inflation, its association with equity markets and financial portfolio implications By Nick James; Kevin Chin
  21. Investing in a cryptocurrency price bubble: speculative Ponzi schemes and cyclic stochastic price pumps By Misha Perepelitsa
  22. Pricing S&P 500 Index Options with L\'evy Jumps By Bin Xie; Weiping Li; Nan Liang
  23. Sources and Transmission of Country Risk By Tarek Alexander Hassan; Jesse Schreger; Markus Schwedeler; Ahmed Tahoun
  24. UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification By Mr. Jorge A Chan-Lau

  1. By: Daniele Bianchi; Mykola Babiak
    Abstract: We investigate the dynamics of daily realised returns and risk premiums for a large cross-section of cryptocurrency pairs through the lens of an Instrumented Principal Component Analysis (IPCA) (see Kelly et al., 2019). We show that a model with three latent factors and time-varying factor loadings significantly outperforms a benchmark model with observable risk factors: the total (predictive) R2 from the IPCA is 17.2% (2.9%) for individual returns, against a benchmark 9.6% (-0.02%) obtained from a model with six observable risk factors explored in previous literature. By looking at the characteristics that significantly matter for the dynamics of risk premiums, we provide robust evidence that liquidity, size, reversal, and both market and downside risks represent the main driving factors behind expected returns. These results hold for both individual assets and characteristic-based portfolios, pre and post the Covid-19 outbreak, and for weekly individual and portfolio returns.
    Keywords: cryptocurrency markets; instrumented PCA; asset pricing; factor models; risk premiums;
    JEL: G11 G12 G17 C23
    Date: 2021–11
  2. By: Skouralis, Alexandros
    Abstract: This paper empirically investigates the transmission of systemic risk across the Euro Area by employing a Global VAR model. We find that a union aggregate systemic risk shock results in a sharp decline in output, with two thirds of the response to be attributed to cross-country spillovers. The results indicate that peripheral economies have a disproportionate importance in spreading systemic risk compared to core countries. Then, we incorporate high-frequency monetary surprises into the model and we find evidence of the risk-taking channel of monetary policy. However, the relationship is reversed in the period of the ZLB, when expansionary shocks mitigate systemic risk. Cross-country spillovers account for a significant fraction (17.4%) of systemic risk responses’ variation. We also show that near term guidance reduces systemic risk, whereas the initiation of the QE program has the opposite effect. Finally, the effectiveness of monetary policy exhibits significant asymmetries, with core countries driving the union response. JEL Classification: C32, E44, F36, F45
    Keywords: Eurozone, global VAR model, systemic risk
    Date: 2021–12
  3. By: Yajie Yang; Longfeng Zhao; Lin Chen; Chao Wang; Jihui Han
    Abstract: In this study, we propose a new multi-objective portfolio optimization with idiosyncratic and systemic risks for financial networks. The two risks are measured by the idiosyncratic variance and the network clustering coefficient derived from the asset correlation networks, respectively. We construct three types of financial networks in which nodes indicate assets and edges are based on three correlation measures. Starting from the multi-objective model, we formulate and solve the asset allocation problem. We find that the optimal portfolios obtained through the multi-objective with networked approach have a significant over-performance in terms of return measures in an out-of-sample framework. This is further supported by the less drawdown during the periods of the stock market fluctuating downward. According to analyzing different datasets, we also show that improvements made to portfolio strategies are robust.
    Date: 2021–11
  4. By: Kamesh Korangi; Christophe Mues; Cristi\'an Bravo
    Abstract: In this paper, we study mid-cap companies, i.e. publicly traded companies with less than US $10 billion in market capitalisation. Using a large dataset of US mid-cap companies observed over 30 years, we look to predict the default probability term structure over the medium term and understand which data sources (i.e. fundamental, market or pricing data) contribute most to the default risk. Whereas existing methods typically require that data from different time periods are first aggregated and turned into cross-sectional features, we frame the problem as a multi-label time-series classification problem. We adapt transformer models, a state-of-the-art deep learning model emanating from the natural language processing domain, to the credit risk modelling setting. We also interpret the predictions of these models using attention heat maps. To optimise the model further, we present a custom loss function for multi-label classification and a novel multi-channel architecture with differential training that gives the model the ability to use all input data efficiently. Our results show the proposed deep learning architecture's superior performance, resulting in a 13% improvement in AUC (Area Under the receiver operating characteristic Curve) over traditional models. We also demonstrate how to produce an importance ranking for the different data sources and the temporal relationships using a Shapley approach specific to these models.
