nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒02‒22
24 papers chosen by
Stan Miles
Thompson Rivers University

  1. Stress-testing net trading income: the case of European banks By Giglio, Carla; Shaw, Frances; Syrichas, Nicolas; Cappelletti, Giuseppe
  2. A Structural Model of Market Friction with Time-Varying Volatility By Giuseppe Buccheri; Stefano Grassi; Giorgio Vocalelli
  3. A structural approach to default modelling with pure jump processes By Jean-Philippe Aguilar; Nicolas Pesci; Victor James
  4. The costs and benefits of reinsurance By Cummins, J. David; Dionne, Georges; Gagné, Robert; Nouira, Abdelhakim
  5. Explainable models of credit losses By João A. Bastos; Sara M. Matos
  6. Time-varying properties of asymmetric volatility and multifractality in Bitcoin By Tetsuya Takaishi
  7. Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model By Pennoni, Fulvia; Bartolucci, Francesco; Forte, Gianfranco; Ametrano, Ferdinando
  8. Modeling extreme events: time-varying extreme tail shape By Schwaab, Bernd; Zhang, Xin; Lucas, André
  9. Financial Destabilization By Ken-ichi Hashimoto; Ryonghun Im; Takuma Kunieda; Akihisa Shibata
  10. The Joint Impact of Bank Capital and Funding Liquidity on the Monetary Policy's Risk-Taking Channel By Bruno de Menna
  11. A Leverage-Based Measure of Financial Stability By Adrian, Tobias; Borowiecki, Karol Jan; Tepper, Alexander
  12. Beta-Adjusted Covariance Estimation By Kirill Dragun; Kris Boudt; Orimar Sauri; Steven Vanduffel
  13. Integrating prediction in mean-variance portfolio optimization By Andrew Butler; Roy H. Kwon
  14. Does diversification protect European banks' market valuation in a pandemic? By Mathieu Simoens; Rudi Vander Vennet
  15. Markowitz portfolio selection for multivariate affine and quadratic Volterra models By Eduardo Abi Jaber; Enzo Miller; Huyên Pham
  16. Economic Uncertainty Before and During the COVID-19 Pandemic By Dave Altig; Scott Baker; Jose Maria Barrero; Nick Bloom; Phil Bunn; Scarlet Chen; Steven J. Davis; Brent Meyer; Emil Mihaylov; Paul Mizen; Nick Parker; Thomas Renault; Pawel Smietanka; Greg Thwaites
  17. Liquidity risk and the dynamics of arbitrage capital By Kondor, Peter; Vayanos, Dimitri
  18. Did Subsidies to Too-Big-To-Fail Banks Increase during the COVID-19 Pandemic? By Asani Sarkar
  19. OIL PRICE EXPOSURE OF CEE FINANCIAL COMPANIES By Alexandra Horobet; Georgiana Maria Vrinceanu; Lucian Belascu
  20. A Theory of Choice Bracketing under Risk By Mu Zhang
  21. Limited liability, strategic default and bargaining power By Balatti, Mirco; López-Quiles, Carolina
  22. Power-Law Return-Volatility Cross Correlations of Bitcoin By T. Takaishi
  23. Stock Market Returns and Oil Price Shocks: A CoVaR Analysis based on Dynamic Vine Copula Models By Julia Kielmann; Hans Manner; Aleksey Min
  24. Pandemic-Era Uncertainty on Main Street and Wall Street By Brent Meyer; Emil Mihaylov; Steven J. Davis; Nicholas Parker; David Altig; Jose Maria Barrero; Nicholas Bloom

  1. By: Giglio, Carla; Shaw, Frances; Syrichas, Nicolas; Cappelletti, Giuseppe
    Abstract: Net trading income is an important but volatile source of income for many euro area banks, highly sensitive to changes in financial market conditions. Using a representative sample of European banks, we study the distribution of net trading income (normalized by total assets) conditional to changes in key macro-financial risk factors. To map the linkages of net trading income with financial risk factors and capture non-linear effects, we implement a dynamic fixed effects quantile model using the method of moments approach. We use the model to empirically estimate and forecast the conditional net trading income distribution from which we quantify tail risk measures and expected losses across banks. We find a heterogeneous and asymmetric impact of the risk factors on the distribution of net trading income. Credit and interest rate spreads affect lower quantiles of the net trading income distribution while stock returns are an important determinant of the upper quantiles. We also find that the onset of the Covid-19 pandemic resulted in a significant increase in the 5th and 10th percentile expected capital shortfall. Moreover, adverse scenario forecasts show a wide dispersion of losses and a long-left tail is evident especially in the most severe scenarios. Our findings highlight strong inter-linkages between financial risk factors and trading income and suggest that this tractable methodology is ideal for use as an additional tool in stress test exercises. JEL Classification: C21, C23, G21, G28
