nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒08‒09
27 papers chosen by
Stan Miles
Thompson Rivers University

  1. Volatility of S&P500: Estimation and Evaluation By Wen Su
  2. Automatic Fatou Property of Law-invariant Risk Measures By Shengzhong Chen; Niushan Gao; Denny Leung; Lei Li
  3. Impact of EU-wide Insurance Stress Tests on Equity Prices and Systemic Risk By Petr Jakubik; Saida Teleu
  4. How Do Investors Prefer Banks to Transit to Basel Internal Models: Mandatorily or Voluntarily? By Henry Penikas; Anastasia Skarednova; Mikhail Surkov
  5. Disastrous Defaults By Gouriéroux, Christian; Monfort, Alain; Mouabbi, Sarah; Renne, Jean-Paul
  6. Default Distances Based on the KMV-CEV Model By Wen Su
  7. On the Dynamics of International Real Estate Investment Trust Propagation Mechanisms: Evidence from Time-Varying Return and Volatility Connectedness Measures By Keagile Lesame; Elie Bouri; David Gabauer; Rangan Gupta
  8. Stressed but not Helpless: Strategic Behaviour of Banks Under Adverse Market Conditions By Grzegorz Halaj; Sofia Priazhkina
  9. A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection By Wei Li; Florentina Paraschiv; Georgios Sermpinis
  10. Firm-bank linkages and optimal policies in a lockdown By Anatoli Segura; Alonso Villacorta
  11. Machine Learning and Factor-Based Portfolio Optimization By Thomas Conlon; John Cotter; Iason Kynigakis
  12. A tail of three occasionally-binding constraints: a modelling approach to GDP-at-Risk By Aikman, David; Bluwstein, Kristina; Karmakar, Sudipto
  13. Analysis of the Dynamic Relationship between Liquidityproxies and returns on French CAC 40 index By Ayad Assoil; Ndéné Ka; Jules Sadefo Kamdem
  14. A New Attempt to Identify Long-term Precursors for Financial Crisis in the Market Correlation Structures By Anton J. Heckens; Thomas Guhr
  15. Corporate loans, banks’ internal risk estimates and central bank collateral: evidence from the euro area By Calza, Alessandro; Hey, Julius-Benjamin; Parrini, Alessandro; Sauer, Stephan
  16. Sharing Asymmetric Tail Risk Smoothing, Asset Pricing and Terms of Trade By Corsetti, G.; Lipińska, A.; Lombardo, G.
  17. The impact of machine learning and big data on credit markets By Eccles, Peter; Grout, Paul; Siciliani, Paolo; Zalewska, Anna
  18. Social Risk Effects: The 'Experience of Social Risk' Factor By Diekert, Florian; Goeschl, Timo; König-Kersting, Christian
  19. Economic Value of Modeling the Joint Distribution of Returns and Volatility: Leverage Timing By Cem Cakmakli; Verda Ozturk
  20. Risk perception and policy responses to Covid-19: New Evidence By Mafalda Venâncio de Vasconcelos; Filipa Leão de Vasconcelos
  21. Pricing Art and the Art of Pricing : On Returns and Risk in Art Auction Markets By Li, Yuexin; Ma, X.; Renneboog, Luc
  22. Oil and US stock market shocks: implications for Canadian equities By Reinhold Heinlein; Scott M. R. Mahadeo
  23. Financial intermediation and risk in decentralized lending protocols By Carlos Castro-Iragorri; Julian Ramirez; Sebastian Velez
  24. Decoupling Shrinkage and Selection for the Bayesian Quantile Regression By David Kohns; Tibor Szendrei
  25. Heavy Tailed, but not Zipf: Firm and Establishment Size in the U.S. By Illenin O. Kondo; Logan T. Lewis; Andrea Stella
  26. Spillovers among Energy Commodities and the Russian Stock Market By Costola, Michele; Lorusso, Marco
  27. The growth-at-risk perspective on the system-wide impact of Basel III finalisation in the euro area By Budnik, Katarzyna; Dimitrov, Ivan; Giglio, Carla; Groß, Johannes; Lampe, Max; Sarychev, Andrei; Tarbé, Matthieu; Vagliano, Gianluca; Volk, Matjaz

  1. By: Wen Su
    Abstract: In an era when derivatives is getting popular, risk management has gradually become the core content of modern finance. In order to study how to accurately estimate the volatility of the S&P 500 index, after introducing the theoretical background of several methods, this paper uses the historical volatility method, GARCH model method and implied volatility method to estimate the real volatility respectively. At the same time, two ways of adjusting the estimation window, rolling and increasing, are also considered. The unbiased test and goodness of fit test are used to evaluate these methods. The empirical result shows that the implied volatility is the best estimator of the real volatility. The rolling estimation window is recommended when using the historical volatility. On the contrary, the estimation window is supposed to be increased when using the GARCH model.
