nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒11‒09
24 papers chosen by
Stan Miles
Thompson Rivers University

  1. A Functional-Coefficient VAR Model for Dynamic Quantiles with Constructing Financial Network By Zongwu Cai; Xiyuan Liu
  2. The loss optimisation of loan recovery decision times using forecast cash flows By Arno Botha; Conrad Beyers; Pieter de Villiers
  3. Liquidity, Interbank Network Topology and Bank Capital By Aref Ardekani
  4. Unbiased estimation and backtesting of risk in the context of heavy tails By Marcin Pitera; Thorsten Schmidt
  5. Determinants of Banks’ Liquidity: a French Perspective on Interactions between Market and Regulatory Requirements By de Bandt Olivier; Lecarpentier Sandrine; Pouvelle Cyril
  6. Conditional Systemic Risk Measures By Alessandro Doldi; Marco Frittelli
  7. COVID-19 Enhanced Diminishing Sensitivity in Prospect-Theory Risk Preferences: A Panel Analysis By Shinsuke Ikeda; Eiji Yamamura; Yoshiro Tsutsui
  8. Risk Management in Engineering and Construction By Nguyen, Phong Thanh; Phu Nguyen, Cuong
  9. Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures By Derek Singh; Shuzhong Zhang
  10. Time-Varying Risk Aversion and Forecastability of the US Term Structure of Interest Rates By Elie Bouri; Rangan Gupta; Anandamayee Majumdar; Sowmya Subramaniam
  11. Monetary Policy, Prudential Policy, and Bank's Risk-Taking: A Literature Review By Melchisedek Joslem Ngambou Djatche
  12. Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk By Tetsuo Kurosaki; Young Shin Kim
  13. How Has Post-Crisis Banking Regulation Affected Hedge Funds and Prime Brokers? By Nina Boyarchenko; Thomas M. Eisenbach; Pooja Gupta; Or Shachar; Peter Van Tassel
  14. Millionaires Speak: What Drives Their Personal Investment Decisions? By Svetlana Bender; James J. Choi; Danielle Dyson; Adriana Z. Robertson
  15. How do People Respond to Small Probability Events with Large, Negative Consequences? By Martin S. Eichenbaum; Miguel Godinho de Matos; Francisco Lima; Sergio Rebelo; Mathias Trabandt
  16. Bear, Bull, Sidewalk, and Crash: The Evolution of the US Stock Market Using Over a Century of Daily Data By Shixuan Wang; Rangan Gupta; Yue-Jun Zhang
  17. Sovereign risk and bank fragility By Anand, Kartik; Mankart, Jochen
  18. Information Coefficient as a Performance Measure of Stock Selection Models By Feng Zhang; Ruite Guo; Honggao Cao
  19. Bank capital forbearance and serial gambling By Martynova, Natalya; Perotti, Enrico C.; Suárez, Javier
  20. Liquidity and Volatility By Itamar Drechsler; Alan Moreira; Alexi Savov
  21. High-dimensional covariance matrix estimation By Lam, Clifford
  22. Five years of regional risk pooling: An updated cost-benefit analysis of the African risk capacity By Kramer, Berber; Rusconi, Rob; Glauber, Joseph W.
  23. Are Crises Predictable? A Review of the Early Warning Systems in Currency and Stock Markets By Peiwan Wang; Lu Zong
  24. Jump Models with delay -- option pricing and logarithmic Euler-Maruyama scheme By Nishant Agrawal; Yaozhong Hu

  1. By: Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Xiyuan Liu (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)
    Abstract: The degree of interdependences among holdings of financial sectors and its varying patterns play important roles in forming systemic risks within a financial system. In this article, we propose a VAR model of conditional quantiles with functional coefficients to construct a novel class of dynamic network system, of which the interdependences among tail risks such as Value-at-Risk are allowed to vary with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system by the local linear smoothing technique. We establish the consistency and the asymptotic normality of the proposed estimator under time series settings. The simulation studies are conducted to show that our new methods work fairly well. The potential of the proposed estimation procedures is demonstrated by an empirical study of constructing and estimating a new type of dynamic financial network.