    Keywords: capital shortfall, net trading income, quantile panel regression, stress testing
    Date: 2021–02
  2. By: Giuseppe Buccheri (DEF Università di Roma "Tor Vergata"); Stefano Grassi (DEF Università di Roma "Tor Vergata"); Giorgio Vocalelli (DEF Università di Roma "Tor Vergata")
    Abstract: We propose a model of price formation in which the trading price varies only if the value of the information signal is large enough to guarantee a profit in excess of transaction costs. Using transaction data only, we extract: (i) the conditional volatility of the underlying security, which is thus cleaned out by market frictions, (ii) an estimate of transaction costs. Our analysis reveals that, after correcting for frictions, the risk of illiquid securities is substantially different from what predicted by traditional volatility models. Furthermore, in periods of high volatility, our estimate of transaction costs remains highly correlated with bid-ask spreads, whereas alternative illiquidity proxies, such as the number of zero returns, loose their explanatory power.
    Keywords: Illiquidity,Market Microstructure,Volatility,Risk assessment.
    JEL: B26 C22 C58
    Date: 2021–01–30
  3. By: Jean-Philippe Aguilar; Nicolas Pesci; Victor James
    Abstract: We present a general framework for the estimation of corporate default based on a firm's capital structure, when its assets are assumed to follow a pure jump L\'evy processes; this setup provides a natural extension to usual default metrics defined in diffusion (log-normal) models, and allows to capture extreme market events such as sudden drops in asset prices, which are closely linked to default occurrence. Within this framework, we introduce several processes featuring negative jumps only and derive practical closed formulas for equity prices, which enable us to use a moment-based algorithm to calibrate the parameters from real market data and to estimate the associated default metrics. A notable feature of these models is the redistribution of credit risk towards shorter maturity: this constitutes an interesting improvement to diffusion models, which are known to underestimate short term default probabilities. We also provide extensions to a model featuring both positive and negative jumps and discuss qualitative and quantitative features of the results. For readers convenience, practical tools for model implementation and R code are also included.
    Date: 2021–02
  4. By: Cummins, J. David (Temple University); Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management); Gagné, Robert (HEC Montreal, Department of Applied Economics); Nouira, Abdelhakim (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: Purchasing reinsurance reduces insurers’ insolvency risk by stabilizing loss experience, increasing capacity, limiting liability on specific risks, and/or protecting against catastrophes. Consequently, reinsurance purchase should reduce capital costs. However, transferring risk to reinsurers is expensive. The cost of reinsurance for an insurer can be much larger than the actuarial price of the risk transferred. In this article, we analyze empirically the costs and the benefits of reinsurance for a sample of U.S. property-liability insurers. The results show that reinsurance purchase increases significantly the insurers’ costs but reduces significantly the volatility of the loss ratio. With purchasing reinsurance, insurers accept to pay higher costs of insurance production to reduce their underwriting risk.
    Keywords: Reinsurance; insolvency risk; risk management; financial intermediation; cost function; panel data
    JEL: C34 C35 G21 G22
    Date: 2021–02–10
  5. By: João A. Bastos; Sara M. Matos
    Abstract: Credit risk management is an area where regulators expect banks to have trans-parent and auditable risk models, which would preclude the use of more accurate black-box models. Furthermore, the opaqueness of these models may hide unknownbiases that may lead to unfair lending decisions. In this study, we show that banksdo not have to sacrifice prediction accuracy at the cost of model transparency tobe compliant with regulatory requirements. We illustrate this by showing that the predictions of credit losses given by a black-box model can be easily explained in terms of their inputs. Because black-box models are better at uncovering complex patterns in the data, banks should consider the determinants of credit losses suggested by these models in lending decisions and pricing of credit exposures.
