nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒11‒30
thirteen papers chosen by

  1. Measuring and Hedging Geopolitical Risk By Robert F. Engle; Susana Campos-Martins
  2. Forecasting Probability of Default for Consumer Loan Management with Gaussian Mixture Models By Hamidreza Arian; Seyed Mohammad Sina Seyfi; Azin Sharifi
  3. Generating unfavourable VaR scenarios with patchwork copulas By Dietmar Pfeifer; Olena Ragulina
  4. The Market Risk Premium for Unsecured Consumer Credit Risk By Matthias Fleckenstein; Francis A. Longstaff
  5. Financial Variables as Predictors of Real Growth Vulnerability By Lucrezia Reichlin; Giovanni Ricco; Thomas Hasenzagl
  6. (When) do banks react to anticipated capital reliefs? By Arnould, Guillaume; Guin, Benjamin; Ongena, Steven; Siciliani, Paolo
  7. Stand Still and Do Nothing: COVID-19, Stock Returns and Volatility By Akbar, Muhammad; Tahir, Aima
  8. Optimal Collaterals in Multi-Enterprise Investment Networks By Moshe Babaioff; Yoav Kolumbus; Eyal Winter
  9. "Dynamic Factor, Leverage and Realized Covariances in Multivariate Stochastic Volatility" By Yuta Yamauchi; Yasuhiro Omori
  10. Flight to Safety in Business cycles By Yadav, Jayant
  11. Masters of Illusion: Bank and Regulatory Accounting for Losses in Distressed Banks By Edward J. Kane
  12. The Financial Firefighter’s Manual By Schuler, Kurt
  13. Treatment of Risk and Uncertainty in Transportation Investment Planning By Lewis, David; Suen, Ling

  1. By: Robert F. Engle (New York Stern School of Business); Susana Campos-Martins (Nuffield College, University of Oxford and NIPE)
    Abstract: Geopolitical events can impact volatilities of all assets, asset classes, sectors and countries. It is shown that innovations to volatilities are correlated across assets and therefore can be used to measure and hedge geopolitical risk. We introduce a definition of geopolitical risk which is based on volatility shocks to a wide range of financial market prices. To measure geopolitical risk, we propose a statistical model for the magnitude of the common volatility shocks. Accordingly, a test and estimation methods are developed and studied using both empirical and simulated data. We provide a novel explanation for why idiosyncratic volatilities comove based on a new way to formulate multiplicative factors. Finally, we propose a new criterion for portfolio optimality which is intended to reduce the exposure to geopolitical risk.
    Date: 2020
  2. By: Hamidreza Arian; Seyed Mohammad Sina Seyfi; Azin Sharifi
    Abstract: Credit scoring is an essential tool used by global financial institutions and credit lenders for financial decision making. In this paper, we introduce a new method based on Gaussian Mixture Model (GMM) to forecast the probability of default for individual loan applicants. Clustering similar customers with each other, our model associates a probability of being healthy to each group. In addition, our GMM-based model probabilistically associates individual samples to clusters, and then estimates the probability of default for each individual based on how it relates to GMM clusters. We provide applications for risk managers and decision makers in banks and non-bank financial institutions to maximize profit and mitigate the expected loss by giving loans to those who have a probability of default below a decision threshold. Our model has a number of advantages. First, it gives a probabilistic view of credit standing for each individual applicant instead of a binary classification and therefore provides more information for financial decision makers. Second, the expected loss on the train set calculated by our GMM-based default probabilities is very close to the actual loss, and third, our approach is computationally efficient.
    Date: 2020–11
  3. By: Dietmar Pfeifer; Olena Ragulina
    Abstract: The central idea of the paper is to present a general simple patchwork construction principle for multivariate copulas that create unfavourable VaR (i.e. Value at Risk) scenarios while maintaining given marginal distributions. This is of particular interest for the construction of Internal Models in the insurance industry under Solvency II in the European Union.