    Date: 2021–11
  5. By: Bin Xie; Weiping Li
    Abstract: We detect the parameter sensitivities of bond pricing which is driven by a Brownian motion and a compound Poisson process as the discontinuous case in credit risk research. The strict mathematical deductions are given theoretically due to the explicit call price formula. Furthermore, we illustrate Matlab simulation to verify these conclusions.
    Date: 2021–11
  6. By: Yuechen Dai; Tonghui Xu
    Abstract: At present, most well-known insurance regulatory bodies focus on reviewing the solvency of insurance companies within a one-year period. However, the operation of insurance companies is a long-term business, with most policyholders planning on holding a policy over many years, not just one. This research adopts a new perspective for measuring the insolvency risk faced by insurance companies over a longer time period by estimating their full expected lifetime (the number of periods into the future that an insurer can be expected to remain solvent, given their initial capital reserves), which has significance for insurance regulation. This research uses python numerical methods to simulate the operating conditions of insurance companies with different initial reserves, and capture the period in which the company becomes insolvent. The results show that, as is logical, the higher is the initial reserve fund, the longer one can expect the company will be in business before insolvency. In addition, our simulation model helps to explain how the relevant probability density for the insolvency date, given an initial reserve fund, can be estimated. By comparing different probability density functions, we find that a lognormal density form provides a reasonable starting point for the density in question.
    Keywords: Insurance regulation, simulation, insolvency
    JEL: C02 C15 C63
    Date: 2021–11–01
  7. By: Julian Jetses; Marcus C. Christiansen
    Abstract: In with-profit life insurance, the prudent valuation of future insurance liabilities leads to systematic surplus that mainly belongs to the policyholders and is redistributed as bonus. For a fair and lawful redistribution of surplus the insurer needs to decompose the total portfolio surplus with respect to the contributions of individual policies and with respect to different risk sources. For this task, actuaries have a number of heuristic decomposition formulas, but an overarching decomposition principle is still missing. This paper fills that gap by introducing a so-called ISU decomposition principle that bases on infinitesimal sequential updates of the insurer's valuation basis. It is shown that the existing heuristic decomposition formulas can be replicated as ISU decompositions. Furthermore, alternative decomposition principles and their relation to the ISU decomposition principle are discussed. The generality of the ISU concept makes it a useful tool also beyond classical surplus decompositions in life insurance.
    Date: 2021–11
  8. By: Murphy, David (London School of Economics and Political Science); Vause, Nicholas (Bank of England)
    Abstract: Following a period of relative calm, many derivative users received large margin calls as financial market volatility spiked amid the onset of the Covid‑19 global pandemic in March 2020. This reinvigorated the policy debate about dampening such ‘procyclicality’ of margin requirements. In this paper, we suggest how margin setters and policymakers might measure procyclicality and target particular levels of it. This procyclicality management involves recalibrating margin model parameters or applying anti-procyclicality (APC) tools. Different options reduce procyclicality by varying amounts, and do so at different costs, which we measure using the average additional margin required over the cycle. Thus, we perform a cost-benefit analysis (CBA) of the different options. We illustrate our approach using a popular type of margin model – filtered historical simulation value-at-risk – on simple portfolios, presenting the costs and benefits of varying a key model parameter and applying a number of different APC tools, including those in European legislation.
    Keywords: Central counterparty; cost-benefit analysis; derivatives clearing; initial margin models; mandatory clearing; procyclicality
    JEL: G17
    Date: 2021–11–19
  9. By: Sebastiano Michele Zema; Giorgio Fagiolo; Tiziano Squartini; Diego Garlaschelli
    Abstract: The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic 'equally-weighted' portfolio. In this paper, we exploit clustering techniques derived from Random Matrix Theory (RMT) to study a third, intermediate (mesoscopic) market structure that turns out to be the most stable over time and provides important practical insights from a portfolio management perspective. First, we illustrate the benefits, in terms of predicted and realized risk profiles, of constructing portfolios by filtering out both random and systemic comovements from the correlation matrix. Second, we redefine the portfolio optimization problem in terms of stock clusters that emerge after filtering. Finally, we propose a new wealth allocation scheme that attaches equal importance to stocks belonging to the same community and show that it further increases the reliability of the constructed portfolios. Results are robust across different time spans, cross-sectional dimensions and set of constraints defining the optimization problem.
