nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒09‒27
fourteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Financial Turbulence, Systemic Risk and the Predictability of Stock Market Volatility By Afees A. Salisu; Riza Demirer; Rangan Gupta
  2. Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting? By Simon Fritzsch; Maike Timphus; Gregor Weiss
  3. Foreign vulnerabilities, domestic risks: the global drivers of GDP-at-Risk By Lloyd, Simon; Manuel, Ed; Panchev, Konstantin
  4. Risk-to-Buffer: Setting Cyclical and Structural Capital Buffers through Banks Stress Tests By Cyril Couaillier; Valerio Scalone
  5. Nonparametric Estimation of Truncated Conditional Expectation Functions By Tomasz Olma
  6. Determining the banking solvency risk in times of COVID-19 through Gram-Charlier expansions By Lina M Cortés; Juan F. Rendón; Javier Perote
  7. Risk-taking and uncertainty: do contingent convertible (CoCo) bonds increase the risk appetite of banks? By Fatouh, Mahmoud; Neamțu, Ioana; van Wijnbergen, Sweder
  8. Scenario generation for market risk models using generative neural networks By Solveig Flaig; Gero Junike
  9. Synthetic Leverage and Fund Risk-Taking By Fricke, Daniel
  10. Litigation Risk and Corporate Cash Holdings:Evidence from a Legal Shock By Tommaso Oliviero; Min Park; Hong Zou
  11. Fractional Growth Portfolio Investment By Anthony E. Brockwell
  12. The countercyclical capital buffer and the composition of bank lending By Raphael Auer; Alexandra Matyunina; Steven Ongena
  13. How Serious is the Measurement-Error Problem in Risk-Aversion Tasks? By Fabien Perez; Guillaume Hollard; Radu Vranceanu
  14. Risk-Adjusted Valuation for Real Option Decisions By Carol Alexander; Xi Chen; Charles Ward

  1. By: Afees A. Salisu (Centre for Econometric & Allied Research, University of Ibadan, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: This paper adds a novel perspective to the literature by exploring the predictive performance of two relatively unexplored indicators of financial conditions, i.e. financial turbulence and systemic risk, over stock market volatility in a sample of seven emerging and advanced economies. The two financial indicators that we utilize in our predictive setting provide a unique perspective to market conditions as they directly relate to portfolio performance metrics from both a volatility and co-movement perspective and, unlike other macro-financial indicators of uncertainty or risk, can be integrated into diversification models within a forecasting and portfolio design setting. Since the two predictors are available at weekly frequency, and we want to provide forecast at the daily level, we use the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) approach. The results suggest that incorporating the two financial indicators (singly and jointly) indeed improves the out-of-sample predictive performance of stock market volatility models across both the short and long horizons. We observe that the financial turbulence indicator that captures asset price deviations from historical patterns does a better job when it comes to the out-of-sample prediction of future returns compared to the measure of systemic risk, captured by the absorption ratio. The outperformance of the financial turbulence indicator implies that unusual deviations in not only asset returns, but also correlation patterns clearly play a role in the persistence of return volatility. Overall, the findings provide an interesting opening for portfolio design purposes in that financial indicators that are directly associated with portfolio diversification performance metrics can also be utilized for forecasting purposes with significant implications for dynamic portfolio allocation strategies.
    Keywords: Systemic risk, Financial turbulence, Stock market, MIDAS models
    JEL: C32 D8 E32 G15
    Date: 2021–09
  2. By: Simon Fritzsch; Maike Timphus; Gregor Weiss
    Abstract: Copulas. We study the model risk of multivariate risk models in a comprehensive empirical study on Copula-GARCH models used for forecasting Value-at-Risk and Expected Shortfall. To determine whether model risk inherent in the forecasting of portfolio risk is caused by the candidate marginal or copula models, we analyze different groups of models in which we fix either the marginals, the copula, or neither. Model risk is economically significant, is especially high during periods of crisis, and is almost completely due to the choice of the copula. We then propose the use of the model confidence set procedure to narrow down the set of available models and reduce model risk for Copula-GARCH risk models. Our proposed approach leads to a significant improvement in the mean absolute deviation of one day ahead forecasts by our various candidate risk models.
    Date: 2021–09
  3. By: Lloyd, Simon (Bank of England); Manuel, Ed (Bank of England); Panchev, Konstantin (University of Oxford)
    Abstract: We study how foreign financial developments influence the conditional distribution of domestic GDP growth. Within a quantile regression setup, we propose a method to parsimoniously account for foreign vulnerabilities using bilateral-exposure weights when assessing downside macroeconomic risks. Using a panel data set of advanced economies, we show that tighter foreign financial conditions and faster foreign credit-to-GDP growth are associated with a more severe left tail of domestic GDP growth, even when controlling for domestic indicators. The inclusion of foreign indicators significantly improves estimates of ‘GDP-at-Risk’, a summary measure of downside risks. In turn, this yields time-varying estimates of higher GDP growth moments that are interpretable and provide advanced warnings of crisis episodes. Decomposing historical estimates of GDP-at-Risk into domestic and foreign sources, we show that foreign shocks are a key driver of domestic macroeconomic tail risks.
