nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒05‒04
thirty papers chosen by
Stan Miles
Thompson Rivers University

  1. Canadian Business Risk Management Programs: In the Eye of the COVID-19 Crisis – A Brief Assessment By Maurice Doyon; Alphonse G. Singbo
  2. Refining Understanding of Corporate Failure through a Topological Data Analysis Mapping of Altman's Z-Score Model By Wanling Qiu; Simon Rudkin; Pawel Dlotko
  3. Designing a NISQ reservoir with maximal memory capacity for volatility forecasting By Samudra Dasgupta; Kathleen E. Hamilton; Pavel Lougovski; Arnab Banerjee
  4. Central Clearing and Systemic Liquidity Risk By Thomas B. King; Travis D. Nesmith; Anna L. Paulson; Todd Prono
  5. Investor-herding and risk-profiles: A State-Space Model-based Assessment By Harminder B. Nath; Robert D. Brooks
  6. A dynamic network model to measure exposure diversification in the Austrian interbank market By Hledik, Juraj; Rastelli, Riccardo
  7. Volatility spillovers and capital buffers among the G-SIBs By Paul D McNelis; James Yetman
  8. Modeling Institutional Credit Risk with Financial News By Tam Tran-The
  9. Buffering Covid-19 losses - the role of prudential policy By Mathias Drehmann; Marc Farag; Nicola Tarashev; Kostas Tsatsaronis
  10. Negative monetary policy rates and systemic banks’ risk-taking: evidence from the euro area securities register By Bubeck, Johannes; Maddaloni, Angela; Peydró, José-Luis
  11. Leverage and margin spirals in fixed income markets during the Covid-19 crisis By Andreas Schrimpf; Hyun Song Shin; Vladyslav Sushko
  12. Persistence in the Market Risk Premium: Evidence across Countries By Guglielmo Maria Caporale; Luis A. Gil-Alana; Miguel Martin-Valmayor
  13. A Simple Method for Extracting the Probability of Default from American Put Option Prices By Bo Young Chang, Greg Orosi; Greg Orosi
  14. Credit risk statistical information of the Bank of Italy and the new AnaCredit data collection By Maria Di Noia; Davide Moretti
  15. Identifying Risk Factors and Their Premia: A Study on Electricity Prices By Wei Wei; Asger Lunde
  16. The Janus Face of bank geographic complexity By Iñaki Aldasoro; Bryan Hardy; Maximilian Jager
  17. International bank lending and corporate debt structure By José María Serena Garralda; Serafeim Tsoukas
  18. Risk Sharing Externalities By Luigi Bocola; Guido Lorenzoni
  19. Shaking Things Up: On the Stability of Risk and Time Preferences By Michel Beine; Gary Charness; Arnaud Dupuy; Majlinda Joxhe
  20. Risk management in tradition agriculture: intercropping in Italian wine production By Martínelli Lasheras, Pablo; Federico, Giovanni
  21. The Trading Response of Individual Investors to Local Bankruptcies By Christine Laudenbach; Benjamin Loos; Jenny Pirschel; Johannes Wohlfart
  22. Oil-Shocks and Directional Predictability of Macroeconomic Uncertainties of Developed Economies: Evidence from High-Frequency Data By Syed Jawad Hussain Shahzad; Rangan Gupta; Riza Demirer; Christian Pierdzioch
  23. Generalized Robustness and Dynamic Pessimism By Pascal J. Maenhout; Andrea Vedolin; Hao Xing
  24. A multivariate micro-level insurance counts model with a Cox process approach By Benjamin Avanzi; Gregory Clive Taylor; Bernard Wong; Xinda Yang
  25. The recent distress in corporate bond markets: cues from ETFs By Sirio Aramonte; Fernando Avalos
  26. Does the Yield Curve Signal Recessions? New Evidence from an International Panel Data Analysis By Jean-Baptiste Hasse; Quentin Lajaunie
  27. Commodity Price Volatility and the Economic Uncertainty of Pandemics By Bakas, Dimitrios; Triantafyllou, Athanasios
  28. Commodity Price Uncertainty as a Leading Indicator of Economic Activity By Bakas, Dimitrios; Ioakimidis, Marilou; Triantafyllou, Athanasios
  29. Optimizing the reliability of a bank with Logistic Regression and Particle Swarm Optimization By Vadlamani Ravi; Vadlamani Madhav
  30. Important Factors Determining Fintech Loan Default: Evidence from the LendingClub Consumer Platform By Christophe Croux; Julapa Jagtiani; Tarunsai Korivi; Milos Vulanovic

  1. By: Maurice Doyon; Alphonse G. Singbo
    Abstract: In the wake of the announcement of numerous federal ad hoc programs to stimulate the economy in response to the COVID-19 crisis, it is worth discussing how the suite of business risk management tools that are part of the Canadian Agricultural Partnership is likely to respond to the negative impacts of the pandemic on Canadian agriculture. We argue for a short term bonification of AgriInvest and AgriStability to face the challenges ahead and to minimize the inefficiencies associated with ad hoc programs. More broadly, our arguments to use a risk management tool for a black swan event instead of ad hoc programs is likely to fuel the debate between risk management and income support.