    Keywords: Random matrix theory; Community detection; Mesoscopic structures; Portfolio optimization.
    Date: 2021–12–07
  10. By: Maria Arduca; Cosimo Munari
    Abstract: We develop a general theory of risk measures that determines the optimal amount of capital to raise and invest in a portfolio of reference traded securities in order to meet a pre-specified regulatory requirement. The distinguishing feature of our approach is that we embed portfolio constraints and transaction costs into the securities market. As a consequence, we have to dispense with the property of translation invariance, which plays a key role in the classical theory. We provide a comprehensive analysis of relevant properties such as star shapedness, positive homogeneity, convexity, quasiconvexity, subadditivity, and lower semicontinuity. In addition, we establish dual representations for convex and quasiconvex risk measures. In the convex case, the absence of a special kind of arbitrage opportunities allows to obtain dual representations in terms of pricing rules that respect market bid-ask spreads and assign a strictly positive price to each nonzero position in the regulator's acceptance set.
    Date: 2021–11
  11. By: Matthieu Stigler; David Lobell
    Abstract: Index insurance is a promising tool to reduce the risk faced by farmers, but high basis risk, which arises from imperfect correlation between the index and individual farm yields, has limited its adoption to date. Basis risk arises from two fundamental sources: the intrinsic heterogeneity within an insurance zone (zonal risk), and the lack of predictive accuracy of the index (design risk). Whereas previous work has focused almost exclusively on design risk, a theoretical and empirical understanding of the role of zonal risk is still lacking. Here we investigate the relative roles of zonal and design risk, using the case of maize yields in Kenya. Our first contribution is to derive a formal decomposition of basis risk, providing a simple upper bound on the insurable basis risk that any index can reach within a given zone. Our second contribution is to provide the first large-scale empirical analysis of the extent of zonal versus design risk. To do so, we use satellite estimates of yields at 10m resolution across Kenya, and investigate the effect of using smaller zones versus using different indices. Our results show a strong local heterogeneity in yields, underscoring the challenge of implementing index insurance in smallholder systems, and the potential benefits of low-cost yield measurement approaches that can enable more local definitions of insurance zones.
    Date: 2021–11
  12. By: Claire Mouminoux (BETA - Bureau d'Économie Théorique et Appliquée - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Christophe Dutang (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - CNRS - Centre National de la Recherche Scientifique - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres); Stéphane Loisel (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Hansjoerg Albrecher (UNIL - Université de Lausanne)
    Abstract: In this paper, we extend the non-cooperative one-period game of Dutang et al. (2013) to model a non-life insurance market over several periods by considering the repeated (one-period) game. Using Markov chain methodology, we derive general properties of insurer portfolio sizes given a price vector. In the case of a regulated market (identical premium), we are able to obtain convergence measures of long run market shares. We also investigate the consequences of the deviation of one player from this regulated market. Finally, we provide some insights of long-term patterns of the repeated game as well as numerical illustrations of leadership and ruin probabilities.
    Keywords: Markov chains Mathematics Subject Classification (2010): MSC 60J10,solvency constraint,non-cooperative game,consumers' price sensitivity,game theory,Markov chains Mathematics Subject Classification (2010): MSC 91G05,Markov chains Mathematics Subject Classification (2010): MSC 91A20
    Date: 2021–10–29
  13. By: Gabriele Lattanzio (Nazarbayev University, Graduate School of Business); Jerome Taillard (Babson College, Department of Finance)
    Abstract: Using text-based measures of cybersecurity risk, we document that low cybersecurity risk firms are more likely to initiate or be targeted for an M&A transaction. Further, we show that the market has recently started to price cybersecurity risk at the time of a deal announcement and – consistent with this finding - attempted mergers are significantly less likely to fail if the selected target has a low cybersecurity risk profile. Cyber risk is finally reflected in merger premium, which appears to be systematically higher for mergers where the acquirer exhibits low cybersecurity risk levels. These findings offer novel evidence on the economic impact of cybersecurity risk on the market for corporate control.