    Keywords: cDynamic financial network; Functional coefficient models; Multivariate conditional quantile models; Nonparametric estimation; VAR modeling
    JEL: C14 C58 C45 G32
    Date: 2020–10
  2. By: Arno Botha; Conrad Beyers; Pieter de Villiers
    Abstract: A theoretical method is empirically illustrated in finding the best time to forsake a loan such that the overall credit loss is minimised. This is predicated by forecasting the future cash flows of a loan portfolio up to the contractual term, as a remedy to the inherent right-censoring of real-world `incomplete' portfolios. Two techniques, a simple probabilistic model as well as an eight-state Markov chain, are used to forecast these cash flows independently. We train both techniques from different segments within residential mortgage data, provided by a large South African bank, as part of a comparative experimental framework. As a result, the recovery decision's implied timing is empirically illustrated as a multi-period optimisation problem across uncertain cash flows and competing costs. Using a delinquency measure as a central criterion, our procedure helps to find a loss-optimal threshold at which loan recovery should ideally occur for a given portfolio. Furthermore, both the portfolio's historical risk profile and forecasting thereof are shown to influence the timing of the recovery decision. This work can therefore facilitate the revision of relevant bank policies or strategies towards optimising the loan collections process, especially that of secured lending.
    Date: 2020–10
  3. By: Aref Ardekani (UP1 - Université Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UNILIM - Université de Limoges)
    Abstract: By applying the interbank network simulation, this paper examines whether the causal relationship between capital and liquidity is influenced by bank positions in the interbank network. While existing literature highlights the causal relationship that moves from liquidity to capital, the question of how interbank network characteristics affect this relationship remains unclear. Using a sample of commercial banks from 28 European countries, this paper suggests that banks' interconnectedness within interbank loan and deposit networks affects their decisions to set higher or lower regulatory capital rations when facing higher illiquidity. This study provides support for the need to implement minimum liquidity ratios to complement capital ratios, as stressed by the Basel Committee on Banking Regulation and Supervision. This paper also highlights the need for regulatory authorities to consider the network characteristics of banks.
    Keywords: Interbank network topology,Bank regulatory capital,Liquidity risk,Basel III
    Date: 2020–10
  4. By: Marcin Pitera; Thorsten Schmidt
    Abstract: While the estimation of risk is an important question in the daily business of banks and insurances, it is surprising that efficient procedures for this task are not well studied. Indeed, many existing plug-in approaches for the estimation of risk suffer from an unnecessary bias which leads to the underestimation of risk and negatively impacts backtesting results, especially in the small sample environment. In this article, we consider efficient estimation of risk in practical situations and provide means to improve the accuracy of risk estimators and their performance in backtesting. In particular, we propose an algorithm for bias correction and show how to apply it for generalized Pareto distributions. Moreover, we propose new estimators for value-at-risk and expected shortfall, respectively, and illustrate the gain in efficiency when heavy tails exist in the data.
    Date: 2020–10
  5. By: de Bandt Olivier; Lecarpentier Sandrine; Pouvelle Cyril
    Abstract: The paper investigates the impact of solvency and liquidity regulation on banks' balance sheet structure. The Covid-19 pandemics shows that periods of sharp increase in risk aversion often result in liquidity strains for banks due to the volatility of long-term funding markets. According to a simple portfolio allocation model banks’ liquidity increases when the regulatory constraint is binding. We provide evidence, using the “liquidity coefficient” implemented in France ahead of Basel III's Liquidity Coverage Ratio, of a positive effect of the solvency ratio on the liquidity coefficient. We also show that in times of crisis, measured by financial variables, French banks actually decreased the liquidity coefficient, with the transmission channel materialising mainly on the liability side.
    Keywords: Bank Capital Regulation, Bank Liquidity Regulation, Basel III, Stress Tests.
