nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒10‒05
twenty-one papers chosen by

  1. A Dual Characterisation of Regulatory Arbitrage for Coherent Risk Measures By Martin Herdegen; Nazem Khan
  2. Loan syndication under Basel II: How firm credit ratings affect the cost of credit? By Hasan, Iftekhar; Kim, Suk-Joong; Politsidis, Panagiotis; Wu, Eliza
  3. Gold as a Financial Instrument By Gomis-Porqueras, Pedro; Shi, Shuping; Tan, David
  4. A Machine Learning Based Regulatory Risk Index for Cryptocurrencies By Xinwen Ni; Wolfgang Karl H\"ardle; Taojun Xe
  5. Investor Demands for Safety, Bank Capital, and Liquidity Measurement By Wayne Passmore; Judit Temesvary
  6. SRISKv2 - A Note By Alexander Jiron; Marco Migueis
  7. Volatility Forecasting with 1-dimensional CNNs via transfer learning By Bernadett Aradi; G\'abor Petneh\'azi; J\'ozsef G\'all
  8. Contagion in derivatives markets By Paddrick, Mark; Rajan, Sriram; Young, H. Peyton
  9. Network geometry and market instability By Areejit Samal; Hirdesh Kumar Pharasi; Sarath Jyotsna Ramaia; Harish Kannan; Emil Saucan; J\"urgen Jost; Anirban Chakraborti
  10. A Deep Learning Approach to Estimate Forward Default Intensities By Marc-Aurèle Divernois
  11. Regulatory stress tests and bank responses By Karel Janda; Oleg Kravtsov
  12. Crop Rotations and Risk Management in Mississippi Delta Agriculture By Stevens, Andrew W.; Bradley, William B.
  13. Building climate resilience through social protection in Brazil: the Garantia Safra public climate risk insurance programme By Elena Kühne
  14. The Stock Market Response to a "Regulatory Sine Curve" By Bo Sun; Xuan S. Tam; Eric R. Young
  15. Can Farmland be a Common Risk Factor in Asset Pricing Models By Noumir, Ashraf; Langemeier, Michael R.
  16. Covid-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent By M. Bel\'en Arouxet; Aurelio F. Bariviera; Ver\'onica E. Pastor; Victoria Vampa
  17. Prudence and prevention: Empirical evidence By Mayrhofer, Thomas; Schmitz, Hendrik
  18. Unobserved performance of hedge funds By Agarwal, Vikas; Ruenzi, Stefan; Weigert, Florian
  19. Uncertainty and monetary policy during extreme events By Giovanni Pellegrino; Efrem Castelnuovo; Giovanni Caggiano
  20. Hedge Fund Performance under Misspecified Models By David Ardia; Laurent Barras; Patrick Gagliardini; O. Scaillet
  21. Is Downside Risk Priced In Cryptocurrency Market? By Victoria Dobrynskaya

  1. By: Martin Herdegen; Nazem Khan
    Abstract: We revisit mean-risk portfolio selection in a one-period financial market where risk is quantified by a positively homogeneous risk measure $\rho$ on $L^1$. We first show that under mild assumptions, the set of optimal portfolios for a fixed return is nonempty and compact. However, unlike in classical mean-variance portfolio selection, it can happen that no efficient portfolios exist. We call this situation regulatory arbitrage, and prove that it cannot be excluded - unless $\rho$ is as conservative as the worst-case risk measure. After providing a primal characterisation, we focus our attention on coherent risk measures, and give a necessary and sufficient characterisation for regulatory arbitrage. We show that the presence or absence of regulatory arbitrage for $\rho$ is intimately linked to the interplay between the set of equivalent martingale measures (EMMs) for the discounted risky assets and the set of absolutely continuous measures in the dual representation of $\rho$. A special case of our result shows that the market does not admit regulatory arbitrage for Expected Shortfall at level $\alpha$ if and only if there exists an EMM $\mathbb{Q} \approx \mathbb{P}$ such that $\Vert \frac{\text{d}\mathbb{Q}}{\text{d}\mathbb{P}} \Vert_{\infty}
    Date: 2020–09
  2. By: Hasan, Iftekhar; Kim, Suk-Joong; Politsidis, Panagiotis; Wu, Eliza
    Abstract: This paper investigates how lenders react to borrowers’ rating changes under heterogeneous conditions and different regulatory regimes. Our findings suggest that corporate downgrades that increase capital requirements for lending banks under the Basel II framework are associated with increased loan spreads and deteriorating non-price loan terms relative to downgrades that do not affect capital requirements. Ratings exert an asymmetric impact on loan spreads, as these remain unresponsive to rating upgrades, even when the latter are associated with a reduction in risk weights for corporate loans. The increase in firm borrowing costs is mitigated in the presence of previous bank-firm lending relationships and for borrowers with relatively strong performance, high cash flows and low leverage.