    Date: 2021–07
  2. By: Shengzhong Chen; Niushan Gao; Denny Leung; Lei Li
    Abstract: In this paper, we show that, on classical model spaces including Orlicz spaces, every real-valued, law-invariant, coherent risk measure automatically has the Fatou property at every point whose negative part has a thin tail.
    Date: 2021–07
  3. By: Petr Jakubik (European Insurance and Occupational Pensions Authority (EIOPA), Germany; Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies, Czech Republic); Saida Teleu (Maltese Financial Services Authority, Malta; Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies, Czech Republic)
    Abstract: Since the global financial crisis in 2007, stress tests have become standard tools for regulators and supervisors to assess the risks and vulnerabilities of financial sectors. To this end, the Insurance and Occupational Pensions Authority (EIOPA) regularly performs EU-wide insurance stress tests. This paper analyses the impact of the conducted exercises in 2014, 2016 and 2018 on the equity prices of insurance companies. Using an event study framework, we find a statistically significant impact only for the publication of the 2018 exercise results. Our empirical analysis further suggests that the final version of technical specifications for the 2014 exercise, the initiation of public consultation, and the published stress test scenario of the 2018 exercise contributed to the decline in systemic risk. To our best knowledge, this is the first paper that investigates this topic for the European insurance sector. Our empirical results could help improve the communication and design of future stress test exercises.
    Keywords: European insurance sector; EU-wide insurance stress test, systemic risk, event study, equity prices
    JEL: G23 G12 G14 G18
    Date: 2021–07
  4. By: Henry Penikas (Bank of Russia, Russian Federation); Anastasia Skarednova (Alfa-Bank, Russian Federation); Mikhail Surkov (Bank of Russia, Russian Federation)
    Abstract: The recently finalized Basel Framework continues allowing banks to use internal data and models to define risk estimates and use them for the capital adequacy ratio computation. World-wide there are above two thousand banks running the Basel internal models. However, there are countries that have none of such banks. For them there exists a dilemma. Namely, which transition path to adopt out of the two. The voluntarily one as in the EU or the mandatory one as in the US. Our objective is to take the investor perspective and benchmark those two modes. Thus, we wish to find whether there is a premium for any of them, or perhaps that they are equivalent. The novelty of our research is the robust estimate that investors prefer mandatory transition style to the voluntarily one. Such a preference is reflected in the rise of the mean return and decline in stock volatility for the transited banks in the US and right the opposite consequences in the EU. However, we should be cautious in interpreting our findings. Such a preference may not only be the premium for the breakage of the vicious cycle and the ultimate improvement in the banks’ risk-management systems and the overall financial stability. It may also hold true if and only if the mandatory transition for particular institutions is accompanied by a restriction for other banks in the region to transit. Our findings are of value primarily to the emerging economies like Argentine and Indonesia.
    Keywords: Basel II, Basel III, BCBS, CAR, difference-in-difference, D-SIB, G-SIB, IRB, risk-weight.
    JEL: C21 G12 G17 G18 G21
    Date: 2021–07
  5. By: Gouriéroux, Christian; Monfort, Alain; Mouabbi, Sarah; Renne, Jean-Paul
    Abstract: We define a disastrous default as the default of a systemic entity, which has a negative effect on the economy and is contagious. Bringing macroeconomic structure to a no-arbitrage asset pricing framework, we exploit prices of disaster-exposed assets (credit and equity derivatives) to extract information on the expected (i) influence of a disastrous default on consumption and (ii) probability of a financial meltdown. Using European data, we find that the returns of disaster-exposed assets are consistent with a systemic default being followed by a 2% decrease in consumption. The recessionary influence of disastrous defaults implies that financial instruments whose payoffs are exposed to such credit events carry substantial risk premiums. We also produce systemic risk indicators based on the probability of observing a certain number of systemic defaults or a sharp drop of consumption.
