nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒11‒22
twelve papers chosen by
Stan Miles
Thompson Rivers University

  1. Risk and return prediction for pricing portfolios of non-performing consumer credit By Siyi Wang; Xing Yan; Bangqi Zheng; Hu Wang; Wangli Xu; Nanbo Peng; Qi Wu
  2. Modeling ex-ante risk premia in the oil market By Remzi Uctum; Georges Prat
  3. Heterogenous criticality in high frequency finance: a phase transition in flash crashes By Jeremy Turiel; Tomaso Aste
  4. Crypto-exchanges and Credit Risk: Modelling and Forecasting the Probability of Closure By Fantazzini, Dean; Calabrese, Raffaella
  5. The USS Trustee's risky strategy By Neil M Davies; Jackie Grant; Chin Yang Shapland
  6. Growth at Risk from Natural Disasters By Sibabrata Das; Mr. Saad N Quayyum; Mr. Tamim Bayoumi
  7. Conventional and Unconventional Monetary Policy Rate Uncertainty and Stock Market Volatility: A Forecasting Perspective By Ruipeng Liu; Rangan Gupta; Elie Bouri
  8. Are Bank Bailouts Welfare Improving? By Shukayev, Malik; Ueberfeldt, Alexander
  9. A Pandemic Forecasting Framework: An Application of Risk Analysis By Allan Dizioli; Aneta Radzikowski; Daniel Rivera Greenwood
  10. Reputational risks in banks: A review of research themes, frameworks, methods, and future research directions By Adeabah, David; Andoh, Charles; Asongu, Simplice; Gemegah, Albert
  11. Updated Methodology for Assigning Credit Ratings to Sovereigns By Karim McDaniels; Nico Palesch; Sanjam Suri; Zacharie Quiviger; John Walsh
  12. Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data By M. Eren Akbiyik; Mert Erkul; Killian Kaempf; Vaiva Vasiliauskaite; Nino Antulov-Fantulin

  1. By: Siyi Wang; Xing Yan; Bangqi Zheng; Hu Wang; Wangli Xu; Nanbo Peng; Qi Wu
    Abstract: We design a system for risk-analyzing and pricing portfolios of non-performing consumer credit loans. The rapid development of credit lending business for consumers heightens the need for trading portfolios formed by overdue loans as a manner of risk transferring. However, the problem is nontrivial technically and related research is absent. We tackle the challenge by building a bottom-up architecture, in which we model the distribution of every single loan's repayment rate, followed by modeling the distribution of the portfolio's overall repayment rate. To address the technical issues encountered, we adopt the approaches of simultaneous quantile regression, R-copula, and Gaussian one-factor copula model. To our best knowledge, this is the first study that successfully adopts a bottom-up system for analyzing credit portfolio risks of consumer loans. We conduct experiments on a vast amount of data and prove that our methodology can be applied successfully in real business tasks.
    Date: 2021–10
  2. By: Remzi Uctum; Georges Prat
    Abstract: Using Consensus Economics survey-based data we show that oil price expectations are not rational, implying that the ex-ante premium is a more relevant concept than the widely popular ex-post premium. We propose for the 3- and 12-month horizons a portfolio choice model with risky oil assets and a risk-free asset. At the maximized expected utility the risk premium is defined as the product of the risk price by the expected oil return volatility. We show that the representative investor can be risk averse or risk seeking depending on the state of nature, implying that the price of risk is positive or negative, respectively. The price of risk and expected volatility being unobservable magnitudes, a state-space model, where the risk prices are represented as stochastic unobservable components and where expected volatilities depend on historical squared returns, is estimated using Kalman filtering. We find evidence of significant disparities of risk prices according to horizons: higher amplitudes and risk seeking behaviour are associated with short horizons and lower fluctuations and risk aversion attitude characterize longer horizons. We show that macroeconomic, financial and oil market-related factors drive risk prices whose signs are consistent with the predictions of prospect theory. An upward sloped term structure of oil risk premia prevails in average over the period.
    Keywords: oil market, oil price expectations, ex-ante risk premium
    JEL: D81 G11 Q43
    Date: 2021
  3. By: Jeremy Turiel; Tomaso Aste
    Abstract: Flash crashes in financial markets have become increasingly important attracting attention from financial regulators, market makers as well as from the media and the broader audience. Systemic risk and propagation of shocks in financial markets is also a topic or great relevance who has attracted increasing attention in recent years. In the present work we bridge the gap between these two topics with an in-depth investigation of the systemic risk structure of co-crashes in high frequency trading. We find that large co-crashes are systemic in their nature and differ from small crashes. We demonstrate that there is a phase transition between co-crashes of small and large sizes, where the former involves mostly illiquid stocks while large and liquid stocks are the most represented and central in the latter. This suggest that systemic effects and shock propagation might be triggered by simultaneous withdrawn or movement of liquidity by HFTs and market makers having cross-asset.