    JEL: G28 G21
    Date: 2020
  6. By: Alessandro Doldi; Marco Frittelli
    Abstract: We investigate to which extent the relevant features of (static) systemic risk measures can be extended to a conditional setting. After providing a general dual representation result, we analyze in greater detail Conditional Shortfall Systemic Risk Measures. In the particular case of exponential preferences, we provide explicit formulas that also allow us to show a time consistency property. Finally, we provide an interpretation of the allocations associated to Conditional Shortfall Systemic Risk Measures as suitably defined equilibria. Conceptually, the generalization from static to conditional systemic risk measures can be achieved in a natural way, even though the proofs become more technical than in the unconditional framework.
    Date: 2020–10
  7. By: Shinsuke Ikeda; Eiji Yamamura; Yoshiro Tsutsui
    Abstract: Based on unique panel data from a five-wave internet survey in Japan, we show how the coronavirus disease 2019 pandemic affected people’s prospect-theory risk preferences, especially in the loss domain. The panel analysis indicates that with the spread of the pandemic, diminishing sensitivity becomes stronger for the participants’ value and probability weighting functions. Thus, due to the pandemic, (i) people become less sensitive to an increase in losses and feel less pain due to losses, especially large ones; and (ii) they become more pessimistic towards tail loss risks, and more optimistic towards non-tail loss risks. One implication is that people have become less cautious of the risks of suffering large non-tail losses, which might retard the recovery of society.
    Date: 2020–10
  8. By: Nguyen, Phong Thanh; Phu Nguyen, Cuong
    Abstract: The constant demand for construction in developing countries like Vietnam causes more and more challenges and difficulties to Project Management Units (PMUs) in carrying projects to completion on schedule, with quality assurance and fewer costs. In order to do this, PMUs need to have better and tighter management tools and forms. However, in order to minimize risks during project implementation, the binding terms in contracts are also becoming stricter with more and more new forms of contracts. One of them is the design-build (DB) contract form. This paper presents the critical risk factors for designbuild projects in the construction industry. Good identification and management of these risk factors will help projects succeed and will increase the confidence of owners and contractors who seek to use the design-build form.
    Keywords: design-build (DB); risk management; project manager; construction management; Vietnam
    JEL: D81 G32 L33 R42
    Date: 2019–02–02
  9. By: Derek Singh; Shuzhong Zhang
    Abstract: This paper expands the notion of robust moment problems to incorporate distributional ambiguity using Wasserstein distance as the ambiguity measure. The classical Chebyshev-Cantelli (zeroth partial moment) inequalities, Scarf and Lo (first partial moment) bounds, and semideviation (second partial moment) in one dimension are investigated. The infinite dimensional primal problems are formulated and the simpler finite dimensional dual problems are derived. A principal motivating question is how does data-driven distributional ambiguity affect the moment bounds. Towards answering this question, some theory is developed and computational experiments are conducted for specific problem instances in inventory control and portfolio management. Finally some open questions and suggestions for future research are discussed.
    Date: 2020–10
  10. By: Elie Bouri (Adnan Kassar School of Business, Lebanese American University, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Anandamayee Majumdar (Department of Physical Sciences, School of Engineering, Technology & Sciences, Independent University, Bangladesh, Dhaka 1229, Bangladesh); Sowmya Subramaniam (Indian Institute of Management Lucknow, Prabandh Nagar off Sitapur Road, Lucknow, Uttar Pradesh 226013, India)
    Abstract: In this paper, we analyse the forecasting ability of a time-varying metric of daily risk aversion for the entire term structure of interest rates of Treasury securities of the United States (US) as reflected by the three latent factors, level, slope and curvature. Using daily data covering the out-of-sample period 22nd June, 1988 to 3rd September, 2020 (given the in-sample period 30th May, 1986 to 21st June, 1988) within a quantiles-based framework, the results show statistically significant forecasting gains emanating from risk aversion for the tails of the conditional distributions of the level, slope and curvature factors at horizons of one-day, one-week, and one-month-ahead. Interestingly, a conditional mean-based model fails to detect any evidence of out-of-sample predictability. Our findings have important implications for academics, bond investors, and policymakers in their quest to better understand the evolution of future movement in US Treasury securities.