    JEL: E43 E44 E47 G01 G12
    Date: 2021–08–02
  6. By: Wen Su
    Abstract: This paper presents a new method to assess default risk based on applying non constant volatility to the KMV model, taking the CEV model as an instance. We find the evidence that the classical KMV model could not distinguish ST companies in China stock market. Aiming at improve the accuracy of the KMV model, we assume the firm's asset value dynamics are given by the CEV process $\frac{dV_A}{V_A} = \mu_A dt + \delta V_A^{\beta-1}dB$ and use fixed effects model and equivalent volatility method to estimate parameters. The estimation results show the $\beta>1$ for non ST companies while $\beta
    Date: 2021–07
  7. By: Keagile Lesame (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon); David Gabauer (Data Analysis Systems, Software Competence Center Hagenberg, Hagenberg, Austria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: In this paper we investigate the time-varying interconnectedness of international REIT markets using daily REIT prices in eleven major REIT countries since the Global Financial Crisis. We construct dynamic total, net total and net pairwise return and volatility connectedness measures to better understand systemic risk and the transmission of shocks across REIT markets. Our findings show that REIT market interdependence is dynamic and increases signicantly during times of heightened uncertainty including the COVID-19 pandemic. We also find that the US REIT market alongside with major European REITs are generally sources of shocks to Asian-Pacific REIT markets. Furthermore, US REITs appear to dominate European REITs. US and to a lesser extent European REITs are generally affected from cross market shocks. These findings highlight that portfolio diversification opportunities decline during times of market uncertainty.
    Keywords: REITs, TVP-VAR, Dynamic connectedness
    JEL: C32 C50 G10
    Date: 2021–07
  8. By: Grzegorz Halaj; Sofia Priazhkina
    Abstract: We model bank management actions in severe stress test conditions using a game-theoretical framework. Banks update their balance sheets to strategically maximize risk-adjusted returns to shareholders given three regulatory constraints and feedback effects related to fire sales, interactions of loan supply and demand, and deteriorating funding conditions. The framework allows us to study the role of strategic behaviors in amplifying or mitigating adverse macrofinancial shocks in a banking system and the role of macroprudential policies in the mitigation of systemic risk. In a macro-consistent stress testing application, we show that a trade-off can arise between banking stability (solvency) and macroeconomic stability (lending) and test whether the release of a countercyclical capital buffer can reduce systemic risk.
    Keywords: Central bank research; Economic models; Financial institutions; Financial stability; Financial system regulation and policies
    JEL: C72 G21
    Date: 2021–07
  9. By: Wei Li; Florentina Paraschiv; Georgios Sermpinis
    Abstract: The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.
    Date: 2021–07
  10. By: Anatoli Segura (Banca d’Italia); Alonso Villacorta (University of California Santa Cruz)
    Abstract: We develop a novel framework featuring loss amplification through firm-bank linkages. We use it to study optimal intervention in a lockdown situation that creates cash shortfalls for firms, which must resort to bank lending. Firms’ increased debt reduces their output due to moral hazard. Banks need safe collateral to raise funds. Without intervention, aggregate risk constrains bank lending, amplifying output losses. Optimal government support provides sufficient aggregate risk insurance, and is implemented through transfers to firms and fairly-priced guarantees on banks’ debt. When aggregate risk is not too large, such guarantees can be financed through a procyclical taxation of firms’ profits.
    Keywords: Covid-19, cash shortfall, firms' debt, moral hazard, bank equity, aggregate risk, government interventions.
    JEL: G01 G20 G28
    Date: 2021–07
  11. By: Thomas Conlon (Smurfit Graduate Business School, University College Dublin); John Cotter (Smurfit Graduate Business School, University College Dublin); Iason Kynigakis (Smurfit Graduate Business School, University College Dublin)
    Abstract: We examine machine learning and factor-based portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristic-sorted portfolios than popular dimensionality reduction techniques. Machine learning methods also lead to covariance and portfolio weight structures that diverge from simpler estimators. Minimum-variance portfolios using latent factors derived from autoencoders and sparse methods outperform simpler benchmarks in terms of risk minimization. These effects are amplified for investors with an increased sensitivity to risk-adjusted returns, during high volatility periods or when accounting for tail risk. Covariance matrices with a time-varying error component improve portfolio performance at a cost of higher turnover.
