nep-rmg New Economics Papers
on Risk Management
Issue of 2019‒11‒18
twenty-two papers chosen by

  1. Market-implied systemic risk and shadow capital adequacy By Chatterjee, Somnath; Jobst, Andreas
  2. Credit, capital and crises: a GDP-at-Risk approach By Aikman, David; Bridges, Jonathan; Hacioglu Hoke, Sinem; O’Neill, Cian; Raja, Akash
  3. A model free approach to the pricing of downside risk in argentinean stocks By José P. Dapena; Juan A. Serur; Julián R. Siri
  4. Dual Representation of Expectile based Expected Shortfall and Its Properties By Samuel Drapeau; Mekonnen Tadese
  5. Incremental Risk Charge Methodology By Xiao, Tim
  6. How Safe are Central Counterparties in Credit Default Swap Markets? By H Peyton Young; Mark Paddrik
  7. Economic uncertainty and bank risk: Evidence from emerging economies By Jeon, Bang; Wu, Ji; Yao, Yao; Chen, Minghua
  8. Contagion in Derivatives Markets By H Peyton Young; Mark Paddrik; Sriram Rajan
  9. A constrained hierarchical risk parity algorithm with cluster-based capital allocation By Johann Pfitzinger; Nico Katzke
  10. How Serious is the Measurement-Error Problem in a Popular Risk-Aversion Task? By Fabien Perez; Guillaume Hollard; Radu Vranceanu; Delphine Dubart
  11. China's Shadow Banking: Bank's Shadow and Traditional Shadow Banking By Guofeng Sun
  12. Low interest rates, bank's search-for-yield behavior and financial portfolio management By Lojak, Benjamin; Makarewicz, Tomasz; Proaño Acosta, Christian
  13. An Economic Examination of Collateralization in Different Financial Markets By Xiao, Tim
  14. Infinite dimensional polynomial processes By Christa Cuchiero; Sara Svaluto-Ferro
  15. Credit supply, uncertainty and trust: the role of social capital By Maddalena Galardo; Maurizio Lozzi; Paolo Emilio Mistrulli
  16. Crypto assets: the role of ICO tokens within a well-diversified portfolio By Saman Adhami; Dominique Guégan
  17. Do "speed bumps" prevent accidents in financial markets? By Gonçalves, Jorge; Kräussl, Roman; Levin, Vladimir
  18. Improving portfolios global performance using a cleaned and robust covariance matrix estimate By Emmanuelle Jay; Thibault Soler; Eugénie Terreaux; Jean-Philippe Ovarlez; Frédéric Pascal; Philippe De Peretti; Christophe Chorro
  19. Lao People’s Democratic Republic; Technical Assistance Report-Risk-Based Banking Supervision By International Monetary Fund
  20. Quantifying the Impact of Foreign Economic Uncertainty on the U.S. Economy By Juan M. Londono; Sai Ma; Beth Anne Wilson
  21. Attention to the tail(s): global financial conditions and exchange rate risks By Eguren-Martin, Fernando; Sokol, Andrej
  22. Effective Lower Bound Risk By Timothy S. Hills; Taisuke Nakata; Sebastian Schmidt

  1. By: Chatterjee, Somnath (Bank of England); Jobst, Andreas (International Monetary Fund)
    Abstract: This paper presents a forward-looking approach to measure systemic solvency risk using contingent claims analysis (CCA) as a theoretical foundation for determining an institution’s default risk based on the uncertainty in its asset value relative to promised debt payments over time. Default risk can be quantified as market-implied expected losses calculated from integrating equity market and balance sheet information in a structural default risk model. The expected losses of multiple banks and their non-parametric dependence structure define a multivariate distribution that generates portfolio-based estimates of the joint default risk using the aggregation technique of the Systemic CCA framework (Jobst and Gray, 2013). This market-implied valuation approach (‘shadow capital adequacy’) endogenises bank solvency as a probabilistic concept based on the perceived default risk (in contrast to accounting-based prudential measures of capital adequacy). The presented model adds to the literature of analytical tools estimating market-implied systemic risk by augmenting the CCA approach with a jump diffusion process of asset changes to inform a more comprehensive and flexible assessment of common vulnerabilities to tail risks of the four largest UK commercial banks.