    Keywords: Credit risk·Loss given default·Recovery rates·Explainable machine learning·Forecasting
    Date: 2021–02
  6. By: Tetsuya Takaishi
    Abstract: This study investigates the volatility of daily Bitcoin returns and multifractal properties of the Bitcoin market by employing the rolling window method and examines relationships between the volatility asymmetry and market efficiency. Whilst we find an inverted asymmetry in the volatility of Bitcoin, its magnitude changes over time, and recently, it has become small. This asymmetric pattern of volatility also exists in higher frequency returns. Other measurements, such as kurtosis, skewness, average, serial correlation, and multifractal degree, also change over time. Thus, we argue that properties of the Bitcoin market are mostly time dependent. We examine efficiency-related measures: the Hurst exponent, multifractal degree, and kurtosis. We find that when these measures represent that the market is more efficient, the volatility asymmetry weakens. For the recent Bitcoin market, both efficiency-related measures and the volatility asymmetry prove that the market becomes more efficient.
    Date: 2021–02
  7. By: Pennoni, Fulvia; Bartolucci, Francesco; Forte, Gianfranco; Ametrano, Ferdinando
    Abstract: A multivariate hidden Markov model is proposed to explain the price evolution of Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. The observed daily log-returns of these five major cryptocurrencies are modeled jointly. They are assumed to be correlated according to a variance-covariance matrix conditionally on a latent Markov process having a finite number of states. For the purpose of comparing states according to their volatility, we estimate specific variance-covariance matrix varying across states. Maximum likelihood estimation of the model parameters is carried out by the Expectation-Maximization algorithm. The hidden states represent different phases of the market identified through the estimated expected values and volatility of the log-returns. We reach interesting results in detecting these phases of the market and the implied transition dynamics. We also find evidence of structural medium term trend in the correlations of Bitcoin with the other cryptocurrencies.
    Keywords: Bitcoin, Bitcoin cash, decoding, Ethereum, expectation-maximization algorithm, Litecoin, Ripple, time-series
    JEL: C32 C51 C53
    Date: 2020
  8. By: Schwaab, Bernd; Zhang, Xin; Lucas, André
    Abstract: We propose a dynamic semi-parametric framework to study time variation in tail parameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a conditional framework to allow for time-variation in the tail shape parameters. The score-driven updates used improve the expected Kullback-Leibler divergence between the model and the true data generating process on every step even if the GPD only fits approximately and the model is mis-specified, as will be the case in any finite sample. This is confirmed in simulations. Using the model, we find that Eurosystem sovereign bond purchases during the euro area sovereign debt crisis had a beneficial impact on extreme upper tail quantiles, leaning against the risk of extremely adverse market outcomes while active. JEL Classification: C22, G11
    Keywords: dynamic tail risk, European Central Bank (ECB), extreme value theory, observation-driven models, Securities Markets Programme (SMP)
    Date: 2021–02
  9. By: Ken-ichi Hashimoto (Graduate School of Economics, Kobe University); Ryonghun Im (Kyoto University); Takuma Kunieda (Kwansei Gakuin University); Akihisa Shibata (Kyoto University)
    Abstract: This paper uses a dynamic general equilibrium model to examine whether financial innovations destabilize an economy. Applying a neoclassical production function, we demonstrate that as financial frictions are mitigated, the economy loses stability and a flip bifurcation occurs at a certain level of financial frictions under an empirically plausible elasticity of substitution between capital and labor. Furthermore, the amplitude of fluctuations increases as financial frictions are mitigated and is maximized when the financial market approaches perfection. These outcomes imply that financial innovations are likely to destabilize an economy.