    Keywords: Autoencoder, Covariance matrix, Dimensionality reduction, Factor models, Machine learning, Minimum-variance, Principal component analysis, Partial least squares, Portfolio optimization, Sparse principal component analysis, Sparse partial least squares
    JEL: C38 C4 C45 C5 C58 G1 G11
    Date: 2021–03–11
  12. By: Aikman, David (King's College London); Bluwstein, Kristina (Bank of England); Karmakar, Sudipto (Bank of England)
    Abstract: We build a semi-structural New Keynesian model with financial frictions to study the drivers of macroeconomic tail risk (‘GDP-at-Risk’). We analyse the empirically observed fat left tail of the GDP distribution by modelling three key non-linearities emphasised in the literature: 1) an effective lower bound on nominal interest rates, 2) a credit crunch in bank credit supply when bank capital depletes, and 3) deleveraging by borrowers when debt service burdens become excessive. We obtain three key results. First, our model generates a significantly fat-tailed distribution of GDP – a finding that is absent in most linear New Keynesian and RBC models. Second, we show how these constraints interact with each other. We find that an economy prone to debt deleveraging will experience significantly more credit crunch and effective lower bound episodes than otherwise. Moreover, as the effective lower bound becomes more proximate, the frequency of credit crunch episodes increases significantly. As a rule of thumb, we find that each 50 basis point decline in monetary policy headroom requires additional capital buffers of 1% of assets or 2%–2.5% points lower debt service burdens to hold the risk level constant. Third, we use the model to generate a historical decomposition of GDP-at-Risk for the United Kingdom. The implied risk outlook deteriorates significantly in the run-up to the Global Financial Crisis, driven by depleted capital buffers and increasing debt burdens. Since then, GDP-at-Risk has remained elevated, with greater bank resilience and lower debt offset by the limited capacity of monetary policy to cushion adverse shocks.
    Keywords: Financial crises; bank capital; debt deleveraging; macroprudential policy; effective lower bound; GDP-at-Risk
    JEL: G01 G28
    Date: 2021–07–26
  13. By: Ayad Assoil (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Ndéné Ka (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier)
    Abstract: The aim of this paper is to analyze the dynamic evolution of six liquidity proxies ontime, and to find their causality with the French CAC 40 stock market index returns, overthe period from January 2007 to December 2018. For that, we use a vector autoregressiveapproach and the impulse response function, to do causality test between the CAC 40index returns and six differents liquidity proxies. Empirical results suggest a significantshort-term relationship between the returns and the liquidity. As for Granger's causalitytest, the results reveal that there is unidirectional causality running from equity returnsto liquidity.
    Keywords: Impulse response function,Granger causality,Liquidity risk,Market risk,VAR model,CAC 40 Market.
    Date: 2021–07–09
  14. By: Anton J. Heckens; Thomas Guhr
    Abstract: Prediction of events in financial markets is every investor's dream and, usually, wishful thinking. From a more general, economic and societal viewpoint, the identification of indicators for large events is highly desirable to assess systemic risks. Unfortunately, the very nature of financial markets, particularly the predominantly non-Markovian character as well as non-stationarity, make this challenge a formidable one, leaving little hope for fully fledged answers. Nevertheless, it is called for to collect pieces of evidence in a variety of observables to be assembled like the pieces of a puzzle that eventually might help to catch a glimpse of long-term indicators or precursors for large events - if at all in a statistical sense. Here, we present a new piece for this puzzle. We use the quasi-stationary market states which exist in the time evolution of the correlation structure in financial markets. Recently, we identified such market states relative to the collective motion of the market as a whole. We study their precursor properties in the US stock markets over 16 years, including two crises, the dot-com bubble burst and the pre-phase of the Lehman Brothers crash. We identify certain interesting features and critically discuss their suitability as indicators.