    Keywords: Systemic risk; contingent claims analysis; jump diffusion; CoVaR; systemic expected shortfall; conditional tail expectation; capital adequacy
    JEL: C61 C63 G01 G21 G28
    Date: 2019–09–16
  2. By: Aikman, David (Bank of England); Bridges, Jonathan (Bank of England); Hacioglu Hoke, Sinem (Bank of England and Data Analytics for Finance and Macro (KCL)); O’Neill, Cian (Bank of England); Raja, Akash (Bank of England)
    Abstract: How can macroeconomic tail risks originating from financial vulnerabilities be monitored systematically over time? This question lies at the heart of operationalising the macroprudential policy regimes that have developed around the world in response to the global financial crisis. Using quantile regressions applied to a panel dataset of 16 advanced economies, we examine how downside risk to growth over the medium term, GDP-at-Risk, is affected by a set of macroprudential indicators. We find that credit booms, property price booms and wide current account deficits each pose material downside risks to growth at horizons of three to five years. We find that such downside risks can be partiall y mitigated, however, by increasing the capitalisation of the banking system. We estimate that across our sample of countries, GDP-at-Risk, defined as the 5th quantile of the projected GDP growth distribution over three years, on average deteriorated by around 4.5 percentage points cumulatively in the run-up to the crisis. Our estimates suggest that an increase in bank capital equivalent to a countercyclical capital buffer rate of 2.5% (5%) would have been sufficient to mitigate up to 20% (40%) of this increase in medium-term macroeconomic tail risk.
    Keywords: Financial stability; GDP-at-Risk; macroprudential policy; quantile regressions; local projections
    JEL: G01 G18 G21
    Date: 2019–09–20
  3. By: José P. Dapena; Juan A. Serur; Julián R. Siri
    Abstract: The return dynamics of Argentina's main stock index, the SP Mer.Val., show a high level of volatility, signaling a higher degree of downside risk. To hedge against that specific risk, investors could buy put options. However, the Argentinean capital markets lacks variety of hedging contracts. The basic availability of put options depends on the possibility of short selling the underlying security, i.e. transfer risk to a third party, something not properly developed in the domestic market. Since data processing power has geometrically increased in the last decades and some mathematic formulas that were helpful for calculation had been surpassed by data gathering and processing that helps to find a better estimate when necessary, in this paper we show the point calculating protection against downside risk in the Argentinean stock market, using real data and programming an algorithm to perform calculations instead of resorting the standard Black-Scholes-Merton formulae, by means of a model free approach to acknowledge the issue.
    Keywords: Asset pricing, options pricing, insurance, capital markets
    JEL: C1 C3 N2 G11
    Date: 2019–11
  4. By: Samuel Drapeau; Mekonnen Tadese
    Abstract: The expectile can be considered as a generalization of quantile. While expected shortfall is a quantile based risk measure, we study its counterpart -- the expectile based expected shortfall -- where expectile takes the place of quantile. We provide its dual representation in terms of Bochner integral. Among other properties, we show that it is bounded from below in terms of convex combinations of expected shortfalls, and also from above by the smallest law invariant, coherent and comonotonic risk measure, for which we give the explicit formulation of the corresponding distortion function. As a benchmark to the industry standard expected shortfall we further provide its comparative asymptotic behavior in terms of extreme value distributions. Based on these results, we finally compute explicitly the expectile based expected shortfall for some selected class of distributions.
