nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒09‒11
eighteen papers chosen by
Stan Miles, Thompson Rivers University

  1. A Case of Risk Management Control and Its Study in Non-Financial Risks By Majid, Hassan
  2. Which is Worse: Heavy Tails or Volatility Clusters? By Joshua Traut; Wolfgang Schadner
  3. A Novel Credit Model Risk Measure: does more data lead to lower model risk in credit scoring models? By Valter T. Yoshida Jr; Alan de Genaro; Rafael Schiozer; Toni R. E. dos Santos
  4. Growth-at-Risk is Investment-at-Risk By Aaron Amburgey; Michael W. McCracken
  5. The impact of financial shocks on the forecast distribution of output and inflation By Mario Forni; Luca Gambetti; Nicolò Maffei-Faccioli; Luca Sala
  6. Crisis Risk and Risk Management By Stulz, Rene M.
  7. Path Shadowing Monte-Carlo By Rudy Morel; St\'ephane Mallat; Jean-Philippe Bouchaud
  8. Systematic Default and Return Predictability in the Stock and Bond Markets By Bao, Jack; Hou, Kewei; Zhang, Shaojun
  9. Incident-Specific Cyber Insurance By Wing Fung Chong; Daniel Linders; Zhiyu Quan; Linfeng Zhang
  10. Bayesian framework for characterizing cryptocurrency market dynamics, structural dependency, and volatility using potential field By Anoop C V; Neeraj Negi; Anup Aprem
  11. Impact of Covid-19 on stock market volatility-A Bangladesh Perspective By Shaturaev, Jakhongir
  12. DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data By Yancheng Liang; Jiajie Zhang; Hui Li; Xiaochen Liu; Yi Hu; Yong Wu; Jinyao Zhang; Yongyan Liu; Yi Wu
  13. An optimal transport approach for the multiple quantile hedging problem By Cyril B\'en\'ezet; Jean-Fran\c{c}ois Chassagneux; Mohan Yang
  14. Forecasting oil prices with penalized regressions, variance risk premia and Google data By Fantazzini, Dean; Kurbatskii, Alexey; Mironenkov, Alexey; Lycheva, Maria
  15. Deep Learning from Implied Volatility Surfaces By Bryan T. Kelly; Boris Kuznetsov; Semyon Malamud; Teng Andrea Xu
  16. Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects By Chao Zhang; Xingyue Pu; Mihai Cucuringu; Xiaowen Dong
  17. Product Liability: Detecting Potential Risks in New Products By Andrea Castellano; Gustavo Ferro; Maximiliano Miranda Zanetti
  18. Bankruptcy in groups By Beaver, William H; Cascino, Stefano; Correia, Maria; McNichols, Maureen F.

  1. By: Majid, Hassan
    Abstract: A Case of Risk Management Control and Its Study in Non-Financial Risks
    Date: 2023–08–04
  2. By: Joshua Traut (University of St. Gallen); Wolfgang Schadner (University of St. Gallen)
    Abstract: Heavy tails and volatility clusters are both stylized facts of financial returns that destabilize markets and are often neglected using the simplifying assumptions of normally distributed and iid returns respectively. This work disentangles the two sources and is the first to assess which one does the greater damage to financial stability and whether the threat can be reduced via diversification. As such, it also quantifies the potential shortfalls of the two commonly used simplifying assumptions. The analysis is carried out for index return series representing seven different asset classes and for individual stock portfolios. The stylized facts are isolated using recent developments in surrogate analysis (IAAFT, IAAWT). Our analysis shows that volatility clusters have a greater impact on maximum drawdowns and aggregate losses across all markets and that diversification does not yield any protection from those risks. In fact, diversification amplifies the translation of the two stylized facts into drawdowns, exacerbating their potential negative effects. We further demonstrate the practical relevance of our findings as we can replicate the results of our surrogate analysis using real portfolios. Moreover, we show that regulators should consider the impact of volatility clusters and discard the simplifying assumption of iid returns in order to enhance the accuracy of capital buffers.
    Keywords: Financial Stability, Tail Risk, Autocorrelation, Volatility Clustering, Heavy Tails, Risk Management
    JEL: G12 G18 G15 G01
    Date: 2023–08
  3. By: Valter T. Yoshida Jr; Alan de Genaro; Rafael Schiozer; Toni R. E. dos Santos
    Abstract: Large databases and Machine Learning have increased our ability to produce models with a different number of observations and explanatory variables. The credit scoring literature has focused on the optimization of classifications. Little attention has been paid to the inadequate use of models. This study fills this gap by focusing on model risk. It proposes a measure to assess credit scoring model risk. Its emphasis is on model misuse. The proposed model risk measure is ordinal, and it applies to many settings and types of loan portfolios, allowing comparisons of different specifications and situations (as in-sample or out-of-sample data). It allows practitioners and regulators to evaluate and compare different credit risk models in terms of model risk. We empirically test our measure in plugin LASSO default models and find that adding loans from different banks to increase the number of observations is not optimal, challenging the generally accepted assumption that more data leads to better predictions.