    Keywords: corporate credit ratings, cost of credit, rating-contingent regulation, capital requirements, Basel II
    JEL: G21 G24 G28 G32
    Date: 2020–06
  3. By: Gomis-Porqueras, Pedro; Shi, Shuping; Tan, David
    Abstract: In this paper, we explore the effectiveness of gold as a hedging and safe haven instrument for a variety of market risks. Rather than confining the analysis to specific countries, we treat gold as a global asset and apply the novel Phillips, Shi and Yu (2015a,b) methodology to identify extreme price movements. This method accounts for both the level and speed of changes in price dynamics that better characterises periods of abnormally high risks. We find that gold is a strong safe haven for stock, European sovereign, and oil inflation market risks. We also show that gold is a strong hedge to inflationary and currency risks. We demonstrate that gold had exhibited safe haven properties during the 2020 Covid-19 crisis, and highlight the importance of considering explosive behaviour in identifying periods of risk.
    Keywords: Gold; Hedge; Safe Haven; Sovereign Debt; Equity Markets.
    JEL: E4 E44 G0
    Date: 2020–09–07
  4. By: Xinwen Ni; Wolfgang Karl H\"ardle; Taojun Xe
    Abstract: Cryptocurrencies' values often respond aggressively to major policy changes, but none of the existing indices informs on the market risks associated with regulatory changes. In this paper, we quantify the risks originating from new regulations on FinTech and cryptocurrencies (CCs), and analyse their impact on market dynamics. Specifically, a Cryptocurrency Regulatory Risk IndeX (CRRIX) is constructed based on policy-related news coverage frequency. The unlabeled news data are collected from the top online CC news platforms and further classified using a Latent Dirichlet Allocation model and Hellinger distance. Our results show that the machine-learning-based CRRIX successfully captures major policy-changing moments. The movements for both the VCRIX, a market volatility index, and the CRRIX are synchronous, meaning that the CRRIX could be helpful for all participants in the cryptocurrency market. The algorithms and Python code are available for research purposes on
    Date: 2020–09
  5. By: Wayne Passmore; Judit Temesvary
    Abstract: We construct a model of a bank's optimal funding choice, where the bank negotiates with both safety-driven short-term bondholders and (mostly) risk-taking long-term bondholders. We establish that investor demands for safety create a negative relationship between the bank's capital choices and short-term funding, as well as negative relationships between capital and common measures of bank liquidity. Consistent with our model, our bank-level empirical analysis of these capital-liquidity tradeoffs show (1) that bank liquidity measures have a strong and negative relationship to its capital ratio for both large and small banks, and (2) that this relationship has weakened with the advent of stronger liquidity regulation. Our results suggest that the safety concerns of bank debt investors may underlie capital-liquidity tradeoffs and that a bank's share of collateralized short-term debt may be a more robust measure of bank liquidity.