    Date: 2021–10
  4. By: Fantazzini, Dean; Calabrese, Raffaella
    Abstract: While there is an increasing interest in crypto-assets, the credit risk of these exchanges is still relatively unexplored. To fill this gap, we consider a unique data set on 144 exchanges active from the first quarter of 2018 to the first quarter of 2021. We analyze the determinants of the decision of closing an exchange using credit scoring and machine learning techniques. The cybersecurity grades, having a public developer team, the age of the exchange, and the number of available traded cryptocurrencies are the main significant covariates across different model specifications. Both in-sample and out-of-sample analyses confirm these findings. These results are robust to the inclusion of additional variables considering the country of registration of these exchanges and whether they are centralized or decentralized.
    Keywords: Exchange; Bitcoin; Crypto-assets; Crypto-currencies; Credit risk; Bankruptcy; Default Probability
    JEL: C21 C35 C51 C53 G23 G32 G33
    Date: 2021
  5. By: Neil M Davies; Jackie Grant; Chin Yang Shapland
    Abstract: How much risk, and what types of risk, is the Universities Superannuation Scheme (USS) taking? This is a critical question for universities across the UK and many of their employees. Will the fund have enough money to pay for all our pensions? Will it run out? Or is there a significant risk that we are collectively overpaying? In September 2021, David Miles and James Sefton, from Imperial College Business School, stepped into this vacuum, publishing 'How much risk is the USS taking?'. The paper presents important, accessible and highly readable analysis which estimates how likely the USS is to default over time. Their work is particularly relevant to the current UCU dispute with 69 employers over the benefit cuts that Universities UK (UUK) is planning to implement on the basis of the 2020 USS valuation. In this Brief, we assess the assumptions, replicate the results, explore further their model and consider potential extensions. We demonstrate that for a cautious model with reasonable assumptions for assets and asset growth, the fund has a less than 7% chance of defaulting for the duration that pensions promises are due, but a greater than 80% chance of being over funded by at least {\pounds}100bn, and nearly 50% chance of having over {\pounds}400bn. We offer warm thanks to David Miles and James Sefton for sharing their code and data, for their helpful conversations and clarification. Their analysis is infinitely clearer, better and more credible than anything the USS has produced. We hope this paper will be the beginning of more work in this area. All errors are our own.
    Date: 2021–10
  6. By: Sibabrata Das; Mr. Saad N Quayyum; Mr. Tamim Bayoumi
    Abstract: The paper analyzes the impact of natural disasters on per-capita GDP growth. Using a quantile regressions and growth-at-risk approach, the paper examines the impact of disasters and policy choices on the distribution of growth rather than simply its average. We find that countries that have in place disaster preparedness mechanisms and lower public debt have lower probability of witnessing a significant drop in growth as a consequence of a natural disaster, but our innovative methodology in this paper finds that the two policies are complements since their effectiveness vary across different disaster scenarios. While both are helpful for small to mid-size disasters, lower debt—and hence more fiscal space—is more beneficial in the face of very large disasters. A balanced strategy would thus involve both policies.
    Date: 2021–09–17
  7. By: Ruipeng Liu (Department of Finance, Deakin Business School, Deakin University, Melbourne, VIC 3125, Australia); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (School of Business, Lebanese American University, Lebanon)
    Abstract: Theory suggests the existence of a bi-directional relationship between stock market volatility and monetary policy rate uncertainty. In light of this, we forecast volatilities of equity markets and shadow short rates (SSR) - a common metric of both conventional and unconventional monetary policy decisions, by applying a bivariate Markov-switching multifractal (MSM) model. Using daily data of eight advanced economies (Australia, Canada, Euro area, Japan, New Zealand, Switzerland, the UK, and the US) over the period of January, 1995 to March, 2021, we find that the bivariate MSM model outperforms, in a statistically significant manner, not only the benchmark historical volatility and the univariate MSM models, but also the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) framework, particularly at longer forecast horizons. This finding confirms the bi-directional relationship between stock market volatility and uncertainty surrounding conventional and unconventional monetary policies, which in turn has important implications for academics, investors and policymakers.
    Keywords: Shadow short rate uncertainty, Stock market volatility, Markov-switching multifractal model (MSM), Forecasting
    JEL: C22 C32 C53 D80 E52 G15
    Date: 2021–11
  8. By: Shukayev, Malik (University of Alberta, Department of Economics); Ueberfeldt, Alexander (Bank of Canada)
    Abstract: The financial sector bailouts seen during the Great Recession generated substantial opposition and controversy. We assess the welfare benefits of government-funded emergency support to the financial sector, taking into account its effects on risk-taking incentives. In our quantitative general equilibrium model, the financial crisis probability depends on financial intermediaries' balance sheet choices, influenced by capital adequacy constraints and ex ante known emergency support provisions. These policy tools interact to make financial sector bailouts welfare improving when capital adequacy constraints are consistent with the current Basel III regulation, but potentially welfare decreasing with looser capital adequacy regulation existing before the Great Recession.