    Date: 2019–11
  5. By: Xiao, Tim
    Abstract: The incremental risk charge (IRC) is a new regulatory requirement from the Basel Committee in response to the recent financial crisis. Notably few models for IRC have been developed in the literature. This paper proposes a methodology consisting of two Monte Carlo simulations. The first Monte Carlo simulation simulates default, migration, and concentration in an integrated way. Combining with full re-valuation, the loss distribution at the first liquidity horizon for a subportfolio can be generated. The second Monte Carlo simulation is the random draws based on the constant level of risk assumption. It convolutes the copies of the single loss distribution to produce one year loss distribution. The aggregation of different subportfolios with different liquidity horizons is addressed. Moreover, the methodology for equity is also included, even though it is optional in IRC.
    Date: 2018–08–16
  6. By: H Peyton Young; Mark Paddrik
    Abstract: We propose a general framework for estimating the vulnerability to default by a central counterparty (CCP) in the credit default swaps market. Unlike conventional stress testing approaches, which estimate the ability of a CCP to withstand nonpayment by its two largest counterparties, we study the direct and indirect effects of nonpayment by members and/or their clients through the full network of exposures. We illustrate the approach for the U.S. credit default swaps market under shocks that are similar in magnitude to the Federal Reserve’s stress tests. The analysis indicates that conventional stress testing approaches may underestimate the potential vulnerability of the main CCP for this market.
    Keywords: Credit default swaps, central counterparties, stress testing, systemic risk, financial networks
    JEL: D85 G01 G17 L14
    Date: 2019–11–04
  7. By: Jeon, Bang (Drexel University); Wu, Ji (Southwestern University of Finance and Economics); Yao, Yao (Southwestern University of Finance and Economics); Chen, Minghua (Southwestern University of Finance and Economics)
    Abstract: This paper examines the impact of economic uncertainty on the risk of banks in emerging markets. Using the data of approximately 1500 banks in 34 emerging economies during the period of 2000-2016, we find consistent evidence that bank risk increases with the level of uncertainty. Economic uncertainty mainly exerts its impact by affecting banks’ return and its volatility, and the effect of nominal uncertainty is seemingly more conspicuous relative to that of real uncertainty. We also find that the effect of uncertainty on bank risk is conditional on banks’ characteristics such as size and efficiency. Moreover, macroprudential policies can play a stabilizing force by mitigating bank risk as economic uncertainty surges.
    Keywords: Economic uncertainty; Bank risk; Emerging economies
    JEL: G15 G21
    Date: 2019–10–22
  8. By: H Peyton Young; Mark Paddrik; Sriram Rajan
    Abstract: A major credit shock can induce large intra-day 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 DTCC 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: D85 G23 L1
    Date: 2019–11–04
  9. By: Johann Pfitzinger (Department of Economics, Stellenbosch University); Nico Katzke (Department of Economics, Stellenbosch University & Prescient Securities, Cape Town)
    Abstract: Hierarchical Risk Parity (HRP) is a risk-based portfolio optimisation algorithm, which has been shown to generate diversified portfolios with robust out-of-sample properties without the need for a positive-definite return covariance matrix (Lopez de Prado 2016). The algorithm applies machine learning techniques to identify the underlying hierarchical correlation structure of the portfolio, allowing clusters of similar assets to compete for capital. The resulting allocation is both well-diversified over risk sources and intuitively appealing. This paper proposes a method of fully exploiting the information created by the clustering process, achieving enhanced out-of-sample risk and return characteristics. In addition, a practical approach to calculating HRP weights under box and group constraints is introduced. A comprehensive set of portfolio simulations over 6 equity universes demonstrates the appeal of the algorithm for portfolios consisting of 20 - 200 assets. HRP delivers highly diversified allocations with low volatility, low portfolio turnover and competitive performance metrics.