    Keywords: Safe assets; Bank liquidity; Liquidity regulation; Capitalization; Bank balance sheet management
    JEL: G11 G18 G21 G23 G28
    Date: 2020–09–18
  6. By: Alexander Jiron; Marco Migueis
    Abstract: SRISK is a very influential metric of the systemic risk posed by financial firms. However, SRISK suffers from a conceptual flaw in its capital shortfall calculation. This note proposes a modified version of this metric, SRISKv2, which corrects this flaw and provides a more sensible metric of the systemic risk posed by financial firms.
    Date: 2020–09–18
  7. By: Bernadett Aradi; G\'abor Petneh\'azi; J\'ozsef G\'all
    Abstract: Volatility is a natural risk measure in finance as it quantifies the variation of stock prices. A frequently considered problem in mathematical finance is to forecast different estimates of volatility. What makes it promising to use deep learning methods for the prediction of volatility is the fact, that stock price returns satisfy some common properties, referred to as `stylized facts'. Also, the amount of data used can be high, favoring the application of neural networks. We used 10 years of daily prices for hundreds of frequently traded stocks, and compared different CNN architectures: some networks use only the considered stock, but we tried out a construction which, for training, uses much more series, but not the considered stocks. Essentially, this is an application of transfer learning, and its performance turns out to be much better in terms of prediction error. We also compare our dilated causal CNNs to the classical ARIMA method using an automatic model selection procedure.
    Date: 2020–09
  8. By: Paddrick, Mark; Rajan, Sriram; Young, H. Peyton
    Abstract: A major credit shock can induce large intraday variation margin payments between counterparties in derivatives markets, which may force some participants to default on their payments. These payment shortfalls become amplified as they cascade through the network of exposures. Using detailed Depository Trust & Clearing Corporation data, we model the full network of exposures, shock-induced payments, initial margin collected, and liquidity buffers for about 900 firms operating in the U.S. credit default swaps market. We estimate the total amount of contagion, the marginal contribution of each firm to contagion, and the number of defaulting firms for a systemic shock to credit spreads. A novel feature of the model is that it allows for a range of behavioral responses to balance sheet stress, including delayed or partial payments. The model provides a framework for analyzing the relative effectiveness of different policy options, such as increasing margin requirements or mandating greater liquidity reserves.
    Keywords: financial networks; contagion; stress testing; credit default swaps
    JEL: F3 G3
    Date: 2020–08
  9. By: Areejit Samal; Hirdesh Kumar Pharasi; Sarath Jyotsna Ramaia; Harish Kannan; Emil Saucan; J\"urgen Jost; Anirban Chakraborti
    Abstract: The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Only recently, researchers have started devoting attention to the higher-order architecture of complex financial systems, that can significantly enhance our ability to estimate systemic risk as well as measure the robustness of financial systems in terms of market efficiency. Geometry-inspired network measures, such as the Ollivier-Ricci curvature and Forman-Ricci curvature, can be used to capture the network fragility and continuously monitor financial dynamics. Here, we explore the utility of such discrete Ricci-type curvatures in characterizing the structure of financial systems, and further, evaluate them as generic indicators of the market instability. For this purpose, we examine the daily returns from a set of stocks comprising the USA S&P-500 and the Japanese Nikkei-225 over a 32-year period, and monitor the changes in the edge-centric network curvatures. We find that the different geometric measures capture well the system-level features of the market and hence we can distinguish between the normal or 'business-as-usual' periods and all the major market crashes. This can be very useful in strategic designing of financial systems and regulating the markets in order to tackle financial instabilities.
    Date: 2020–09
  10. By: Marc-Aurèle Divernois (EPFL; Swiss Finance Institute)
    Abstract: This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous literature is to allow the estimation of non-linear forward intensities by using neural networks instead of classical maximum likelihood estimation. The model specification allows an easy replication of previous literature using linear assumption and shows the improvement that can be achieved.