    Date: 2020–11
  4. By: Matthias Fleckenstein; Francis A. Longstaff
    Abstract: We use the prices of credit card asset-backed securities to study the market risk premium associated with unsecured consumer credit risk. The consumer credit risk premium has historically been comparable to high yield corporate bond spreads, but has increased dramatically since the financial crisis. We find evidence that this increase is primarily due to balance-sheet costs imposed by recent changes in regulatory capital requirements which have effectively placed credit card securitizations back onto issuer balance sheets. These changes in capital regulation may have added hundreds of basis points to the cost of unsecured household credit.
    JEL: G12
    Date: 2020–10
  5. By: Lucrezia Reichlin (London Business School); Giovanni Ricco (Observatoire français des conjonctures économiques); Thomas Hasenzagl
    Abstract: We evaluate the role of financial conditions as predictors of macroeconomic risk first in the quantile regression framework of Adrian et al. (2019b), which allows for non-linearities, and then in a novel linear semi-structural model as proposed by Hasenzagl et al. (2018). We distinguish between price variables such as credit spreads and stock variables such as leverage. We find that (i) although the spreads correlate with the left tail of the conditional distribution of GDP growth, they provide limited advanced information on growth vulnerability; (ii) nonfinancial leverage provides a leading signal for the left quantile of the GDP growth distribution in the 2008 recession; (iii) measures of excess leverage conceptually similar to the Basel gap, but cleaned from business cycle dynamics via the lenses of the semi-structural model, point to two peaks of accumulation of risks – the eighties and the first eight years of the new millennium, with an unstable relationship with business cycle chronology.
    Keywords: Financial cycle; Business cycle; Credit; Financial crises; Downside risk; Entropy; Quantile regressions
    JEL: E32 E44 C32 C53
    Date: 2020
  6. By: Arnould, Guillaume (Bank of England); Guin, Benjamin (Bank of England); Ongena, Steven (University of Zurich); Siciliani, Paolo (Bank of England)
    Abstract: We study how banks react to policy announcements during a representative policy cycle involving consultation and publication using a novel dataset on the population of all mortgage transactions and regulatory risk assessments of banks. We demonstrate that banks likely to benefit from lower capital requirements increase the size of this capital relief by permanently investing into low risk assets after the publication of the policy. In contrast, there is no evidence that they already reacted to the early step of the development of the policy, the publication of the consultation paper. We show how these results can be used to estimate a lower bound on the cost of capital for smaller banks, for which such estimates are typically difficult to obtain.
    Keywords: Bank regulation; mortgage lending; supervisory review process; capital requirements
    JEL: G21
    Date: 2020–11–13
  7. By: Akbar, Muhammad; Tahir, Aima
    Abstract: We examine the intraday returns and volatility in the US equity market amid the COVID-19 pandemic crisis. Our empirical results suggest increase in volatility overtime with mostly negative returns and higher volatility in last trading session of the day. Our Univariate analysis reveal structural break(s) since the first trading halt in March 2020 and that failure to account for this may lead to biased and unstable conditional estimates. Allowing for time varying conditional variance and conditional correlation, our dynamic conditional correlation tests suggest that COVID-19 cases and deaths are jointly related to stock returns and realised volatility.
    Date: 2020–11–19
  8. By: Moshe Babaioff; Yoav Kolumbus; Eyal Winter
    Abstract: We study a market of investments on networks, where each agent (vertex) can invest in any enterprise linked to him, and at the same time, raise capital for his firm own enterprise from other agents he is linked to. Failing to raise sufficient capital results with the firm defaulting, being unable to invest in others. Our main objective is to examine the role of collaterals in handling the strategic risk that can propagate to a systemic risk throughout the network in a cascade of defaults. We take a mechanism design approach and solve for the optimal scheme of collateral contracts that capital raisers offer their investors. These contracts aim at sustaining the efficient level of investment as a unique Nash equilibrium, while minimizing the total collateral. Our main results contrast the network environment with its non-network counterpart (where the sets of investors and capital raisers are disjoint). We show that for acyclic investment networks, the network environment does not necessitate any additional collaterals, and systemic risk can be fully handled by optimal bilateral collateral contracts between capital raisers and their investors. This is, unfortunately, not the case for cyclic investment networks. We show that bilateral contracting will not suffice to resolve systemic risk, and the market will need an external entity to design a global collateral scheme for all capital raisers. Furthermore, the minimum total collateral that will sustain the efficient level of investment as a unique equilibrium may be arbitrarily higher, even in simple cyclic investment networks, compared with its corresponding non-network environment. Additionally, we prove computational-complexity results, both for a single enterprise and for networks.