    Keywords: COVID-19,Business Risk Management,Agricultural Sector,Canada,
    Date: 2020–04–21
  2. By: Wanling Qiu; Simon Rudkin; Pawel Dlotko
    Abstract: Corporate failure resonates widely leaving practitioners searching for understanding of default risk. Managers seek to steer away from trouble, credit providers to avoid risky loans and investors to mitigate losses. Applying Topological Data Analysis tools this paper explores whether failing firms from the United States organise neatly along the five predictors of default proposed by the Z-score models. Firms are represented as a point cloud in a five dimensional space, one axis for each predictor. Visualising that cloud using Ball Mapper reveals failing firms are not often neighbours. As new modelling approaches vie to better predict firm failure, often using black boxes to deliver potentially over-fitting models, a timely reminder is sounded on the importance of evidencing the identification process. Value is added to the understanding of where in the parameter space failure occurs, and how firms might act to move away from financial distress. Further, lenders may find opportunity amongst subsets of firms that are traditionally considered to be in danger of bankruptcy but actually sit in characteristic spaces where failure has not occurred.
    Date: 2020–04
  3. By: Samudra Dasgupta; Kathleen E. Hamilton; Pavel Lougovski; Arnab Banerjee
    Abstract: Quantitative risk management, particularly volatility forecasting, is critically important to traders, portfolio managers as well as policy makers. In this paper, we applied quantum reservoir computing for forecasting VIX (the CBOE volatility index), a highly non-linear and memory intensive `real-life' signal that is driven by market dynamics and trader psychology and cannot be expressed by a deterministic equation. As a first step, we lay out the systematic design considerations for using a NISQ reservoir as a computing engine (which should be useful for practitioners). We then show how to experimentally evaluate the memory capacity of various reservoir topologies (using IBM-Q's Rochester device) to identify the configuration with maximum memory capacity. Once the optimal design is selected, the forecast is produced by a linear combination of the average spin of a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards. We test the forecast performance over the sub-prime mortgage crisis period (Dec 2007 - Jun 2009). Our results show a remarkable ability to predict the volatility during the Great Recession using today's NISQs.
    Date: 2020–04
  4. By: Thomas B. King; Travis D. Nesmith; Anna L. Paulson; Todd Prono
    Abstract: By stepping between bilateral counterparties, a central counterparty (CCP) transforms credit exposure. CCPs generally improve financial stability. Nevertheless, large CCPs are by nature concentrated and interconnected with major global banks. Moreover, although they mitigate credit risk, CCPs create liquidity risks, because they rely on participants to provide cash. Such requirements increase with both market volatility and default; consequently, CCP liquidity needs are inherently procyclical. This procyclicality makes it more challenging to assess CCP resilience in the rare event that one or more large financial institutions default. Liquidity-focused macroprudential stress tests could help to assess and manage this systemic liquidity risk.
    Keywords: Financial systems; Central counterparties; CCPs; Margin; Liquidity risk; Systemic risk; Financial stability; Procyclicality
    JEL: E58 G21 G23 G28 N22
    Date: 2020–01–31
  5. By: Harminder B. Nath; Robert D. Brooks
    Abstract: This paper, using the Australian stock market data, examines the investor-herding and riskprofiles link that has implications for asset pricing, portfolio diversification and foreign investments. As investors may herd towards a specific factor, sector or style to combat market conditions for optimizing investment returns, examining such herding can reveal investors' risk profiles. We employ State-Space models for extracting time series of herd dynamics and the proportion of signal explained by herding (PoSEH). Market volatility has a significant negative effect on PoSEH, with the most/least effect on high/low performance days of stock returns. Using quantile regression, we observe that herding and adverseherding can emerge during the worst and best performance days of stock returns, and that extreme volatility can bring herding to a near halt. The study reveals the presence of a regulated stock market environment and risk-aversion tendencies among investors.