    Keywords: Yield Curve Factors, Risk Aversion, Out-of-Sample Forecasts
    JEL: C22 C53 E43 G12 G17
    Date: 2020–10
  11. By: Melchisedek Joslem Ngambou Djatche (Université Côte d'Azur; GREDEG CNRS)
    Abstract: The pre-crisis low interest rates environment is raising concerns among researchers and policymakers about its impact on the triangle prudential policy - monetary policy - bank's risk-taking. While interest rates is set at low level for inflationary and economic growth reasons, they may lead banks to take more risk, jeopardizing the financial system and impeding the recovery. This paper provides a literature review, on the one hand, on the interaction of monetary and prudential policies through their impacts on bank's risk-taking, and on the other hand, on the issues of their coordination. Monetary policy appears to have ambiguous effects on banks' profitability, and then, on banks' risk-taking behaviour. Despite monetary and prudential policies pursue different objectives, they inevitably interact, raising challenges that face policymakers. Albeit it is argued that monetary policy alone is not sufficient to maintain macroeconomic and financial stability, and that it should be coordinated with prudential policy, the form of this coordination is not clear-cut.
    Keywords: Monetary policy, prudential policy, financial stability, bank's risk-taking
    Date: 2020–10
  12. By: Tetsuo Kurosaki; Young Shin Kim
    Abstract: We study portfolio optimization of four major cryptocurrencies. Our time series model is a generalized autoregressive conditional heteroscedasticity (GARCH) model with multivariate normal tempered stable (MNTS) distributed residuals used to capture the non-Gaussian cryptocurrency return dynamics. Based on the time series model, we optimize the portfolio in terms of Foster-Hart risk. Those sophisticated techniques are not yet documented in the context of cryptocurrency. Statistical tests suggest that the MNTS distributed GARCH model fits better with cryptocurrency returns than the competing GARCH-type models. We find that Foster-Hart optimization yields a more profitable portfolio with better risk-return balance than the prevailing approach.
    Date: 2020–10
  13. By: Nina Boyarchenko; Thomas M. Eisenbach; Pooja Gupta; Or Shachar; Peter Van Tassel
    Abstract: “Arbitrageurs” such as hedge funds play a key role in the efficiency of financial markets. They compare closely related assets, then buy the relatively cheap one and sell the relatively expensive one, thereby driving the prices of the assets closer together. For executing trades and other services, hedge funds rely on prime brokers and broker-dealers. In a previous Liberty Street Economics blog post, we argued that post-crisis changes to regulation and market structure have increased the costs of arbitrage activity, potentially contributing to the persistent deviations in the prices of closely related assets since the 2007–09 financial crisis. In this post, we document how post-crisis changes to bank regulations have affected the relationship between hedge funds and broker-dealers.
    Keywords: post-crisis regulation; hedge funds; prime brokers; basis trades
    JEL: G1 G2
    Date: 2020–10–19
  14. By: Svetlana Bender; James J. Choi; Danielle Dyson; Adriana Z. Robertson
    Abstract: We survey 2,484 U.S. individuals with at least $1 million of investable assets about how well leading academic theories describe their financial beliefs and decisions. The most important factors determining portfolio equity share are professional advice, time until retirement, personal experiences, rare disaster risk, and health risk. Beliefs about how expected returns vary with stock characteristics often differ from historical relationships, and more risk is not always associated with higher expected returns. Those who invest in active equity funds most often do so based on professional advice and because they expect to earn higher average returns. Only 19% of respondents agree that high past fund manager performance is strong evidence of stock-picking skill and that there are diminishing returns to scale in active management. Concentrated equity holdings are most often motivated by a belief that the overweighted stock has superior risk-adjusted returns.
    JEL: G11 G12
    Date: 2020–10
  15. By: Martin S. Eichenbaum; Miguel Godinho de Matos; Francisco Lima; Sergio Rebelo; Mathias Trabandt
    Abstract: We study how people react to small probability events with large negative consequences using the outbreak of the COVID-19 epidemic as a natural experiment. Our analysis is based on a unique administrative data set with anonymized monthly expenditures at the individual level. We find that older consumers reduced their spending by more than younger consumers in a way that mirrors the age dependency in COVID-19 case-fatality rates. This differential expenditure reduction is much more prominent for high-contact goods than for low-contact goods and more pronounced in periods with high COVID-19 cases. Our results are consistent with the hypothesis that people react to the risk of contracting COVID-19 in a way that is consistent with a canonical model of risk taking.