    Date: 2021–07
  15. By: Calza, Alessandro; Hey, Julius-Benjamin; Parrini, Alessandro; Sauer, Stephan
    Abstract: We use a unique dataset of ratings for euro area corporate loans from commercial banks’ internal rating-based (IRBs) systems and central banks’ in-house credit assessment systems (ICASs) to investigate whether banks’ IRB ratings underestimate the credit risk of their corporate loan portfolios when the latter are used as collateral in the Eurosystem’s monetary policy operations. We are able to identify systematic risk underestimation by comparing the IRB ratings with those produced for the same borrowers by the ICASs. Our results show that while they are on average more conservative than ICASs for the entire population of rated corporate loans, IRBs are significantly less conservative than ICASs for those loans that are actually used as Eurosystem collateral, particularly for large loans. The less conservative estimates of risk by IRBs relative to ICASs can be partly explained by banks’ liquidity constraints, but not by their degree of capitalisation. Overall, our findings suggest the existence of a collateral-related channel through which the use of IRB ratings may influence the internal estimation of risk by banks. JEL Classification: G21, G28
    Keywords: banking regulation, central bank liquidity, internal ratings, probability of default
    Date: 2021–07
  16. By: Corsetti, G.; Lipińska, A.; Lombardo, G.
    Abstract: With the Global Financial Crisis, the COVID-19 pandemic, and the looming Climate Change, investors and policymakers around the world are bracing for a new global environment with heightened tail risk. Asymmetric exposure to this risk across countries raises the private and social value of arrangements improving insurance. We offer an analytical decomposition of the welfare effects of efficient capital market integration into a "smoothing" and a "level effect". Enhancing risk sharing affects the volatility of consumption, but also brings about equilibrium adjustment in asset and goods prices. This in turn drives relative wealth and consumption, as well as labor and capital allocation, across borders. Using model simulation, we explore quantitatively the empirical relevance of the different channels through which riskier and safer countries benefit from sharing macroeconomic risk. We offer an algorithm for the correct solution of the equilibrium using DSGE models under complete markets, at higher order of approximation.
    Keywords: International Risk Sharing, Asymmetry, Fat Tails, Welfare, Transfer Problem
    JEL: F15 F41 G15
    Date: 2021–07–26
  17. By: Eccles, Peter (Bank of England); Grout, Paul (Bank of England); Siciliani, Paolo (Bank of England); Zalewska, Anna (University of Bath)
    Abstract: There is evidence that machine learning (ML) can improve the screening of risky borrowers, but the empirical literature gives diverse answers as to the impact of ML on credit markets. We provide a model in which traditional banks compete with fintech (innovative) banks that screen borrowers using ML technology and show that the impact of the adoption of the ML technology on credit markets depends on the characteristics of the market (eg borrower mix, cost of innovation, the intensity of competition, precision of the innovative technology, etc.). We provide a series of scenarios. For example, we show that if implementing ML technology is relatively expensive and lower-risk borrowers are a significant proportion of all risky borrowers, then all risky borrowers will be worse off following the introduction of ML, even when the lower-risk borrowers can be separated perfectly from others. At the other extreme, we show that if costs of implementing ML are low and there are few lower-risk borrowers, then lower-risk borrowers gain from the introduction of ML, at the expense of higher-risk and safe borrowers. Implications for policy, including the potential for tension between micro and macroprudential policies, are explored.
    Keywords: Adverse selection; banking; big data; capital requirements; credit markets; fintech; machine learning; prudential regulation
    JEL: G21 G28 G32
    Date: 2021–07–09
  18. By: Diekert, Florian; Goeschl, Timo; König-Kersting, Christian
    Abstract: Anticipating "social risk", or risk caused by humans, affects decision-making differently from anticipating natural risk. Drawing upon a large sample of the US population (n=3,982), we show that the phenomenon generalizes to risk experience. Experiencing adverse outcomes caused by another human reduces future risk-taking, but experiencing the same outcome caused by nature does not. While puzzling from a consequentialist perspective, the Experience of Social Risk Factor that we identify deepens our understanding of decision-making in settings in which outcomes are co-determined by different sources of uncertainty. Our findings imply that a unifying theory of social risk effects requires new explanations.