    Date: 2021–02
  10. By: Bruno de Menna (LEREPS - Laboratoire d'Etude et de Recherche sur l'Economie, les Politiques et les Systèmes Sociaux - UT1 - Université Toulouse 1 Capitole - UT2J - Université Toulouse - Jean Jaurès - Institut d'Études Politiques [IEP] - Toulouse - ENSFEA - École Nationale Supérieure de Formation de l'Enseignement Agricole de Toulouse-Auzeville)
    Abstract: Despite an extensive literature on the risk–taking channel of monetary policy, the joint impact of bank capital and deposits on the latter remains poorly documented. Yet that prospect is essential for monetary policy taking action under the Basel III framework involving concomitant capital and funding liquidity standards. Using data on euro area from 1999 to 2018 and triple interactions between monetary policy, equity and funding liquidity, we shed light on a "crowding–out of deposits" effect prior to the 2008 GFC which supports the need for simultaneous capital and funding liquidity ratios to mitigate the monetary transmission to bank credit risk. Interestingly, our findings also highlight a missing "crowding–out of deposits" effect amongst poorly efficient banks in the aftermath of the GFC. As a result, a trade-off arises between financial stability and increased funding liquidity for these financial intermediaries, making a special treatment required for inefficient banks operating in a low interest rate environment. These results challenge the implementation of uniform funding liquidity requirements across the euro area.
    Keywords: Credit risk,Monetary policy transmission,Capital buffer,Funding liquidity
    Date: 2021–02–11
  11. By: Adrian, Tobias (Monetary and Capital Markets Department); Borowiecki, Karol Jan (Department of Business and Economics); Tepper, Alexander (Columbia University)
    Abstract: The size and the leverage of financial market investors and the elasticity of demand of unlevered investors define MinMaSS, the smallest market size that can support a given degree of leverage. The financial system's potential for financial crises can be measured by the stability ratio, the fraction of total market size to MinMaSS. We use that financial stability metric to gauge the buildup of vulnerability in the run-up to the 1998 Long-Term Capital Management crisis and argue that policymakers could have detected the potential for the crisis.
    Keywords: Leverage; financial crisis; financial stability; minimum market size for stability; MinMaSS; stability ratio; Long-Term Capital Management; LTCM
    JEL: G01 G10 G20 G21
    Date: 2021–02–17
  12. By: Kirill Dragun; Kris Boudt; Orimar Sauri; Steven Vanduffel (-)
    Abstract: The increase in trading frequency of Exchanged Traded Funds (ETFs) presents a positive externality for nancial risk management when the price of the ETF is available at a higher frequency than the price of the component stocks. The positive spillover consists in improving the accuracy of pre-estimators of the integrated covariance of the stocks included in the ETF. The proposed Beta Adjusted Covariance (BAC) equals the preestimator plus a minimal adjustment matrix such that the covariance-implied stock-ETF beta equals a target beta. We focus on the Hayashi and Yoshida (2005) pre-estimator and derive the asymptotic distribution of its implied stock-ETF beta. The simulation study conrms that the accuracy gains are substantial in all cases considered. In the empirical part of the paper, we show the gains in tracking error eciency when using the BAC adjustment for constructing portfolios that replicate a broad index using a subset of stocks.
    Keywords: High-frequency data, realized covariances, ETF, asynchronicity, stock-ETF beta, Localized Hayashi-Yoshida, Index tracking
    JEL: C22 C51 C53 C58
    Date: 2021–02
  13. By: Andrew Butler; Roy H. Kwon
    Abstract: Many problems in quantitative finance involve both predictive forecasting and decision-based optimization. Traditionally, predictive models are optimized with unique prediction-based objectives and constraints, and are therefore unaware of how those predictions will ultimately be used in the context of their final decision-based optimization. We present a stochastic optimization framework for integrating regression based predictive models in a mean-variance portfolio optimization setting. Closed-form analytical solutions are provided for the unconstrained and equality constrained case. For the general inequality constrained case, we make use of recent advances in neural-network architecture for efficient optimization of batch quadratic-programs. To our knowledge, this is the first rigorous study of integrating prediction in a mean-variance portfolio optimization setting. We present several historical simulations using global futures data and demonstrate the benefits of the integrated approach in comparison to the decoupled alternative.