    JEL: E21 I1
    Date: 2020–10
  16. By: Shixuan Wang (Department of Economics, University of Reading, Reading, RG6 6AA, United Kingdom); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Yue-Jun Zhang (College of Business Administration, University of Nebraska at Omaha, 6708 Pine Street, Omaha, NE 68182, USA)
    Abstract: In this paper, we employ a four-state hidden semi-Markov model (HSMM), which outperforms a hidden Markov model (HMM), to identify market conditions of the Dow Jones Industrial stock market over the daily period of 16th of February, 1885 to 4th of June, 2020. Our results indicate that the four hidden states represent bear-, bull-, sidewalk-, and crash-markets, which in turn appropriately captures the various major historical events during the period of study. Our results have implications for investors and policymakers.
    Keywords: Dow Jones Industrial Average, Stock Returns, Hidden (semi-)Markov Models
    JEL: C22 G10
    Date: 2020–10
  17. By: Anand, Kartik; Mankart, Jochen
    Abstract: We develop a model of bank risk-taking with strategic sovereign default risk. Domestic banks invest in real projects and purchase government bonds. While an increase in bond purchases crowds out profitable investments, it improves the government's incentives to repay and therefore lowers its borrowing costs. For low levels of government debt, banks influence their default risks through purchases of bonds. But, for high debt levels, this influence is lost since bank and government default are perfectly correlated. Banks fail to account for how their bond purchases influence the government's default incentives. This leads to socially inefficient levels of bond holdings.
    Keywords: sovereign debt,financial intermediation,financial repression,bank fragility
    JEL: G01 G21 G28
    Date: 2020
  18. By: Feng Zhang; Ruite Guo; Honggao Cao
    Abstract: Information coefficient (IC) is a widely used metric for measuring investment managers' skills in selecting stocks. However, its adequacy and effectiveness for evaluating stock selection models has not been clearly understood, as IC from a realistic stock selection model can hardly be materially different from zero and is often accompanies with high volatility. In this paper, we investigate the behavior of IC as a performance measure of stick selection models. Through simulation and simple statistical modeling, we examine the IC behavior both statically and dynamically. The examination helps us propose two practical procedures that one may use for IC-based ongoing performance monitoring of stock selection models.
    Date: 2020–10
  19. By: Martynova, Natalya; Perotti, Enrico C.; Suárez, Javier
    Abstract: We analyze the strategic interaction between undercapitalized banks and a supervisor who may intervene by preventive recapitalization. Supervisory forbearance emerges because political and fiscal costs undermine supervisors' commitment to intervene. When supervisors have lower credibility, banks' incentives to voluntary recapitalize are lower and supervisors may end up intervening more. Importantly, when intervention capacity is constrained (e.g. for fiscal reasons), private recapitalization decisions become strategic complements, producing equilibria with extremely high forbearance and high systemic costs. Anticipating forbearance in response to diffuse undercapitalization, banks may ex ante choose more correlated risks, a form of "serial gambling" undermining the supervisory response.
    Keywords: bank supervision,bank recapitalization,forbearance
    JEL: G21 G28
    Date: 2020
  20. By: Itamar Drechsler; Alan Moreira; Alexi Savov
    Abstract: Liquidity provision is a bet against private information: if private information turns out to be higher than expected, liquidity providers lose. Since information generates volatility, and volatility co-moves across assets, liquidity providers have a negative exposure to aggregate volatility shocks. As aggregate volatility shocks carry a very large premium in option markets, this negative exposure can explain why liquidity provision earns high average returns. We show this by incorporating uncertainty about the amount of private information into an otherwise standard model. We test the model in the cross section of short-term reversals, which mimic the portfolios of liquidity providers. As predicted by the model, reversals have large negative betas to aggregate volatility shocks. These betas explain their average returns with the same risk price as in option markets, and their predictability by VIX in the time series. Volatility risk thus explains the liquidity premium among stocks and why it increases in volatile times. Our results provide a novel view of the risks and returns to liquidity provision.