    Keywords: social risk; risk experience; decision-making under risk; behavioral economics; experiment
    Date: 2021–08–02
  19. By: Cem Cakmakli (Koc University); Verda Ozturk (Duke University)
    Abstract: We propose a joint modeling strategy for timing the joint distribution of the returns and their volatility. We do this by incorporating the potentially asymmetric links into the system of ‘independent’ predictive regressions of returns and volatility, allowing for asymmetric cross-correlations, denoted as instantaneous leverage effects, in addition to cross-autocorrelations between returns and volatility, denoted as intertemporal leverage effects. We show that while the conventional intertemporal leverage effects bear little economic value, our results point to the sizeable value of exploiting the contemporaneous asymmetric link between returns and volatility. Specifically, a mean-variance investor would be willing to pay several hundred basis points to switch from the strategies based on conventional predictive regressions of mean and volatility in isolation of each other to the joint models of returns and its volatility, taking the link between these two moments into account. Moreover, our findings are robust to various effects documented in the literature.
    Keywords: Economic value, system of equations, leverage timing, market timing, volatility timing.
    JEL: C30 C52 C53 C58 G11
    Date: 2021–07
  20. By: Mafalda Venâncio de Vasconcelos; Filipa Leão de Vasconcelos
    Abstract: Governments around the world have been taking unprecedent responses in order to slow down the spread of Covid-19, a highly infectious disease caused by the new coronavirus SARS-CoV-2. Although containment measures imposed by governments may help to contain the spread of the virus they had led to large economic and social costs. In this study we link psychological vulnerabilities to economics in an attempt to analyze the impact of government containment measures on citizens' risk perception of death directly caused by Covid-19. In the context of pandemic, it is crucial to understand if restrictions imposed by governments impact people's risk perception. If people perceive higher risk, they will be more prone to follow health authorities' recommendations and there are higher chances that the pandemic will be brought under control. Our study presents evidence that during the first wave of Covid-19, stringent containment measures imposed by governments increase citizens' risk perception. We also find that economic activity is also an important driver of risk perception, namely, higher economic activity decreases people's risk perception of death directly caused by Covid-19.
    Keywords: Containment measures; Covid-19; Economic activity; Google search; Media; Stringency index
    Date: 2021
  21. By: Li, Yuexin (Tilburg University, School of Economics and Management); Ma, X. (Tilburg University, School of Economics and Management); Renneboog, Luc (Tilburg University, School of Economics and Management)
    Date: 2021
  22. By: Reinhold Heinlein (University of the West of England); Scott M. R. Mahadeo (University of Portsmouth)
    Abstract: Oil and US stock market shocks are expected to be relevant for Canadian equities, as Canada is an oil-exporter adjacent to the US. We evaluate how the relationship between Canadian stock market indices and such external shocks change under extraordinary events. To do this, we subject statistically identified oil and S&P 500 market shocks to a surprise filter, which detects shocks with the greatest magnitude occurring over a given lookback period; and an outlier filter, which detects extrema shocks that exceed a normal range. Then, we examine how the dependence structure between shocks and Canadian equities change under the extreme surprise and outlier episodes through various co-moment spillover tests. Our results show that co-moments beyond correlation are important in reflecting the changes occurring in the relationships between external shocks and Canadian equities in extreme events. Additionally, the differences in findings under extreme positive and negative shocks provide evidence for asymmetric spillover effects from the oil and US stock markets to Canadian equities. Moreover, the observed heterogeneity in the relationships between disaggregated Canadian equities and shocks in the crude oil and S&P 500 markets are useful to policymakers for revealing sector-specific vulnerabilities, and provide portfolio diversification opportunities for investors to exploit.
    Keywords: Canada; oil market; spillover; stock market
    JEL: C32 G15 Q43
    Date: 2021–07–10
  23. By: Carlos Castro-Iragorri; Julian Ramirez; Sebastian Velez
    Abstract: We provide an overview of decentralized protocols like Compound and Aave that offer collateralized loans for cryptoasset investors. Compound and Aave are two of the most important application in the decentralized finance (DeFi) ecosystem. Using publicly available information on rates, supply and borrow activity, and accounts we analyze different elements of the protocols. In particular, we estimate ex-post margins that give a comprehensive account of the cost of financial intermediation. We find that ex-post margins considering all markets are 1% and lower for stablecoin markets. In addition, we estimate quarterly indicators regarding solvency, asset quality, earnings and market risk similar to the ones used in traditional banking. This provides a first look at the use of these metrics and a comparison between the similarities and challenges to our understanding of financial intermediation in these protocols based on tools used for traditional banking.