    Date: 2021–02
  14. By: Mathieu Simoens; Rudi Vander Vennet (-)
    Abstract: We use the Covid-19 pandemic to assess whether diversification in various dimensions can protect European banks from substantial negative valuation shocks. Our results demonstrate that functional diversification acts as an economically significant shock absorber: it mitigates banks' stock market decline by approximately 10 percentage points. Loan portfolio diversification also contributes to dampening the valuation shock, but with a much lower impact (4.4 percentage points). Geographical diversification fails to act as a shock absorber. Banks with lower pre-Covid systematic risk, higher liquidity buyers, higher cost eficiency and active in countries with better post-Covid growth prospects weathered the storm better.
    Keywords: European banks, Covid-19, valuation, functional diversification, geographical diversification
    JEL: G21 G28 G01
    Date: 2021–02
  15. By: Eduardo Abi Jaber (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Enzo Miller (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique); Huyên Pham (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)
    Abstract: This paper concerns portfolio selection with multiple assets under rough covariance matrix. We investigate the continuous-time Markowitz mean-variance problem for a multivariate class of affine and quadratic Volterra models. In this incomplete non-Markovian and non-semimartingale market framework with unbounded random coefficients, the optimal portfolio strategy is expressed by means of a Riccati backward stochastic differential equation (BSDE). In the case of affine Volterra models, we derive explicit solutions to this BSDE in terms of multi-dimensional Riccati-Volterra equations. This framework includes multivariate rough Heston models and extends the results of \cite{han2019mean}. In the quadratic case, we obtain new analytic formulae for the the Riccati BSDE and we establish their link with infinite dimensional Riccati equations. This covers rough Stein-Stein and Wishart type covariance models. Numerical results on a two dimensional rough Stein-Stein model illustrate the impact of rough volatilities and stochastic correlations on the optimal Markowitz strategy. In particular for positively correlated assets, we find that the optimal strategy in our model is a `buy rough sell smooth' one.
    Keywords: Stein-Stein and Wishart models,Riccati equations,non-Markovian Heston,multi- dimensional Volterra process,rough volatility,Mean-variance portfolio theory,correlation matrices
    Date: 2020
  16. By: Dave Altig (Federal Reserve Bank of Atlanta); Scott Baker (Northwestern University - Kellogg School of Management); Jose Maria Barrero (Stanford University - Department of Economics); Nick Bloom (Stanford University - Graduate School of Business); Phil Bunn (Bank of England); Scarlet Chen (Stanford University - Department of Economics); Steven J. Davis (University of Chicago - Booth School of Business); Brent Meyer (Federal Reserve Bank of Atlanta); Emil Mihaylov (Federal Reserve Bank of Atlanta); Paul Mizen (University of Nottingham - School of Economics); Nick Parker (Federal Reserve Bank of Atlanta); Thomas Renault (University Paris 1 Panthéon-Sorbonne); Pawel Smietanka (Bank of England); Greg Thwaites (University of Nottingham - School of Economics)
    Abstract: We consider several economic uncertainty indicators for the US and UK before and during the COVID-19 pandemic: implied stock market volatility, newspaper-based economic policy uncertainty, twitter chatter about economic uncertainty, subjective uncertainty about future business growth, and disagreement among professional forecasters about future GDP growth. Three results emerge. First, all indicators show huge uncertainty jumps in reaction to the pandemic and its economic fallout. Indeed, most indicators reach their highest values on record. Second, peak amplitudes differ greatly – from an 80 percent rise (relative to January 2020) in two-year implied volatility on the S&P 500 to a 20-fold rise in forecaster disagreement about UK growth. Third, time paths also differ: Implied volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting the difference in uncertainty measures between Wall Street and Main Street.
    Keywords: Forward-looking uncertainty measures, volatility, COVID-19, coronavirus
    JEL: D80 E22 E66 G18 L50
    Date: 2020
  17. By: Kondor, Peter; Vayanos, Dimitri
    Abstract: We develop a continuous-time model of liquidity provision in which hedgers can trade multiple risky assets with arbitrageurs. Arbitrageurs have constant relative risk-aversion (CRRA) utility, while hedgers' asset demand is independent of wealth. An increase in hedgers' risk aversion can make arbitrageurs endogenously more risk-averse. Because arbitrageurs generate endogenous risk, an increase in their wealth or a reduction in their CRRA coefficient can raise risk premia despite Sharpe ratios declining. Arbitrageur wealth is a priced risk factor because assets held by arbitrageurs offer high expected returns but suffer the most when wealth drops. Aggregate illiquidity, which declines in wealth, captures that factor.