    Date: 2020–11
  9. By: Yuta Yamauchi (Graduate School of Economics, The University of Tokyo); Yasuhiro Omori (Faculty of Economics, The University of Tokyo)
    Abstract: In the stochastic volatility models for multivariate daily stock returns, it has been found that the estimates of parameters become unstable as the dimension of returns increases. To solve this problem, we focus on the factor structure of multiple returns and consider two additional sources of information: first, the realized stock index associated with the market factor, and second, the realized covariance matrix calculated from high frequency data. The proposed dynamic factor model with the leverage effect and realized measures is applied to ten of the top stocks composing the exchange traded fund linked with the investment return of the S&P500 index and the model is shown to have a stable advantage in portfolio performance.
    Date: 2020–11
  10. By: Yadav, Jayant
    Abstract: FTS in Business cycles examines the dynamic effects and empirical significance of Flight to Safety (FTS) shocks in the context of US business cycles. FTS represents a sudden preference for safe over risky investments and contains important information on agents’ time-varying risk-aversion and their expectations for future economic activity. This analysis presents an identification for FTS shocks using vector autoregressions (VAR). Sign restrictions are applied, while controlling for monetary policy and productivity shocks, on the price differential series between stocks and bonds in the US. Identified positive disturbances to this differential series are characterised as FTS shocks. The business cycle impact of FTS is calculated by applying the structural VAR model to the US economic data from 1955 to 2019. A sudden increase in risk aversion, which is displayed through the FTS shocks in the identified VAR model, has played a significant role in keeping investments low in the US. FTS shocks explain more than sixty per cent of the variation in US investments and they explain a higher proportion of macroeconomic fluctuations in periods around the Global financial crisis. This is a significant linkage when compared against the results of DSGE models enriched with time-varying risk-premium and investment technology. FTS also comes up ahead of news shocks in providing early signals of shifts in total factor productivity. This analysis is consistent with other comparable high-frequency, kernel-based measures of identifying FTS. The results also reveal the asymmetric impact on the business cycle of Flight to Safety and its complement Flight to Risk phenomenon. This asymmetry lends support to pursuing a cyclical risk-aversion driven view of business cycles.
    Keywords: Flight to Safety, Business Cycles, Structural VAR, Sign Restrictions,
    JEL: C32 C52 C58 E22 E32 E44 G11
    Date: 2020–10–31
  11. By: Edward J. Kane (Boston College)
    Abstract: This essay is part of a larger work on the history of Federal Reserve policymaking entitled Banking on Bull. The study seeks to explain why the instruments of central banking inevitably break down over time. A big part of the explanation is that policymakers want accounting measures of bank net worth to be flexible enough to allow bankers and regulators to slow the release of adverse information about distressed banks, particularly very large ones. Modern regulatory frameworks focus on maintaining what can be described as the adequacy of accounting capital. But this framework is bull, because in tough times, bank accountants know how to make losses disappear.
    Keywords: capital requirements, too big to fail, loss recognition, income-distribution effects
    JEL: E58 G21 G32
    Date: 2020–08–27
  12. By: Schuler, Kurt (The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise)
    Abstract: Apparently no up to date compilation exists of the various measures governments and the private sector have undertaken to address economic crises, especially financial crises. This paper tries to provide an overview that is comprehensive but brief. It is not an exhaustive analysis of crises, but rather an aid to thinking about how to respond to them, especially at their most acute.
    Keywords: Financial; crises
    Date: 2020–11
  13. By: Lewis, David; Suen, Ling
    Keywords: Public Economics
    Date: 2020–10–22

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