    Keywords: Herd behaviour, risk aversion, state-space models, quantile regression.
    JEL: C31 C32 G12 G14
    Date: 2020
  6. By: Hledik, Juraj; Rastelli, Riccardo
    Abstract: We design a statistical model for measuring the homogeneity of a financial network that evolves over time. Our model focuses on the level of diversification of financial institutions; that is, whether they are more inclined to distribute their assets equally among partners, or if they rather concentrate their commitments towards a limited number of institutions. Crucially, a Markov property is introduced to capture time dependencies and to make our measures comparable across time. We apply the model on an original dataset of Austrian interbank exposures. The temporal span encompasses the onset and development of the financial crisis in 2008 as well as the beginnings of the European sovereign debt crisis in 2011. Our analysis highlights an overall increasing trend for network homogeneity, whereby core banks have a tendency to distribute their market exposures more equally across their partners. JEL Classification: X00, X01, X02, X03
    Keywords: Austrian interbank market, Bayesian inference, dynamic networks, latent variable models, systemic risk
    Date: 2020–04
  7. By: Paul D McNelis; James Yetman
    Abstract: We assess the dynamics of volatility spillovers among global systemically important banks (G-SIBs). We measure spillovers using vector-autoregressive models of range volatility of the equity prices of G-SIBs, together with machine learning methods. We then compare the size of these spillovers with the degree of systemic importance measured by the Basel Committee on Banking Supervision's G-SIB bucket designations. We find a high positive correlation between the two. We also find that higher bank capital reduces volatility spillovers, especially for banks in higher G-SIB buckets. Our results suggest that requiring banks that are designated as being more systemically important globally to hold additional capital is likely to reduce volatility spillovers from them to other large banks.
    Keywords: G-SIBs, contagion, connectedness, bank capital, cross validation
    JEL: C58 F65 G21 G28
    Date: 2020–04
  8. By: Tam Tran-The
    Abstract: Credit risk management, the practice of mitigating losses by understanding the adequacy of a borrower's capital and loan loss reserves, has long been imperative to any financial institution's long-term sustainability and growth. MassMutual is no exception. The company is keen on effectively monitoring downgrade risk, or the risk associated with the event when credit rating of a company deteriorates. Current work in downgrade risk modeling depends on multiple variations of quantitative measures provided by third-party rating agencies and risk management consultancy companies. As these structured numerical data become increasingly commoditized among institutional investors, there has been a wide push into using alternative sources of data, such as financial news, earnings call transcripts, or social media content, to possibly gain a competitive edge in the industry. The volume of qualitative information or unstructured text data has exploded in the past decades and is now available for due diligence to supplement quantitative measures of credit risk. This paper proposes a predictive downgrade model using solely news data represented by neural network embeddings. The model standalone achieves an Area Under the Receiver Operating Characteristic Curve (AUC) of more than 80 percent. The output probability from this news model, as an additional feature, improves the performance of our benchmark model using only quantitative measures by more than 5 percent in terms of both AUC and recall rate. A qualitative evaluation also indicates that news articles related to our predicted downgrade events are specially relevant and high-quality in our business context.
    Date: 2020–04
  9. By: Mathias Drehmann; Marc Farag; Nicola Tarashev; Kostas Tsatsaronis
    Abstract: By allowing banks to run down some of their buffers, policymakers are sending a strong signal about their resolve to lessen the economic fallout from the pandemic. Such prudential measures complement the main policy levers: monetary and fiscal instruments. To avoid a reduction in credit to the real economy, authorities need to ensure that banks have the capacity and willingness to make use of the flexibility afforded by the buffer release. Payout restrictions on banks and risk-sharing between banks and the public sector will be key. r banks to continue playing a positive role in the supply of funding during the recovery, they should maintain usable buffers for a long period, as losses from a severe recession will take time to materialise.