    Keywords: Risk Parity, Diversification, Portfolio Optimisation, Clustering
    JEL: G11
    Date: 2019
  10. By: Fabien Perez (ENSAE - Ecole Nationale de la Statistique et de l'Analyse Economique - Ecole Nationale de la Statistique et de l'Analyse Economique); Guillaume Hollard (CES - Centre d'économie de la Sorbonne - CNRS - Centre National de la Recherche Scientifique - UP1 - Université Panthéon-Sorbonne); Radu Vranceanu (THEMA - Théorie économique, modélisation et applications - UCP - Université de Cergy Pontoise - Université Paris-Seine - CNRS - Centre National de la Recherche Scientifique); Delphine Dubart (ESSEC Business School - Essec Business School)
    Abstract: This paper uses the test/retest data from the Holt and Laury (2002) experiment to provide estimates of the measurement error in this popular risk-aversion task. Maximum likelihood estimation suggests that the variance of the measurement error is approximately equal to the variance of the number of safe choices. Simulations confirm that the coefficient on the risk measure in univariate OLS regressions is approximately half of its true value. Unlike measurement error, the discrete transformation of continuous riskaversion is not a major issue. We discuss the merits of a number of different solutions: increasing the number of observations, IV and the ORIV method developed by Gillen et al. (2019).
    Keywords: ORIV,Experiments,Measurement error,Risk-aversion,Test/retest
    Date: 2019–09–17
  11. By: Guofeng Sun
    Abstract: Banks' shadow, or money creation by banks beyond traditional loans, plays an important role in China's money-creation process, posing a number of challenges to monetary policy operations and financial risk management. This paper analyzes the money-creation mechanisms of China's shadow banking sector in detail, provides accurate measurements, investigates its effects on financial risk, and surveys recent regulation. To strengthen supervision, China's regulators should closely track the evolution of various shadow banking channels, both on- and off-balance sheet. Specific macroprudential regulation tools, such as asset reserves and risk reserves, should be applied separately to banks' shadow and traditional shadow banking.
    Keywords: banks' shadow, traditional shadow banking, credit money creation, bank accounting, regulation
    JEL: E44 E51 G28
    Date: 2019–11
  12. By: Lojak, Benjamin; Makarewicz, Tomasz; Proaño Acosta, Christian
    Abstract: We investigate the relationship between monetary policy and banks' risk-taking behavior. We study a general equilibrium model in which a risk averse bank credits firms and also manages a portfolio consisting of a risky and a risk-free asset. When a bank signs up credit contracts with firms, it takes into account their solvency and potential gains from outside investment strategies. We show that the bank's asset/liability and risk management depend on the prevailing policy rate. However, low policy rates incentivizes a bank to search-for-yield by re-allocating their asset portfolios towards more risky exposures ultimately leads to under-capitalized positions. This renders the financial sector more vulnerable.
    Date: 2019
  13. By: Xiao, Tim
    Abstract: This paper attempts to assess the economic significance and implications of collateralization in different financial markets, which is essentially a matter of theoretical justification and empirical verification. We present a comprehensive theoretical framework that allows for collateralization adhering to bankruptcy laws. As such, the model can back out differences in asset prices due to collateralized counterparty risk. This framework is very useful for pricing outstanding defaultable financial contracts. By using a unique data set, we are able to achieve a clean decomposition of prices into their credit risk factors. We find empirical evidence that counterparty risk is not overly important in credit-related spreads. Only the joint effects of collateralization and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of financial contracts. We also analyze the difference between cleared and OTC markets.
    Date: 2018–06–18
  14. By: Christa Cuchiero; Sara Svaluto-Ferro
    Abstract: We introduce polynomial processes taking values in an arbitrary Banach space $B$ via their infinitesimal generator $L$ and the associated martingale problem. We obtain two representations of the (conditional) moments in terms of solutions of a system of ODEs on the truncated tensor algebra of dual respectively bidual spaces. We illustrate how the well-known moment formulas for finite dimensional or probability-measure valued polynomial processes can be deduced in this general framework. As an application we consider polynomial forward variance curve models which appear in particular as Markovian lifts of (rough) Bergomi-type volatility models. Moreover, we show that the signature process of a $d$-dimensional Brownian motion is polynomial and derive its expected value via the polynomial approach.