    Date: 2021–07
  24. By: David Kohns; Tibor Szendrei
    Abstract: This paper extends the idea of decoupling shrinkage and sparsity for continuous priors to Bayesian Quantile Regression (BQR). The procedure follows two steps: In the first step, we shrink the quantile regression posterior through state of the art continuous priors and in the second step, we sparsify the posterior through an efficient variant of the adaptive lasso, the signal adaptive variable selection (SAVS) algorithm. We propose a new variant of the SAVS which automates the choice of penalisation through quantile specific loss-functions that are valid in high dimensions. We show in large scale simulations that our selection procedure decreases bias irrespective of the true underlying degree of sparsity in the data, compared to the un-sparsified regression posterior. We apply our two-step approach to a high dimensional growth-at-risk (GaR) exercise. The prediction accuracy of the un-sparsified posterior is retained while yielding interpretable quantile specific variable selection results. Our procedure can be used to communicate to policymakers which variables drive downside risk to the macro economy.
    Date: 2021–07
  25. By: Illenin O. Kondo; Logan T. Lewis; Andrea Stella
    Abstract: Heavy tails play an important role in modern macroeconomics and international economics. Previous work often assumes a Pareto distribution for firm size, typically with a shape parameter approaching Zipf’s law. This convenient approximation has dramatic consequences for the importance of large firms in the economy. But we show that a lognormal distribution, or better yet, a convolution of a lognormal and a non-Zipf Pareto distribution, provides a better description of the U.S. economy, using confidential Census Bureau data. These findings hold even far in the upper tail and suggest heterogeneous firm models should more systematically explore deviations from Zipf’s law.
    Keywords: Firm size distribution, TFP distribution, Lognormal, Pareto, Zipf’s law, Granularity
    JEL: L11 E24
    Date: 2021–07
  26. By: Costola, Michele; Lorusso, Marco
    Abstract: We examine the connectedness in the energy commodities sector and the Russian stock market over the period 2005-2020 using the variance decomposition approach. Our analysis identifies the booms and busts in the correspondence of political and war episodes that are related to spillover effects in the Russian economy, as well as the energy commodities markets. Our findings show that the Russian Oil & Gas and Metals & Mining sectors are net shock contributors of crude oil and have the highest spillovers to other Russian sectors. Furthermore, we disentangle the sources of spillovers that originated from the financial and energy commodity markets and find that a positive change in the energy commodity volatility spillover is associated with an increase in Russian geopolitical uncertainty. Finally, we show that the spread of COVID-19 increases the stock market volatility spillover, whereas it lowers the energy commodity volatility spillover.
    Keywords: Spillover Effects, Russian Stock Market, Russian Sectoral Indices, Commodity Markets, International Financial Markets
    JEL: C3 C58 E44 G1
    Date: 2021–07–31
  27. By: Budnik, Katarzyna; Dimitrov, Ivan; Giglio, Carla; Groß, Johannes; Lampe, Max; Sarychev, Andrei; Tarbé, Matthieu; Vagliano, Gianluca; Volk, Matjaz
    Abstract: This paper assesses the macroeconomic implications of the Basel III finalisation for the euro area, employing a large-scale semi-structural model encompassing over 90 banks and 19-euro area economies. The new regulatory framework will influence banks’ reactions to economic conditions and, as a result, affect the ability of the banking system to amplify or dampen economic shocks. The assessment covers the entire distribution of conditional economic predictions to measure the cost and benefit of the reforms. Looking at the means of conditional forecasts of output growth provides an indication of the costs of the reform, namely a transitory reduction in euro area gross domestic product (GDP) and in lending to the non-financial private sector. Looking at the lower percentile of output growth forecasts, i.e. growth at risk, captures the long-term benefits of the Basel III finalisation package in terms of improved resilience and the ability of the banking system to supply lending to the real economy under adverse conditions. These permanent growth-at-risk benefits ultimately outweigh the short-term costs of the reform. JEL Classification: E37, E58, G21, G28
    Keywords: banking sector, Basel III finalisation, impact assessment, real-financial feedback mechanism, regulatory policy
    Date: 2021–07

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