    Keywords: Bankruptcy, Credit Risk, Default, Machine Learning, Neural Networks, Doubly Stochastic, Forward Poisson Intensities
    JEL: C22 C23 C53 C58 G33 G34
    Date: 2020–07
  11. By: Karel Janda; Oleg Kravtsov
    Abstract: In this paper, we investigate how the regulatory stress test framework in the European Union affects banks’ investment decisions and portfolio choices. Using the causal inference and event study methodology, we document a substantial impact of EU-wide stress tests in 2011, 2014 and 2016 on the banks’ portfolio strategies. The banks subject to regulatory stress tests tend to structure their portfolios with lower risk assets that is reflected in a decline in risk-weighted assets as compared to the control group. At the same time, the dynamic of realized risk that is measured by the proportion of non-performing exposure in portfolios remains unaffected. The estimates based on two alternative subsamples indicate that the magnitude of such effect rise with the increase in the size of the bank´s assets.
    Keywords: regulatory stress test, capital regulation, heterogeneous treatment effect, event study, instrumental variable
    JEL: G20 G21 G28
    Date: 2020–08
  12. By: Stevens, Andrew W.; Bradley, William B.
    Keywords: Production Economics, Risk and Uncertainty, Agricultural Finance
    Date: 2020–07
  13. By: Elena Kühne (IPC-IG)
    Abstract: This Policy Research Brief examines social protection's role in building climate resilience based on evidence from the Garantia Safra programme, a public index-based climate risk insurance scheme in Brazil.
    Keywords: protection; resilience; climate change adaptation; disaster risk management; climate risk insurance; smallholders; rural development; Garantia Safra
    Date: 2020–08
  14. By: Bo Sun; Xuan S. Tam; Eric R. Young
    Abstract: We construct new indicators of financial regulatory intensity and find evidence that a "regulatory sine curve" generally exists: regulatory oversight increases following a recession and wanes as the economy returns to normalcy. We then build an asset pricing model, based on the idea that regulatory oversight both deters incentives to commit fraud ex ante and reveals hidden negative information ex post. Our calibration suggests that these mechanisms can be quantitatively important for stock price dynamics.
    Keywords: Cyclical financial regulation; Stock crash risk; Gradual boom and sudden crash
    JEL: G12 G30 K20
    Date: 2020–09–18
  15. By: Noumir, Ashraf; Langemeier, Michael R.
    Keywords: Agricultural Finance, Risk and Uncertainty, Agribusiness
    Date: 2020–07
  16. By: M. Bel\'en Arouxet; Aurelio F. Bariviera; Ver\'onica E. Pastor; Victoria Vampa
    Abstract: Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into complex, high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of eleven important coins. Our study covers the pre-Covid-19 and the subsequent pandemia period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of Covid-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.
    Date: 2020–09
  17. By: Mayrhofer, Thomas; Schmitz, Hendrik
    Abstract: Theoretical papers show that optimal prevention decisions in the sense of selfprotection (i.e., primary prevention) depend not only on the level of (second-order) risk aversion but also on higher-order risk preferences such as prudence (third-order risk aversion). We study empirically whether these theoretical results hold and whether prudent individuals show less preventive (self-protection) effort than non-prudent individuals. We use a unique dataset that combines data on higher-order risk preferences and various measures of observed real-world prevention behavior. We find that prudent individuals indeed invest less in self-protection as measured by influenza vaccination. This result is driven by high risk individuals such as individuals >60 years of age or chronically ill. We do not find a clear empirical relationship between riskpreferences and prevention in the sense of self-insurance (i.e. secondary prevention). Neither risk aversion nor prudence is related to cancer screenings such as mammograms, Pap smears or X-rays of the lung.