    Keywords: liquidity risk; wealth effects; heterogeneous agents; intermediary asset pricing; endogenous risk; 336585
    JEL: F3 G3
    Date: 2019–06–01
  18. By: Asani Sarkar
    Abstract: Once a bank grows beyond a certain size or becomes too complex and interconnected, investors often perceive that it is “too big to fail” (TBTF), meaning that if the bank were to become distressed, the government would likely bail it out. In a recent post, I showed that the implicit funding subsidies to systemically important banks (SIBs) declined, on average, after a set of reforms for eliminating TBTF perceptions was implemented. In this post, I discuss whether these subsidies increased again during the COVID-19 pandemic and, if so, whether the increase accrued to large firms in all sectors of the economy.
    Keywords: Too-Big-To-Fail; global banks; systemic risk; Financial Stability Board; COVID-19
    JEL: G32 G21
    Date: 2021–02–11
  19. By: Alexandra Horobet (The Bucharest University of Economic Studies, Romania); Georgiana Maria Vrinceanu (The Bucharest University of Economic Studies, Romania); Lucian Belascu (“Lucian Blaga†University of Sibiu, Romania)
    Abstract: In recent years an alarming situation concerning the global financial markets is represented by the fact that Brent crude oil price and stock prices created the impression that they are strongly correlated. Besides, crude oil represents an indispensable and critical resource for the world economy and European Union member countries are net oil importers. In this general framework, the main purpose of this paper is to investigate the exposure to oil price risk of financial companies listed on stock exchanges from Central and Eastern European countries using monthly datasets covering the period between January 2011 and December 2018. The empirical analysis includes financial companies from seven economies from Central and Eastern Europe, all EU members and oil importers: Croatia, Czech Republic, Hungary, Poland, Romania, Slovakia and Slovenia. We use Brent crude oil prices, companies’ stock returns, local stock market indices, the Dow Jones Europe Financials Index and foreign exchange rates of the domestic currencies against the US dollar, as well as an index that capture the financial sector – related stress (CLIFS) in order to shed light on the idiosyncrasies of the oil price – returns relationship. The relevance of financial companies’ exposures to oil price changes is identified using the panel data methodology in a traditional OLS structure, as well as in a dynamic ARDL panel estimation that capture the longrun versus the short-run exposure of CEE financial companies to oil price risk. Our results suggest that oil price fluctuations impact the stock prices of financial companies from CEE countries, but the link between stock return and oil price risk has some specificities and is mostly observable on the long run. The oil price changes have a negative impact on companies’ stock returns, thus proving that they should be understood as a risk factor for the financial sector. At the same time, our results indirectly highlight the ubiquitous exposure of CEE economies to market risk factors and the worrying role of economy-wide risk transmitter of the financial sector.
    Keywords: Oil price, Exposure, Central and Eastern Europe, Financial sector
    JEL: F23 G15 G32
    Date: 2019–12
  20. By: Mu Zhang
    Abstract: Aggregating risks from multiple sources can be complex and demanding, and decision makers usually adopt heuristics to simplify the decision process. This paper axiomatizes two such heuristics, narrow bracketing and correlation neglect, by relaxing the standard independence axiom in the expected utility benchmark. Our representation theorem allows for either narrow bracketing, or correlation neglect, or both of them. The flexibility of our framework allows for applications in various setups. For example, we accommodate the experimental evidence in narrow bracketing and risk aversion over small gambles with background risk. In intertemporal choices, we show how our framework unifies three seemingly distinct models in the literature and introduce a new model that can satisfy many desirable normative properties in time preferences simultaneously, including indifference to temporal resolution of uncertainty, dynamic consistency and separation of time and risk preferences. One special class of the model shares the same predictions as Epstein and Zin (1989) in macroeconomics and finance applications, and is immune to the critique in Epstein, Farhi, and Strzalecki (2014).