    Keywords: Mergers and Acquisitions, Cybersecurity Risk, M&A Withdrawal, Valuation
    JEL: G30 G34 M14
    Date: 2021–10
  14. By: E Philip Davis; Ka Kei Chan; Dilruba Karim
    Abstract: Studies of the effect of macroprudential policy on bank risk tend to disregard the potential complementary role of bank competition, which could influence policy's effectiveness in achieving its financial stability objectives. Accordingly, we assess the relation of macroprudential policy and competition to bank risk jointly from a sample of 1373 banks from 13 East Asian countries, using the latest IMF dataset of macroprudential policy from 1990 to 2018. Among our results, we have found that whereas macroprudential policies did commonly have a beneficial effect on risk at a bank level controlling for competition, there are a number of cases where policies were deleterious through increased risk. Notably in the developing and emerging East Asian countries and in the short term, the interactions between competition and macroprudential measures often show a lesser response in terms of risk reduction for banks with more market power, a form of "competition-stability". We suggest that this links in turn to ability of such banks to undertake risk-shifting in response to macroprudential policy. On the other hand, we find for banks in advanced East Asian countries some tendency in the long term for banks facing intense competition to take relatively more risks in face of macroprudential measures, i.e. "competition fragility". These findings provide important implications for regulators.
    Keywords: Macroprudential policy, bank risk, Z score, bank competition
    JEL: E44 E58 G17 G28
    Date: 2021–12
  15. By: Heinzel, Christoph; Peter, Richard
    Abstract: Using a unified approach, we show how precautionary saving, self-protection and self-insurance are jointly determined by risk preferences and the preference over the timing of uncertainty resolution. We cover higher-order risk effects and examine both risk averters and risk lovers. When decision-makers use several instruments simultaneously to respond to income risk, substitutive interaction effects arise. We quantify precautionary and substitution effects numerically and discuss the role of instrument interaction for the inference of preference parameters from precautionary motives. Instruments can differ substantially in the size of the precautionary motive and in the susceptibility to substitution effects. This affects their suitability for the identification of precautionary preferences.
    Keywords: Consumer/Household Economics, Risk and Uncertainty
    Date: 2021–12–17
  16. By: Yu, Jianyu
    Keywords: Risk and Uncertainty
    Date: 2021–08
  17. By: Simon Gilchrist; Bin Wei; Vivian Z. Yue; Egon Zakrajšek
    Abstract: In this paper, we study the interplay between sovereign risk and global financial risk. We show that a substantial portion of the comovement among sovereign spreads is accounted for by changes in global financial risk. We construct bond-level sovereign spreads for dollar-denominated bonds issued by over 50 countries from 1995 to 2020 and use various indicators to measure global financial risk. Through panel regressions and local projection analysis, we find that an increase in global financial risk causes a large and persistent widening of sovereign bond spreads. These effects are strongest when measuring global risk using the excess bond premium – a measure of the risk-bearing capacity of U.S. financial intermediaries. The spillover effects of global financial risk are more pronounced for speculative-grade sovereign bonds.
    JEL: E43 E44 F33 G12
    Date: 2021–11
  18. By: Douadia Bougherara (CEE-M, Univ. Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France); Lana Friesen (School of Economics, University of Queensland, Brisbane, Australia); Céline Nauges (Toulouse School of Economics, INRAE, University of Toulouse Capitole, Toulouse, France)
    Abstract: We study the interaction between risk taking and skewness seeking behavior among the French population using an experiment that elicits certainty equivalent over lotteries that vary the second and third moments orthogonally. We find that the most common behavior is risk avoidance and skewness seeking. On average, we find no interaction between the two, and a weakly significant interaction only in some segments of the population. That is, in most cases, skewness seeking is not affected by the variance of the lotteries involved, nor is risk taking affected by the skewness of the lotteries. We also find a significant positive correlation between risk avoiding and skewness seeking behavior. Older and female participants make more risk avoiding and more skewness seeking choices, while less educated people and those not in executive occupations are more skewness seeking.
    Keywords: Risk; Skewness; Certainty Equivalent; Experiment
    JEL: C91 D81 D91 G11 G22
    Date: 2021–11–25
  19. By: Gottlieb, Daniel; Smetters, Kent
    Abstract: Most individual life insurance policies lapse, with lapsers cross-subsidizing non-lapsers. We show that policies and lapse patterns predicted by standard rational expectations models are the opposite of those observed empirically. We propose two behavioral models consistent with the evidence: (i) consumers forget to pay premiums and (ii) consumers understate future liquidity needs. We conduct two surveys with a large insurer. New buyers believe that their own lapse probabilities are small compared to the insurer's actual experience. For recent lapsers, forgetfulness accounts for 37.8 percent of lapses while unexpected liquidity accounts for 15.4 percent.