    Date: 2020–04–24
  10. By: Bubeck, Johannes; Maddaloni, Angela; Peydró, José-Luis
    Abstract: We show that negative monetary policy rates induce systemic banks to reach-for-yield. For identification, we exploit the introduction of negative deposit rates by the European Central Bank in June 2014 and a novel securities register for the 26 largest euro area banking groups. Banks with more customer deposits are negatively affected by negative rates, as they do not pass negative rates to retail customers, in turn investing more in securities, especially in those yielding higher returns. Effects are stronger for less capitalized banks, private sector (financial and non-financial) securities and dollar-denominated securities. Affected banks also take higher risk in loans. JEL Classification: E43, E52, E58, G01, G21
    Keywords: banks, negative rates, non-standard monetary policy, reach-for-yield, securities
    Date: 2020–04
  11. By: Andreas Schrimpf; Hyun Song Shin; Vladyslav Sushko
    Abstract: For a two-week period in mid-March 2020, government bond markets experienced uncharacteristic turbulence, sometimes selling off sharply in risk-off episodes when they would normally attract safe haven flows. Evidence in the US Treasury market points to forced selling of treasury securities by investors who had attempted to exploit small yield differences through the use of leverage. Even though government bonds are safe assets, large holdings by leveraged investors may detract from orderly market functioning and may necessitate interventions by the central bank.
    Date: 2020–04–02
  12. By: Guglielmo Maria Caporale; Luis A. Gil-Alana; Miguel Martin-Valmayor
    Abstract: This paper provides evidence on the degree of persistence of one of the key components of the CAPM, namely the market risk premium, as well as its volatility. The analysis applies fractional integration methods to data for the US, Germany and Japan, and for robustness purposes considers different time horizons (2, 5 and 10 years) and frequencies (monthly and weekly). The empirical findings in most cases imply that the market risk premium is a highly persistent variable which can be characterized as a random walk process, whilst its volatility is less persistent and exhibits stationary long-memory behaviour. There is also evidence that in the case of the US the degree of persistence has changed as a results of various events; this is confirmed by both endogenous break tests and the associated subsample estimates. Market participants should take this evidence into account when designing their investment strategies.
    Keywords: CAPM, risk premium, persistence, mean reversion, long memory
    JEL: C22 G11
    Date: 2020
  13. By: Bo Young Chang, Greg Orosi; Greg Orosi
    Abstract: In this paper, we present a novel method to extract the risk-neutral probability of default of a firm from American put option prices. Building on the idea of a default corridor proposed in Carr and Wu (2011), we derive a parsimonious closed-form formula for American put option prices from which the probability of default can be inferred. The proposed method is easy to implement and helps overcome the main limitation of the method used in Carr and Wu (2011), which relies on the price of one deep-out-of-the-money put option. Our empirical results are based on seven large U.S. firms for the period 2002 to 2010. These results show that, in some cases, the option-implied probability of default can provide a more accurate estimate of default probability, compared to the estimates implied from credit default swap spreads.
    Keywords: Asset Pricing; Financial markets; Market structure and pricing
    JEL: G1 G13 G3 G33
    Date: 2020–04
  14. By: Maria Di Noia (Banca d’Italia); Davide Moretti (Banca d’Italia)
    Abstract: This paper provides an overview of the statistical information on credit risk managed by the Bank of Italy. The Institute has a long tradition of systematic and detailed financial data collection. With specific reference to information on credit risk, the Bank of Italy has managed the Central Credit Register since the early 1960s and, more recently, introduced new data collections on credit risk primarily in response to specific supervisory needs. At international level, also due to the increasing demand for data subsequent to the global financial crisis of the last decade, the data and data collection systems of euro-area countries have become increasingly harmonized and granular. In this context, the introduction of the AnaCredit framework can be considered the main driver behind the spread of this ‘new paradigm’ of data collection, and may also prove to be a strong incentive for national and European Authorities to rationalize the reporting burden for reporting agents.
    Keywords: AnaCredit, Central Credit Register, credit risk, non-performing loans, granular surveys, statistics
    JEL: G21 C81
    Date: 2020–04
  15. By: Wei Wei; Asger Lunde
    Abstract: We propose a multi-factor model and an estimation method based on particle MCMC to identify risk factors in electricity prices. Our model identifies long-run prices, shortrun deviations, and spikes as three main risk factors in electricity spot prices. Under our model, different risk factors have distinct impacts on futures prices and can carry different risk premia. We generalize the Fama-French regressions to analyze properties of true risk premia. We show that model specification plays an important role in detecting time varying risk premia. Using spot and futures prices in the Germany/Austria market, we demonstrate that our proposed model surpasses alternative models that have less risk factors in forecasting spot prices and in detecting time varying risk premia.
    Keywords: Risk factors, risk premia, futures, particle filter, MCMC.