    Date: 2023–08
  4. By: Aaron Amburgey; Michael W. McCracken
    Abstract: We investigate the role financial conditions play in the composition of U.S. growth-at-risk. We document that, by a wide margin, growth-at-risk is investment-at-risk. That is, if financial conditions indicate U.S. real GDP growth will be in the lower tail of its conditional distribution, we know that the main contributor is a decline in investment. Consumption contributes under extreme financial stress. Government spending and net exports do not play a role.
    Keywords: growth-at-risk; real-time data; quantiles; expected shortfall
    JEL: C12 C32 C38 C52
    Date: 2023–08–21
  5. By: Mario Forni; Luca Gambetti; Nicolò Maffei-Faccioli; Luca Sala
    Abstract: Financial shocks represent a major driver of fluctuations in tail risk, defined as the 5th percentile of the forecast distributions of output and inflation. Since the variance and the asymmetry of the forecast distributions are largely driven by the left tail, financial shocks turn out to play a prominent role for distribution dynamics. Monetary policy shocks also play a role in shaping risk, although its effects are smaller than those of financial shocks. These findings are obtained using a novel econometric approach which combines quantile regressions and Structural VARs.
    Keywords: Tail Risk, Uncertainty, Skewness, Forecast Distribution, SVAR, Financial shocks, Monetary Policy Shocks, Quantile Regressions
    JEL: C32 E32
    Date: 2023–03
  6. By: Stulz, Rene M. (Ohio State U)
    Abstract: This paper assesses the current state of knowledge about crisis risk and its implications for risk management. Better data that became available since the Global Financial Crisis (GFC) has improved our understanding of crisis risk. These data have been used to show that some types of crises become predictable when one accounts for interactions between risks. Specifically, a financial crisis is much more likely in the years following both high credit growth and high asset valuations. However, some other types of crises do not seem predictable. There is no evidence that the frequency of economic and financial crises is increasing. The existing data show that political crises make economic crises more likely, so that, as suggested by the concept of polycrisis, feedback between non-economic crises and economic crises can be important, but there is no comparable evidence for climate events. Strategies that increase firm operational and financial flexibility appear successful at reducing the adverse impact of crises on firms.
    JEL: G01 G21 G32
    Date: 2023–05
  7. By: Rudy Morel; St\'ephane Mallat; Jean-Philippe Bouchaud
    Abstract: We introduce a Path Shadowing Monte-Carlo method, which provides prediction of future paths, given any generative model. At any given date, it averages future quantities over generated price paths whose past history matches, or `shadows', the actual (observed) history. We test our approach using paths generated from a maximum entropy model of financial prices, based on a recently proposed multi-scale analogue of the standard skewness and kurtosis called `Scattering Spectra'. This model promotes diversity of generated paths while reproducing the main statistical properties of financial prices, including stylized facts on volatility roughness. Our method yields state-of-the-art predictions for future realized volatility and allows one to determine conditional option smiles for the S\&P500 that outperform both the current version of the Path-Dependent Volatility model and the option market itself.
    Date: 2023–08
  8. By: Bao, Jack (U of Delaware); Hou, Kewei (Ohio State U); Zhang, Shaojun (Ohio State U)
    Abstract: We construct a measure of systematic default defined as the probability that many firms default at the same time. We account for correlations in defaults between firms through exposures to common shocks. Systematic default spikes during recessions, is correlated with macroeconomic indicators, and predicts future realized defaults. More importantly, it predicts future equity and corporate bond index returns both in- and out-of-sample. Finally, we find that the cross-section of average stock returns is related to firm-level exposures to systematic default risk.
    JEL: E32 G12 G13 G17
    Date: 2023–05
  9. By: Wing Fung Chong; Daniel Linders; Zhiyu Quan; Linfeng Zhang
    Abstract: In the current market practice, many cyber insurance products offer a coverage bundle for losses arising from various types of incidents, such as data breaches and ransomware attacks, and the coverage for each incident type comes with a separate limit and deductible. Although this gives prospective cyber insurance buyers more flexibility in customizing the coverage and better manages the risk exposures of sellers, it complicates the decision-making process in determining the optimal amount of risks to retain and transfer for both parties. This paper aims to build an economic foundation for these incident-specific cyber insurance products with a focus on how incident-specific indemnities should be designed for achieving Pareto optimality for both the insurance seller and buyer. Real data on cyber incidents is used to illustrate the feasibility of this approach. Several implementation improvement methods for practicality are also discussed.