    Date: 2021–02
  21. By: Balatti, Mirco; López-Quiles, Carolina
    Abstract: In this paper we examine the effects of limited liability on mortgage dynamics. While the literature has focused on default rates, renegotiation, or loan rates individually, we study them together as equilibrium outcomes of the strategic interaction between lenders and borrowers. We present a simple model of default and renegotiation where the degree of limited liability plays a key role in agents' strategies. We then use Fannie Mae loan performance data to test the predictions of the model. We focus on Metropolitan Statistical Areas that are crossed by a State border in order to exploit the discontinuity in regulation around the borders of States. As predicted by the model, we find that limited liability results in higher default rates and renegotiation rates. Regarding loan pricing, while the model predicts higher interest rates for limited liability loans, we find no such evidence in the Fannie Mae data. We further investigate this by using loan application data, which contains the interest rates on loans sold to private vs public investors. We find that private investors do price in the difference in ex-ante predictable default risk for limited liability loans. JEL Classification: D10, E40, G21, R20, R30
    Keywords: debt repudiation, discontinuity, lender recourse, mortgage contracts, renegotiation
    Date: 2021–01
  22. By: T. Takaishi
    Abstract: This paper investigates the return-volatility asymmetry of Bitcoin. We find that the cross correlations between return and volatility (squared return) are mostly insignificant on a daily level. In the high-frequency region, we find thata power-law appears in negative cross correlation between returns and future volatilities, which suggests that the cross correlation is \revision{long ranged}. We also calculate a cross correlation between returns and the power of absolute returns, and we find that the strength of \revision{the cross correlations} depends on the value of the power.
    Date: 2021–02
  23. By: Julia Kielmann (Technical University of Munich, Germany); Hans Manner (University of Graz, Austria); Aleksey Min (Technical University of Munich, Germany)
    Abstract: Crude oil plays a significant role in economic developments in the world. Understanding the relationship between oil price changes and stock market returns helps to improve portfolio strategies and risk positions. Kilian (2009) proposes to decompose the oil price into three types of oil price shocks by using a structural vector autoregression (SVAR) model. This paper investigates the dynamic, non-linear dependence and risk spillover effects between BRICS stock returns and the different types of oil price shocks using an appropriate multivariate and dynamic copula model. Risk is measured using the conditional Value-at-Risk, conditioning on one or more simultaneous oil and stock market shocks. For this purpose, a D-vine based quantile regression model and the GAS copula model are combined. Our results show, inter alia, that the early stages of the Covid-19 crisis leads to increasing risk levels in the BRICS stock markets except for the Chinese one, which has recovered quickly and therefore shows no changes in the risk level.
    Keywords: Oil prices; risk management; time-varying copula; D-vine copula; CoVaR.
    JEL: C12 C32 C52 C53
    Date: 2021–01
  24. By: Brent Meyer (Federal Reserve Bank of Atlanta); Emil Mihaylov (Federal Reserve Bank of Atlanta); Steven J. Davis (University of Chicago - Booth School of Business; Hoover Institution; NBER); Nicholas Parker (Federal Reserve Bank of Atlanta); David Altig (Federal Reserve Bank of Atlanta); Jose Maria Barrero (Instituto Tecnológico Autónomo de México - Business School); Nicholas Bloom (University of Chicago - Department of Economics; CEPR; NBER)
    Abstract: We draw on the monthly Survey of Business Uncertainty (SBU) to make three observations about pandemic-era uncertainty in the U.S. economy. First, equity market traders and executives of nonfinancial firms share similar assessments about uncertainty at one-year look- ahead horizons. That is, the one-year VIX has moved similarly to our survey-based measure of (average) firm-level subjective uncertainty at one-year forecast horizons. Second, looking within the distribution of beliefs in the SBU reveals that firm-level expectations shifted towards upside risk in the latter part of 2020. In this sense, decision makers in nonfinancial businesses share some of the optimism that seems manifest in equity markets. Third, and despite the positive shift in tail risks, overall uncertainty continues to substantially dampen capital spending plans, pointing to a source of weak growth in potential GDP.
    Keywords: Business expectations, uncertainty, subjective forecast distributions, surveys
    JEL: L2 M2 O32 O33
    Date: 2020

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