    JEL: C51 G13 Q4
    Date: 2020
  16. By: Iñaki Aldasoro; Bryan Hardy; Maximilian Jager
    Abstract: We study the relationship between bank geographic complexity and risk using a unique dataset of 96 global bank holding companies (BHCs) over 2008-2016. From data on the affiliate network of internationally active banking entities, we construct a measure of geographic coverage and complexity for each BHC. We find that higher geographic complexity heightens banks' capacity to absorb local economic shocks, reducing their risk. However, higher geographic complexity is also associated with a higher vulnerability to global shocks and less impact of prudential regulation, increasing their risk. Geographic complexity helps more (with respect to local shocks) and hurts less (with respect to global shocks) if countries' business cycles are misaligned. Large, international regulatory reforms such as the implementation of the GSIB framework and the European Single Supervisory Mechanism reduce bank risk, but geographic complexity weakens this effect. Bank geographic complexity therefore has a Janus face, decreasing some but increasing other aspects of bank risk.
    Keywords: bank geographic complexity, bank risk, bank regulation, GSIB
    JEL: G21 G28
    Date: 2020–04
  17. By: José María Serena Garralda; Serafeim Tsoukas
    Abstract: Using a cross-country sample of bank-dependent public firms we study the international spillovers of a change in banking regulation on corporate borrowing. For identification we examine how US firms' liabilities vis-à-vis banks, non-bank lenders and bond markets evolve after an increase in capital requirements implemented by the European Banking Authority (EBA) in 2011. We find that US firms experience a reduction in credit lines but not in term loans from EU banks. In addition, US firms are able to compensate for the reduction in credit lines from EU banks by securing liquidity facilities from US non-bank financial institutions, without increasing borrowing from corporate bond markets. These results suggest that diversified domestic loan markets, with both banks and non-bank financial institutions providing loans to corporations, can help overcome cuts in cross-border bank funding.
    Keywords: credit lines, term loans, bank capital requirements, firm-level data, non-bank financial intermediaries
    JEL: G21 G32 F32 F34
    Date: 2020–04
  18. By: Luigi Bocola; Guido Lorenzoni
    Abstract: Financial crises typically arise because firms and financial institutions choose balance sheets that expose them to aggregate risk. We propose a theory to explain these risk exposures. We study a financial accelerator model where entrepreneurs can issue state-contingent claims to consumers. Even though entrepreneurs could use these contingent claims to hedge negative shocks, we show that they tend not to do so. This is because it is costly to buy insurance against these shocks as consumers are also harmed by them. This effect is self-reinforcing, as the fact that entrepreneurs are unhedged amplifies the negative effects of shocks on consumers’ incomes. We show that this feedback can be quantitatively important and lead to inefficiently high risk exposure for entrepreneurs.
    JEL: E44 G01 G11
    Date: 2020–04
  19. By: Michel Beine; Gary Charness; Arnaud Dupuy; Majlinda Joxhe
    Abstract: We conduct a survey and incentivized lab-in-the-field experimental tasks in Tirana, Albania. While the original purpose of our study was to examine whether and how deep parameters such as time and risk preferences affect the intention to migrate, our study was transformed into a natural experiment owing to two large earthquakes that shook the Tirana area during our data-collection period. These events provide us with a rare opportunity to gather evidence (including a pre-earthquake control) on the effect of natural disasters on time and risk preferences. We find unambiguous effects towards more risk aversion and impatience for affected individuals. Moreover, as it turns out, the second earthquake amplified the effect of the first one, suggesting that experiences cumulate in their influence on these preferences.
    Keywords: time preferences, risk preferences, natural disaster, Albania, migration
    JEL: B49 C90 D91 F22
    Date: 2020
  20. By: Martínelli Lasheras, Pablo; Federico, Giovanni
    Abstract: This paper investigates the causes of the traditional practice of intercropping &- i.e., of scattering vines across fields rather than concentrating them in specialized vineyards. We interpret it as a risk management strategy based on spatial diversification, which entailed transportation costs. We test our model with data for 1930s Italy, where intercropping was widely but unevenly diffused. We show that its adoption was positively related to the pattern of scattered dwellings which dated back to the late Middle Ages and reduced transportation costs to individual plots.