    Date: 2019–11
  15. By: Maddalena Galardo (Bank of Italy); Maurizio Lozzi (Bank of Italy); Paolo Emilio Mistrulli (Bank of Italy)
    Abstract: Despite social capital being widely acknowledged as a key factor in the functioning of financial markets, the evidence on the channels through which it operates is still scant. In this paper we isolate one possible channel and investigate whether social capital plays a role in mitigating the impact of uncertainty shocks on bank credit supply. We exploit both the huge rise in the level of uncertainty that followed the Lehman Brothers default and a very granular and rich loan-level dataset from the Italian Credit register that allows us to clearly disentangle demand and supply factors. We find that social capital makes credit markets more resilient to uncertainty shocks, especially when informational asymmetries between banks and borrowers are more severe.
    Keywords: credit supply, uncertainty, social capital, trust, loan applications
    JEL: A13 G01 G2
    Date: 2019–11
  16. By: Saman Adhami (Vienna Graduate School of Finance;; Dominique Guégan (Université Paris1 Panthéon-Sorbonne, Centre d'Economie de la Sorbonne, - Ca' Foscari University of Venezia, University of Economics Ho Chi Minh City, Vietnam)
    Abstract: This paper re-examines the discussion on blockchain technology, crypto assets and ICOs providing also evidence that in crypto markets there are currently two classes of assets, namely standalone cryptocurrencies (or 'coins') and tokens which result from an ICO and are intrisically linked to the performance of the issuing company or venture. While the former have been arguments of various empirical studies regarding their price dynamics and their effect on the variance of a well-diversified portfolio, no such study has been done to analyze listed tokens, which in our sample are over 700 and with a backing of about §17.3Bn from their respective ICOs. Therefore, investors interested in optimizing their portfolios should first assess the diversifier, hedge or safe haven role of tokens vis-à-vis traditional assets, on top of 'coins' in order to sensibly use this new asset class. After constructing various indices to represent both the token asset class as a whole and its sub-classes, we model dynamic conditional correlations among all the assets in our sample to obtain time-varying correlations for each token-asset pair. We find that tokens are effective diversifiers but not a hedge or a safe haven asset. We evidence that tokens retain important systematic differences with the two other asset classes to which they are most generally compared to, namely 'coins' and equities
    Keywords: Cryptocurrency; DCC-MGARCH; Hedge; Initial Coin Offering; Safe Haven
    JEL: G11 G15
    Date: 2019–09
  17. By: Gonçalves, Jorge; Kräussl, Roman; Levin, Vladimir
    Abstract: Is it true that speed bumps level the playing field, make financial markets more stable and reduce negative externalities of high-frequency trading (HFT) firms? We examine how the implementation of a particular speed bump - Midpoint Extended Life order (M-ELO) on Nasdaq impacted financial markets stability in terms of occurrences of mini-flash crashes in individual securities. We use high-frequency order book message data around the implementation date and apply difference-in-differences analysis to estimate the average treatment effect of the speed bump on market stability and liquidity provision. The results suggest that the introduction of the M-ELO decreases the average number of crashes on Nasdaq compared to other exchanges by 4.7%. Liquidity provision by HFT firms also improves. These findings imply that technology-based solutions by exchanges are feasible alternatives to regulatory intervention towards safer markets.