    JEL: E44 G12 G23
    Date: 2020–10
  21. By: Lam, Clifford
    Abstract: Covariance matrix estimation plays an important role in statistical analysis in many fields, including (but not limited to) portfolio allocation and risk management in finance, graphical modeling, and clustering for genes discovery in bioinformatics, Kalman filtering and factor analysis in economics. In this paper, we give a selective review of covariance and precision matrix estimation when the matrix dimension can be diverging with, or even larger than the sample size. Two broad categories of regularization methods are presented. The first category exploits an assumed structure of the covariance or precision matrix for consistent estimation. The second category shrinks the eigenvalues of a sample covariance matrix, knowing from random matrix theory that such eigenvalues are biased from the population counterparts when the matrix dimension grows at the same rate as the sample size. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical and Graphical Methods of Data Analysis > Nonparametric Methods.
    Keywords: Structured covariance estimation; sparsity; low rank plus sparse; factor model; shrinkage
    JEL: C1
    Date: 2020–03–01
  22. By: Kramer, Berber; Rusconi, Rob; Glauber, Joseph W.
    Abstract: An initial cost-benefit analysis (CBA) of the African Risk Capacity (ARC), published in 2013, showed that regional risk pooling for severe droughts could increase benefits to poor households by as much as US$ 1.90 per dollar invested, due to the speed, cost and targeting gains from improved risk financing and contingency planning of a humanitarian response. We revisit the assumptions underpinning this initial CBA to reflect current ARC operations, and we update the CBA using new methods for evaluating the costs and benefits of regional risk pooling to finance disaster risk management. Under the revised methods and assumptions, the increase in benefits to the poor will have exceeded the costs of regional risk pooling, but not by as much as US$ 1.90 per dollar invested. This is because ARC premiums have been higher than assumed in the initial CBA, and insured countries have used ARC payouts mainly to distribute food aid, instead of leveraging state-contingent welfare schemes with potentially larger speed, cost and targeting gains. We discuss potential ways to lower premiums and strengthen the benefits to poor households, highlighting also the potential to realize welfare gains from improved risk management and investments ex ante, even during years without insurance payout.
    Keywords: AFRICA; AFRICA SOUTH OF SAHARA; CENTRAL AFRICA; EAST AFRICA; NORTH AFRICA; SOUTHERN AFRICA; WEST AFRICA; drought; disaster risk management; cost benefit analysis; risk transfer; mitigation; investment; disaster risk reduction; sovereign risk financing; risk financing; risk pooling
    Date: 2020
  23. By: Peiwan Wang; Lu Zong
    Abstract: The study efforts to explore and extend the crisis predictability by synthetically reviewing and comparing a full mixture of early warning models into two constitutions: crisis identifications and predictive models. Given empirical results on Chinese currency and stock markets, three-strata findings are concluded as (i) the SWARCH model conditional on an elastic thresholding methodology can most accurately classify crisis observations and greatly contribute to boosting the predicting precision, (ii) stylized machine learning models are preferred given higher precision in predicting and greater benefit in practicing, (iii) leading factors sign the crisis in a diversified way for different types of markets and varied prediction periods.
    Date: 2020–10
  24. By: Nishant Agrawal; Yaozhong Hu
    Abstract: In this paper, we In this paper, we obtain the existence, uniqueness and positivity of the solution to delayed stochastic differential equations with jumps. This equation is then applied to model the price movement of the risky asset in a financial market and the Black-Scholes formula for the price of European option is obtained together with the hedging portfolios. The option price is evaluated analytically at the last delayed period by using the Fourier transformation technique. But in general there is no analytical expression for the option price. To evaluate the price numerically we then use the Monte-Carlo method. To this end we need to simulate the delayed stochastic differential equations with jumps. We propose a logarithmic numerical scheme to approximate the equation and prove that all the approximations remain positive and the rate of convergence of the scheme is proved to be 0.5.
    Date: 2020–10

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