    Keywords: Financial stability; GDP-at-Risk; international spillovers; local projections; quantile regression; tail risk
    JEL: E44 E58 F30 F41 F44 G01
    Date: 2021–09–17
  4. By: Cyril Couaillier; Valerio Scalone
    Abstract: In this work we present the Risk-to-Buffer: a new framework to jointly calibrate cyclical and structural capital buffers, based on the integration of a non-linear macroeconomic model with a Stress test model. The macroeconomic model generates scenarios whose severity depends on the level of cyclical risk. Risk-related scenarios feed into a banks' Stress test model. Banks' capital losses deriving from the reference-risk scenario are used to calibrate the structural buffer. Additional losses associated to the current-risk scenario are used to calibrate the cyclical buffer.
    Keywords: Financial Vulnerability, Macroprudential Policy, Non-linear Models, Macroprudential Space, Deb
    JEL: C32 E51 E58 G51
    Date: 2021
  5. By: Tomasz Olma
    Abstract: Truncated conditional expectation functions are objects of interest in a wide range of economic applications, including income inequality measurement, financial risk management, and impact evaluation. They typically involve truncating the outcome variable above or below certain quantiles of its conditional distribution. In this paper, based on local linear methods, a novel, two-stage, nonparametric estimator of such functions is proposed. In this estimation problem, the conditional quantile function is a nuisance parameter that has to be estimated in the first stage. The proposed estimator is insensitive to the first-stage estimation error owing to the use of a Neyman-orthogonal moment in the second stage. This construction ensures that inference methods developed for the standard nonparametric regression can be readily adapted to conduct inference on truncated conditional expectations. As an extension, estimation with an estimated truncation quantile level is considered. The proposed estimator is applied in two empirical settings: sharp regression discontinuity designs with a manipulated running variable and randomized experiments with sample selection.
    Date: 2021–09
  6. By: Lina M Cortés; Juan F. Rendón; Javier Perote
    JEL: C14 C22 C54 G21 G28
    Date: 2021–09–20
  7. By: Fatouh, Mahmoud (Bank of England); Neamțu, Ioana (Bank of England); van Wijnbergen, Sweder (University of Amsterdam)
    Abstract: We assess the impact of contingent convertible (CoCo) bonds and the wealth transfers they imply conditional on conversion on the risk-taking behaviour of the issuing bank. We also test for regulatory arbitrage: do banks try to maintain risk-taking incentives by issuing CoCo bonds, when regulators reduce them through higher capitalisation ratios? While we test for, and reject sample selection bias, we show that CoCo bonds issuance has a strong positive effect on risk-taking behaviour, and so do conversion parameters that reduce dilution of existing shareholders upon conversion. Higher economic volatility amplifies the impact of CoCo bonds on risk-taking.
    Keywords: Contingent convertible bonds; risk-taking; bank capital structure; selection bias
    JEL: G01 G11 G21 G32
    Date: 2021–08–27
  8. By: Solveig Flaig; Gero Junike
    Abstract: In this research, we show how to expand existing approaches of generative adversarial networks (GANs) being used as economic scenario generators (ESG) to a whole internal model - with enough risk factors to model the full band-width of investments for an insurance company and for a one year horizon as required in Solvency 2. For validation of this approach as well as for optimisation of the GAN architecture, we develop new performance measures and provide a consistent, data-driven framework. Finally, we demonstrate that the results of a GAN-based ESG are similar to regulatory approved internal models in Europe. Therefore, GAN-based models can be seen as an assumption-free data-driven alternative way of market risk modelling.
    Date: 2021–09
  9. By: Fricke, Daniel
    Abstract: Mutual fund risk-taking via active portfolio rebalancing varies both in the cross-section and over time. In this paper, I show that the same is true for funds’ off-balance sheet risk-taking, even after controlling for on-balance sheet activities. For this purpose, I propose a novel measure of synthetic leverage, which can be estimated based on publicly available information. In the empirical application, I show that German equity funds have increased their risk-taking via synthetic leverage from mid-2015 up until early 2019. In the cross-section, I find that synthetically leveraged funds tend to underperform and display higher levels of fragility. JEL Classification: G11, G23, E44
    Keywords: derivatives, leverage, mutual funds, risk-taking, securities lending
    Date: 2021–09
  10. By: Tommaso Oliviero (Università di Napoli Federico II and CSEF); Min Park (University of Bristol); Hong Zou (University of Hong Kong)
    Abstract: Theory offers two diverging views on the effect of ex-ante litigation risk on corporate cash holdings. To test the effect, this paper exploits the phase-by-phase introduction of securities class actions in Korea. Following the increase in litigation risk, firms significantly increase their cash holdings. The effects are stronger for firms with high operating cash flow volatility, no D&O insurance coverage, and tighter financial constraints. The results hold robustly in differences-in-differences and regression discontinuity designs. The causal estimates, together with the increase in corporate litigation risk worldwide, put forth a novel link, unexplored in the literature, between litigation risk and the secular increase in cash holdings.