    Keywords: mini-flash crash,speed bump,midpoint extended life order
    JEL: C21 G14 G18
    Date: 2019
  18. By: Emmanuelle Jay (Fidéas Capital, Quanted & Europlace Institute of Finance); Thibault Soler (Fidéas Capital et Centre d'Economie de la Sorbonne); Eugénie Terreaux (DEMR, ONERA - Université Paris-Saclay); Jean-Philippe Ovarlez (DEMR, ONERA - Université Paris-Saclay); Frédéric Pascal (L2S, Centrale Supélec - Université Paris-Saclay); Philippe De Peretti (Centre d'Economie de la Sorbonne - Université Paris 1Panthéon-Sorbonne;; Christophe Chorro (Centre d'Economie de la Sorbonne - Université Paris 1 Panthéon-Sorbonne;
    Abstract: This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimization problem. The particular case of the Maximum Variety Portfolio is treated but the same improvements apply also in the other optimization problems such as the Minimum Variance Portfolio. We assume that the most important information (or the latent factors) are embedded in correlated Elliptical Symmetric noise extending classical Gaussian assumptions. We propose here to focus on a recent method of model order selection allowing to efficiently estimate the subspace of main factors describing the market. This non-standard model order selection problem is solved through Random Matrix Theory and robust covariance matrix estimation. Morepver we extend the method to non-homogeneous assets returns. The proposed procedure will be explained through synthetic data and be applied and compared with standard techniques on real market data showing promising improvements
    Keywords: Robust Covariance Matrix Estimation; Model Order Selection; Random Matrix Theory; Portfolio Optimization; Financial Time Series; Multi-Factor Model; Elliptical Symmetric Noise; Maximum Variety Portfolio
    JEL: C5 G11
    Date: 2019–10
  19. By: International Monetary Fund
    Abstract: At the request of the Bank of Lao, and in continuation of the FIRST TA project, this TA report provides advice towards implementing risk-based supervision (RBS). The BoL is in the process of implementing its risk-based approach to supervision to make the banking system more stable and sounder. This mission looked at the full cycle of onsite and offsite supervision process and provided advice related to applying the RBS manual, drafting and utilizing Institution Profile (IP) and Risk Assessment Summary (RAS), writing an effective examination report, the use of a risk-based approach to internal systemic reporting, and developing a supervisory response framework. The bulk of the mission time was spent on formal hands-on training sessions. During the mission, training was provided to BSD staff in small group discussions, and a more formal seminar was organized for all offsite and onsite BSD staff, which focused on reviewing the underlying RBS concepts and the elements of RBS manuals.
    Date: 2019–10–30
  20. By: Juan M. Londono; Sai Ma; Beth Anne Wilson
    Abstract: In this note, we construct a measure of real economic uncertainty (REU)--based on the predictability of near-term economic performance--for the major advanced economies.
    Date: 2019–10–08
  21. By: Eguren-Martin, Fernando (Bank of England); Sokol, Andrej (European Central Bank, Bank of England and CfM)
    Abstract: We document how the entire distribution of exchange rate returns responds to changes in global financial conditions. We measure global financial conditions as the common component of country-specific financial condition indices, computed consistently across a large panel of developed and emerging economies. Based on quantile regression results, we provide a characterisation and ranking of the tail behaviour of a large sample of currencies in response to a tightening of global financial conditions, corroborating some of the prevailing narratives about safe haven and risky currencies. We then carry out a portfolio sorting exercise to identify the macroeconomic fundamentals associated with such different tail behaviour, and find that currency portfolios sorted on the basis of relative interest rates, current account balances and levels of international reserves display a higher likelihood of large losses in response to a tightening of global financial conditions.
    Keywords: Exchange rates; tail risks; financial conditions indices; global financial cycle; quantile regression
    JEL: F31 G15
    Date: 2019–09–16
  22. By: Timothy S. Hills; Taisuke Nakata; Sebastian Schmidt
    Abstract: Even when the policy rate is currently not constrained by its effective lower bound (ELB), the possibility that the policy rate will become constrained in the future lowers today's inflation by creating tail risk in future inflation and thus reducing expected inflation. In an empirically rich model calibrated to match key features of the U.S. economy, we find that the tail risk induced by the ELB causes inflation to undershoot the target rate of 2 percent by as much as 50 basis points at the economy's risky steady state. Our model suggests that achieving the inflation target may be more difficult now than before the Great Recession, if the likely decline in long-run neutral rates has led households and firms to revise up their estimate of the frequency of future ELB events.
    Keywords: Deflationary Bias ; Disinflation ; Effective Lower Bound ; Inflation Targeting ; Risky Steady State ; Tail Risk
    JEL: E32 E52
    Date: 2019–11–04

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