    Keywords: Traditional Agriculture; Risk Management; Diversification; Intercropping
    JEL: R14 Q12 O13 N64 N63 L23
    Date: 2020–04–28
  21. By: Christine Laudenbach; Benjamin Loos; Jenny Pirschel; Johannes Wohlfart
    Abstract: We use data from a German online brokerage and a survey to show that retail investors sharply reduce risk-taking in response to nearby firm bankruptcies, which are not predictive of returns. The effects on trading are spatially highly concentrated, immediate and not persistent. They seem to operate through more pessimistic expected returns and increased risk aversion and do not reflect wealth effects or changes in background risks. Investors learn about bankruptcies through immediate coverage in local newspapers. Our findings suggest that non-informative local experiences that make downside risks of stock investment more salient contribute to idiosyncratic short-term fluctuations in trading.
    Keywords: individual investors, risk-taking, trading, experiences
    JEL: D14 G11
    Date: 2020
  22. By: Syed Jawad Hussain Shahzad (Montpellier Business School, Montpellier, France; South Ural State University, Chelyabinsk, Russian Federation); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: Using high-frequency (daily) data on macroeconomic uncertainties and the partial cross-quantilogram approach, we examine the directional predictability of disentangled oil-price-shocks for the entire conditional distribution of uncertainties of five advanced economies (Canada, Euro Area, Japan, the United Kingdom, and the United States). Our results show that oil-demand, supply, and financial risk-related shocks can predict the future path of uncertainty; however, the predictive relationship is contingent on the initial level of macroeconomic uncertainty and the size of the shocks. Our results suggest that macroeconomic uncertainty is indeed predictable at high frequency, and that oil-price-shocks capture valuable predictive information regarding the future path of macroeconomic uncertainties.
    Keywords: Oil shocks, uncertainty, partial cross-quantilograms, directional predictability, developed economies
    JEL: C22 C32 Q41
    Date: 2020–04
  23. By: Pascal J. Maenhout; Andrea Vedolin; Hao Xing
    Abstract: This paper develops a theory of dynamic pessimism and its impact on asset prices. Notions of time-varying pessimism arise endogenously in our setting as a consequence of agents’ concern for model misspecification. We generalize the robust control approach of Hansen and Sargent (2001) by replacing relative entropy as a measure of discrepancy between models by the more general family of Cressie-Read discrepancies. As a consequence, the decision-maker’s distorted beliefs appear as an endogenous state variable driving risk aversion, portfolio decisions, and equilibrium asset prices. Using survey data, we estimate time-varying pessimism and find that such a proxy features a strong business cycle component. We then show that using our measure of pessimism helps match salient features in equity markets such as excess volatility and high equity premium.
    JEL: G11
    Date: 2020–04
  24. By: Benjamin Avanzi; Gregory Clive Taylor; Bernard Wong; Xinda Yang
    Abstract: In this paper, we focus on estimating ultimate claim counts in multiple insurance processes and thus extend the associated literature of micro-level stochastic reserving models to the multivariate context. Specifically, we develop a multivariate Cox process to model the joint arrival process of insurance claims in multiple Lines of Business. The dependency structure is introduced via multivariate shot noise intensity processes which are connected with the help of L\'evy copulas. Such a construction is more parsimonious and tractable in higher dimensions than plain vanilla common shock models. We also consider practical implementation and explicitly incorporate known covariates, such as seasonality patterns and trends, which may explain some of the relationship between two insurance processes (or at least help tease out those relationships). We develop a filtering algorithm based on the reversible-jump Markov Chain Monte Carlo (RJMCMC) method to estimate the unobservable stochastic intensities. Model calibration is illustrated using real data from the AUSI data set.
    Date: 2020–04
  25. By: Sirio Aramonte; Fernando Avalos
    Abstract: Amid widespread sell-offs in risky asset classes, corporate bond exchange-traded funds (ETFs) traded at steep discounts to underlying asset values in March. Contributing factors were high market volatility, reduced risk-taking by dealers and investors' reaction to policy decisions. Policy interventions that improve market functioning in a given sector can have temporary yet important spillovers to other segments through portfolio rebalancing by investors.
    Date: 2020–04–14
  26. By: Jean-Baptiste Hasse (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Quentin Lajaunie (LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - CNRS - Centre National de la Recherche Scientifique)
    Abstract: In this paper, we reexamine the predictive power of the yield spread across countries and over time. Using a dynamic panel/dichotomous model framework and a unique dataset covering 13 OECD countries over a period of 45 years, we empirically show that the yield spread signals recessions. This result is robust to different econometric specifications, controlling for recession risk factors and time sampling. Using a new cluster analysis methodology, we present empirical evidence of a partial homogeneity of the predictive power of the yield spread. Our results provide a valuable framework for monitoring economic cycles.