    Keywords: Liquidity; cash holdings; litigation risk; difference-in-differences; regression discontinuity.
    JEL: G30 G32 K22
    Date: 2021–09–06
  11. By: Anthony E. Brockwell
    Abstract: We review some fundamental concepts of investment from a mathematical perspective, concentrating specifically on fractional-Kelly portfolios, which allocate a fraction of wealth to a growth-optimal portfolio while the remainder collects (or pays) interest at a risk-free rate. We elucidate a coherent continuous-parameter time-series framework for analysis of these portfolios, explaining relationships between Sharpe ratios, growth rates, and leverage. We see how Kelly's criterion prescribes the same leverage as Markowitz mean-variance optimization. Furthermore, for fractional Kelly portfolios, we state a simple distributional relationship between portfolio Sharpe ratio, the fractional coefficient, and portfolio log-returns. These results provide critical insight into realistic expectations of growth for different classes of investors, from individuals to quantitative trading operations. We then illustrate application of the results by analyzing performance of various bond and equity mixes for an investor. We also demonstrate how the relationships can be exploited by a simple method-of-moments calculation to estimate portfolio Sharpe ratios and levels of risk deployment, given a fund's reported returns.
    Date: 2021–09
  12. By: Raphael Auer (Swiss National Bank; Bank for International Settlements (BIS)); Alexandra Matyunina (University of Zurich; Swiss Finance Institute); Steven Ongena (University of Zurich - Department of Banking and Finance; Swiss Finance Institute; KU Leuven; Centre for Economic Policy Research (CEPR))
    Abstract: Do targeted macroprudential measures impact non-targeted sectors too? We answer this question by investigating the compositional changes in the supply of credit by Swiss banks, exploiting their differential exposure to the activation in 2013 of the countercyclical capital buffer (CCyB) which targeted banks’ exposure to residential mortgages. We find that the additional capital requirements stemming from the activation of the CCyB causes higher growth in banks’ commercial lending. While banks lend more to all categories of firms, including larger corporate borrowers in the syndicated loan market, smaller and riskier firms are the primary beneficiaries of the new macroprudential measure. However, the interest rates and other costs of obtaining credit for these firms increase as well.
    Keywords: macroprudential policy, spillovers, credit, bank capital, systemic risk, syndicated loan market
    JEL: E51 E58 E60 G01 G21 G28
    Date: 2021–09
  13. By: Fabien Perez (CREST, ENSAE, INSEE); Guillaume Hollard (CREST, EcolePolytechnique, CNRS); Radu Vranceanu (ESSEC Business School and THEMA)
    Abstract: This paper analyzes within-session test/retest data from four different tasks used to elicit risk attitudes. Maximum-likelihood and non-parametric estimations on 16 datasets reveal that, irrespective of the task, measurement error accounts for approximately 50% of the variance of the observed variable capturing risk attitudes. The consequences of this large noise element are evaluated by means of simulations. First, as predicted by theory, the coefficient on the risk measure in univariate OLS regressions is attenuated to approximately half of its true value, irrespective of the sample size. Second, the risk-attitude measure may spuriously appear to be insignificant, especially in small samples. Unlike the measurement error arising from within-individual variability, rounding has little influence on significance and biases. In the last part, we show that instrumental-variable estimation and the ORIV method, developed by Gillen et al. (2019), both of which require test/retest data, can eliminate the attenuation bias, but do not fully solve the insignificance problem in small samples. Increasing the number of observations to N=500 removes most of the insignificance issues.
    Keywords: Measurement error, Risk-aversion, Test/retest, ORIV, Sample size
    JEL: C18 C26 C91 D81
    Date: 2021–07–08
  14. By: Carol Alexander; Xi Chen; Charles Ward
    Abstract: We model investor heterogeneity using different required returns on an investment and evaluate the impact on the valuation of an investment. By assuming no disagreement on the cash flows, we emphasize how risk preferences in particular, but also the costs of capital, influence a subjective evaluation of the decision to invest now or retain the option to invest in future. We propose a risk-adjusted valuation model to facilitate investors' subjective decision making, in response to the market valuation of an investment opportunity. The investor's subjective assessment arises from their perceived misvaluation of the investment by the market, so projected cash flows are discounted using two different rates representing the investor's and the market's view. This liberates our model from perfect or imperfect hedging assumptions and instead, we are able to illustrate the hedging effect on the real option value when perceptions of risk premia diverge. During crises periods, delaying an investment becomes more valuable as the idiosyncratic risk of future cash flows increases, but the decision-maker may rush to invest too quickly when the risk level is exceptionally high. Our model verifies features established by classical real-option valuation models and provides many new insights about the importance of modelling divergences in decision-makers risk premia, especially during crisis periods. It also has many practical advantages because it requires no more parameter inputs than basic discounted cash flow approaches, such as the marketed asset disclaimer method, but the outputs are much richer. They allow for complex interactions between cost and revenue uncertainties as well as an easy exploration of the effects of hedgeable and un-hedgeable risks on the real option value. Furthermore, we provide fully-adjustable Python code in which all parameter values can be chosen by the user.
    Date: 2021–09

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