    Keywords: fire sales externality; short-term bank funding; endogenous financial crises; bank regulation; bailouts; government guarantees
    JEL: D62 E32 E44 G01
    Date: 2021–11–16
  9. By: Allan Dizioli; Aneta Radzikowski; Daniel Rivera Greenwood
    Abstract: This paper introduces a simple, frequently and easily updated, close to the data epidemiological model that has been used for near-term forecast and policy analysis. We provide several practical examples of how the model has been used. We explain the epidemic development in the UK, the USA and Brazil through the model lens. Moreover, we show how our model would have predicted that a super infectious variant, such as the delta, would spread and argue that current vaccination levels in many countries are not enough to curb other waves of infections in the future. Finally, we briefly discuss the importance of how to model re-infections in epidemiological models.
    Keywords: COVID-19, epidemiology modelling, vaccines impact, virus variants and testing; vaccine hesitancy; vaccination data; Google mobility; virus variant; vaccination assumption; COVID-19; Emerging and frontier financial markets; Aging; Global
    Date: 2021–08–27
  10. By: Adeabah, David; Andoh, Charles; Asongu, Simplice; Gemegah, Albert
    Abstract: Reputation is an important factor for long-term stability, competitiveness, and success of all contemporary organizations. It is even more important for banks because of their systemic role in a modern economy. In this study, we present a review of the current body of literature regarding reputational risks in banks. Using the systematic literature review method, 35 articles published from 2010 to 2020 are reviewed and analyzed. It was found that only developed countries (i.e., the United States and Europe) have been actively contributing to research on reputational risks in banks, suggesting that reputational risks management of banks has not gained the global attention it deserves. Additionally, issues of mitigation of reputational risks are identified as the most frequently studied research theme with a paucity of research on measurement, determinants, and implications of reputational risks at both micro and macro levels. Furthermore, it was noticed that reputational risk management frameworks are still underdeveloped. In theory, this review should help with a strong conceptualization of reputational risks management in banks and guide further research.
    Keywords: reputational risks; banks; systematic literature review
    JEL: G0 G2 G3
    Date: 2021–01
  11. By: Karim McDaniels; Nico Palesch; Sanjam Suri; Zacharie Quiviger; John Walsh
    Abstract: The investment of foreign exchange reserves or other asset portfolios requires an assessment of the credit quality of investment counterparties. Traditionally, foreign exchange reserve and other asset managers relied on credit rating agencies (CRAs) as the main source of information for credit assessments. In October 2010, the Financial Stability Board issued principles to reduce reliance on CRA ratings in standards, laws and regulations, in support of financial stability. Moreover, best practices in the asset management industry suggest that investors should understand the credit risks they are exposed to and, more broadly, that they should rely on internal credit assessments to inform investment decisions. In support of these objectives, the Bank of Canada first published its sovereign rating methodology in 2017. It provided a detailed technical description of the process developed to assign internal credit ratings to sovereigns, using only publicly available data. This publication updates the internal sovereign rating methodology to stay abreast of evolving best practices and leverage internal experience. This updated methodology proposes three key innovations: (i) a new approach to assessing a sovereign’s fiscal position, (ii) adjustments to the approach to assessing monetary policy flexibility and (iii) the explicit consideration of climate-related factors.
    Keywords: Credit risk management; Foreign reserves management
    JEL: G28 G32 F31
    Date: 2021–11
  12. By: M. Eren Akbiyik; Mert Erkul; Killian Kaempf; Vaiva Vasiliauskaite; Nino Antulov-Fantulin
    Abstract: Understanding the variations in trading price (volatility), and its response to external information is a well-studied topic in finance. In this study, we focus on volatility predictions for a relatively new asset class of cryptocurrencies (in particular, Bitcoin) using deep learning representations of public social media data from Twitter. For the field work, we extracted semantic information and user interaction statistics from over 30 million Bitcoin-related tweets, in conjunction with 15-minute intraday price data over a 144-day horizon. Using this data, we built several deep learning architectures that utilized a combination of the gathered information. For all architectures, we conducted ablation studies to assess the influence of each component and feature set in our model. We found statistical evidences for the hypotheses that: (i) temporal convolutional networks perform significantly better than both autoregressive and other deep learning-based models in the literature, and (ii) the tweet author meta-information, even detached from the tweet itself, is a better predictor than the semantic content and tweet volume statistics.
    Date: 2021–10

This nep-rmg issue is ©2021 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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