    Keywords: Yield Spread,Recession,Panel Binary Model,Cluster Analysis
    Date: 2020–04
  27. By: Bakas, Dimitrios; Triantafyllou, Athanasios
    Abstract: In this paper, we empirically investigate the impact of pandemics on commodity price volatility. In specific, we explore the impact of economic uncertainty related to global pandemics on the volatility of the S&P GSCI commodity index as well as on the sub-indexes of crude oil and gold. The results show that uncertainty related to pandemics have a strong negative impact on the volatility of commodity markets and especially on crude oil market, while the effect on gold market is positive but less significant. Our findings remain robust to a series of robustness checks.
    Keywords: Pandemics, Commodity Markets, Economic Uncertainty, Volatility
    Date: 2020–04–24
  28. By: Bakas, Dimitrios; Ioakimidis, Marilou; Triantafyllou, Athanasios
    Abstract: In this paper we examine the impact of commodity price uncertainty on US economic activity. Our empirical analysis indicates that uncertainty in agricultural, energy and metals markets depresses US economic activity and acts as an early warning signal for US recessions. Our VAR analysis shows that uncertainty shocks in agricultural and metals markets have a more long-lasting dampening effect on US economic activity and its components, when compared to the effect of oil price uncertainty shocks. Finally, we show that when accounting for the effects of macroeconomic and monetary factors, the negative dynamic response of economic activity to agricultural and metals price uncertainty shocks remains unaltered, while the respective macroeconomic response to energy uncertainty shocks is significantly reduced due to either systematic policy reactions or random shocks in monetary policy.
    Keywords: Volatility, Commodity Markets, Economic Recession, Economic Activity
    Date: 2020–04–24
  29. By: Vadlamani Ravi; Vadlamani Madhav
    Abstract: It is well-known that disciplines such as mechanical engineering, electrical engineering, civil engineering, aerospace engineering, chemical engineering and software engineering witnessed successful applications of reliability engineering concepts. However, the concept of reliability in its strict sense is missing in financial services. Therefore, in order to fill this gap, in a first-of-its-kind-study, we define the reliability of a bank/firm in terms of the financial ratios connoting the financial health of the bank to withstand the likelihood of insolvency or bankruptcy. For the purpose of estimating the reliability of a bank, we invoke a statistical and machine learning algorithm namely, logistic regression (LR). Once, the parameters are estimated in the 1st stage, we fix them and treat the financial ratios as decision variables. Thus, in the 1st stage, we accomplish the hitherto unknown way of estimating the reliability of a bank. Subsequently, in the 2nd stage, in order to maximize the reliability of the bank, we formulate an unconstrained optimization problem in a single-objective environment and solve it using the well-known particle swarm optimization (PSO) algorithm. Thus, in essence, these two stages correspond to predictive and prescriptive analytics respectively. The proposed 2-stage strategy of using them in tandem is beneficial to the decision-makers within a bank who can try to achieve the optimal or near-optimal values of the financial ratios in order to maximize the reliability which is tantamount to safeguarding their bank against solvency or bankruptcy.
    Date: 2020–03
  30. By: Christophe Croux; Julapa Jagtiani; Tarunsai Korivi; Milos Vulanovic
    Abstract: This study examines key default determinants of fintech loans, using loan-level data from the LendingClub consumer platform during 2007–2018. We identify a robust set of contractual loan characteristics, borrower characteristics, and macroeconomic variables that are important in determining default. We find an important role of alternative data in determining loan default, even after controlling for the obvious risk characteristics and the local economic factors. The results are robust to different empirical approaches. We also find that homeownership and occupation are important factors in determining default. Lenders, however, are required to demonstrate that these factors do not result in any unfair credit decisions. In addition, we find that personal loans used for medical financing or small business financing are more risky than other personal loans, holding the same characteristics of the borrowers. Government support through various public-private programs could potentially make funding more accessible to those in need of medical services and small businesses without imposing excessive risk to small peer-to-peer (P2P) investors.
    Keywords: crowdfunding; lasso selection methods; peer-to-peer lending; household finance; machine learning; financial innovation; big data; P2P/marketplace lending
    JEL: G21 D14 D10 G29 G20
    Date: 2020–04–16

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