    JEL: D91 G22
    Date: 2021–08–01
  20. By: Nick James; Kevin Chin
    Abstract: This paper uses new and recently introduced mathematical techniques to undertake a data-driven study on the systemic nature of global inflation. We start by investigating country CPI inflation over the past 70 years. There, we highlight the systemic nature of global inflation with a judicious application of eigenvalue analysis and determine which countries exhibit most "centrality" with an inner-product based optimization method. We then turn to inflationary impacts on financial market securities, where we explore country equity indices' equity robustness and the varied performance of equity sectors during periods of significant inflationary pressure. Finally, we implement a time-varying portfolio optimization to determine which asset classes were most beneficial in increasing portfolio Sharpe ratio when an investor must hold a core (and constant) allocation to equities.
    Date: 2021–11
  21. By: Misha Perepelitsa
    Abstract: The problem of investing into a cryptocurrency market requires good understanding of the processes that regulate the price of the currency. In this paper we offer a view of a cryptocurrency market as self-organized speculative Ponzi scheme that operates on the platform of a price bubble spontaneously created by traders. The synergy between investors and traders creates an interesting dynamical patterns of the price and systematic risk of the system. We use microscale, agent-based models to simulate the system behavior and derive macroscale ODE models to estimate such parameters as the return rate and total value of investments. We provide the formula for the total risk of the system as a sum of two independent components, one being characteristic of the price bubble and the other of the investor behavior.
    Date: 2021–11
  22. By: Bin Xie; Weiping Li; Nan Liang
    Abstract: We analyze various jumps for Heston model, non-IID model and three L\'evy jump models for S&P 500 index options. The L\'evy jump for the S&P 500 index options is inevitable from empirical studies. We estimate parameters from in-sample pricing through SSE for the BS, SV, SVJ, non-IID and L\'evy (GH, NIG, CGMY) models by the method of Bakshi et al. (1997), and utilize them for out-of-sample pricing and compare these models. The sensitivities of the call option pricing for the L\'evy models with respect to parameters are presented. Empirically, we show that the NIG model, SV and SVJ models with estimated volatilities outperform other models for both in-sample and out-of-sample periods. Using the in-sample optimized parameters, we find that the NIG model has the least SSE and outperforms the rest models on one-day prediction.
    Date: 2021–11
  23. By: Tarek Alexander Hassan; Jesse Schreger; Markus Schwedeler; Ahmed Tahoun
    Abstract: We use textual analysis of earnings conference calls held by listed firms around the world to measure the amount of risk managers and investors at each firm associate with each country at each point in time. Flexibly aggregating this firm-country-quarter-level data allows us to systematically identify spikes in perceived country risk (“crises”) and document their source and pattern of transmission to foreign firms. While this pattern usually follows a gravity structure, it often changes dramatically during crises. For example, while crises originating in developed countries propagate disproportionately to foreign financial firms, emerging market crises transmit less financially and more to traditionally exposed countries. We apply our measures to show that (i) elevated perceptions of a country's riskiness, particularly those of foreign and financial firms, are associated with significant falls in local asset prices, capital outflows, and reductions in firm-level investment and employment. (ii) Risk transmitted from foreign countries affects the investment decisions of domestic firms. (iii) Heterogeneous currency loadings on perceived global risk can help explain the cross-country pattern of interest rates and currency risk premia.
    JEL: D21 F23 F3 F30 G15
    Date: 2021–11
  24. By: Mr. Jorge A Chan-Lau
    Abstract: We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be identified with a different regime. The likelihood that a country may experience an econmic crisis could be set equal to its cluster crisis frequency. Moreover, unFEAR could serve as a first step towards developing cluster-specific crisis prediction models tailored to each crisis regime.
    Keywords: clustering;unsupervised feature extraction;autoencoder;deep learning;biased label problem;crisis prediction;WP;crisis frequency;crisis observation;crisis risk;crisis data points; machine learning; Early warning systems; Global
    Date: 2020–11–25

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