    Keywords: prudence,risk preferences,prevention,vaccination,screening
    JEL: D12 D81 I12
    Date: 2020
  18. By: Agarwal, Vikas; Ruenzi, Stefan; Weigert, Florian
    Abstract: We investigate hedge fund firms' unobserved performance (UP), measured as the riskadjusted return difference between a fund firm's reported return and hypothetical portfolio return derived from its disclosed long equity holdings. Fund firms with high UP outperform those with low UP by 7.2% p.a. after accounting for typical hedge fund risk factors. In a horse-race, UP better forecasts fund performance than other predictors. We find that UP is positively associated with a fund firm's intraquarter trading in equity positions, derivatives usage, short selling, and confidential holdings. UP exhibits significant persistence but investors do not yet use it for manager selection.
    Keywords: Hedge fund skill,Confidential Holdings,Derivative Usage,Short Selling,Unobserved Performance
    JEL: G11 G23
    Date: 2020
  19. By: Giovanni Pellegrino; Efrem Castelnuovo; Giovanni Caggiano
    Abstract: How damaging are uncertainty shocks during extreme events such as the great recession and the Covid-19 outbreak? Can monetary policy limit output losses in such situations? We use a nonlinear VAR framework to document the large response of real activity to a financial uncertainty shock during the great recession. We replicate this evidence with an estimated DSGE framework featuring a concept of uncertainty comparable to that in our VAR. We employ the DSGE model to quantify the impact on real activity of an uncertainty shock under different Taylor rules estimated with normal times vs. great recession data (the latter associated with a stronger response to output). We find that the uncertainty shock-induced output loss experienced during the 2007-09 recession could have been twice as large if policymakers had not responded aggressively to the abrupt drop in output in 2008Q3. Finally, we use our estimated DSGE framework to simulate different paths of uncertainty associated to different hypothesis on the evolution of the coronavirus pandemic. We find that: i) Covid-19-induced uncertainty could lead to an output loss twice as large as that of the great recession; ii) aggressive monetary policy moves could reduce such loss by about 50%.
    Keywords: Uncertainty shock, nonlinear IVAR, nonlinear DSGE framework, minimum-distance estimation, great recession, Covid-19
    JEL: C22 E32 E52
    Date: 2020–09
  20. By: David Ardia (HEC Montreal - Department of Decision Sciences); Laurent Barras (McGill University - Desautels Faculty of Management); Patrick Gagliardini (University of Lugano; Swiss Finance Institute); O. Scaillet (University of Geneva GSEM and GFRI; Swiss Finance Institute; University of Geneva - Research Center for Statistics)
    Abstract: We develop a new approach for evaluating performance across hedge funds. Our approach allows for performance comparisons between models that are misspecified – a common feature given the numerous factors that drive hedge fund returns. The empirical results show that the standard models used in previous work omit similar factors because they (i) perform exactly like the CAPM, and (ii) produce large and positive alphas. In contrast, we observe a large and statistically significant decrease in performance with a new model formed with alternative factors that capture variance, correlation, liquidity, betting-against-beta, carry, and time-series momentum strategies. Overall, the results suggest that the average returns of hedge funds are largely explained by mechanical trading strategies.
    Keywords: Hedge funds, performance, model misspecification, large panel
    JEL: G11 G12 C14 C33 C58
    Date: 2020–09
  21. By: Victoria Dobrynskaya (National Research University Higher School of Economics)
    Abstract: I look at the cryptocurrency market through the prism of standard multifactor asset-pricing models with particular attention to the downside market risk. The analysis for 1,700 coins reveals that there is a significant heterogeneity in the exposure to the downside market risk, and that a higher downside risk exposure is associated with higher average returns. The extra downside risk is priced with a statistically significant premium in cross-sectional regressions. Adding the downside risk component to the CAPM and the 3-factor model for cryptocurrencies improves the explanatory power of the models significantly. The downside risk is orthogonal to the size and momentum risks and constitutes an important forth component in the multifactor cryptocurrency pricing model.
    Keywords: cryptocurrency, coins, cryptofinance, alternative investments, downside risk, DR-CAPM
    JEL: D14 G12 G15
    Date: 2020

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