    Date: 2023–08
  10. By: Anoop C V; Neeraj Negi; Anup Aprem
    Abstract: Identifying the structural dependence between the cryptocurrencies and predicting market trend are fundamental for effective portfolio management in cryptocurrency trading. In this paper, we present a unified Bayesian framework based on potential field theory and Gaussian Process to characterize the structural dependency of various cryptocurrencies, using historic price information. The following are our significant contributions: (i) Proposed a novel model for cryptocurrency price movements as a trajectory of a dynamical system governed by a time-varying non-linear potential field. (ii) Validated the existence of the non-linear potential function in cryptocurrency market through Lyapunov stability analysis. (iii) Developed a Bayesian framework for inferring the non-linear potential function from observed cryptocurrency prices. (iv) Proposed that attractors and repellers inferred from the potential field are reliable cryptocurrency market indicators, surpassing existing attributes, such as, mean, open price or close price of an observation window, in the literature. (v) Analysis of cryptocurrency market during various Bitcoin crash durations from April 2017 to November 2021, shows that attractors captured the market trend, volatility, and correlation. In addition, attractors aids explainability and visualization. (vi) The structural dependence inferred by the proposed approach was found to be consistent with results obtained using the popular wavelet coherence approach. (vii) The proposed market indicators (attractors and repellers) can be used to improve the prediction performance of state-of-art deep learning price prediction models. As, an example, we show improvement in Litecoin price prediction up to a horizon of 12 days.
    Date: 2023–08
  11. By: Shaturaev, Jakhongir
    Abstract: This study aims to measure the impact of Covid-19 on the volatility of stock market in Bangladesh. It considers daily data of Dhaka Stock Exchange Broad Index (DSEX) as dependent variable and growth rate of exchange rate and Brent oil price as independent variables while death case due to coronavirus as Covid-19 proxy. This study observes significantly negative impact of Covid-19 on the volatility. It is expected that the recommendations of this study may ensure a stable and vibrant capital market to enhance the economic progress of Bangladesh.
    Keywords: Covid-19, volatility, stock market, GARCH
    JEL: G0 H0 K0 M0
    Date: 2023–02–15
  12. By: Yancheng Liang; Jiajie Zhang; Hui Li; Xiaochen Liu; Yi Hu; Yong Wu; Jinyao Zhang; Yongyan Liu; Yi Wu
    Abstract: Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient boosting and random forest methods. Different from images or languages, real-world financial data are high-dimensional, sparse, noisy and extremely imbalanced, which makes deep neural network models particularly challenging to train and fragile in practice. In this work, we propose DeRisk, an effective deep learning risk prediction framework for credit risk prediction on real-world financial data. DeRisk is the first deep risk prediction model that outperforms statistical learning approaches deployed in our company's production system. We also perform extensive ablation studies on our method to present the most critical factors for the empirical success of DeRisk.
    Date: 2023–08
  13. By: Cyril B\'en\'ezet (ENSIIE, LaMME); Jean-Fran\c{c}ois Chassagneux (LPSM); Mohan Yang (ADIA)
    Abstract: We consider the multiple quantile hedging problem, which is a class of partial hedging problems containing as special examples the quantile hedging problem (F{\"o}llmer \& Leukert 1999) and the PnL matching problem (introduced in Bouchard \& Vu 2012). In complete non-linear markets, we show that the problem can be reformulated as a kind of Monge optimal transport problem. Using this observation, we introduce a Kantorovitch version of the problem and prove that the value of both problems coincide. In the linear case, we thus obtain that the multiple quantile hedging problem can be seen as a semi-discrete optimal transport problem, for which we further introduce the dual problem. We then prove that there is no duality gap, allowing us to design a numerical method based on SGA algorithms to compute the multiple quantile hedging price.
    Date: 2023–08
  14. By: Fantazzini, Dean; Kurbatskii, Alexey; Mironenkov, Alexey; Lycheva, Maria
    Abstract: This paper investigates whether augmenting models with the variance risk premium (VRP) and Google search data improves the quality of the forecasts for real oil prices. We considered a time sample of monthly data from 2007 to 2019 that includes several episodes of high volatility in the oil market. Our evidence shows that penalized regressions provided the best forecasting performances across most of the forecasting horizons. Moreover, we found that models using the VRP as an additional predictor performed best for forecasts up to 6-12 months ahead forecasts, while models using Google data as an additional predictor performed better for longer-term forecasts up to 12-24 months ahead. However, we found that the differences in forecasting performances were not statistically different for most models, and only the Principal Component Regression (PCR) and the Partial least squares (PLS) regression were consistently excluded from the set of best forecasting models. These results also held after a set of robustness checks that considered model specifications using a wider set of influential variables, a Hierarchical Vector Auto-Regression model estimated with the LASSO, and a set of forecasting models using a simplified specification for Google Trends data.
    Keywords: Oil price; Variance Risk Premium; Google Trends; VAR; LASSO; Ridge; Elastic Net; Principal compo-nents, Partial least squares
    JEL: C22 C32 C52 C53 C55 C58 G17 O13 Q47
    Date: 2022
  15. By: Bryan T. Kelly (Yale School of Management; AQR Capital Management; NBER); Boris Kuznetsov (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Semyon Malamud (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute; and CEPR); Teng Andrea Xu (Ecole Polytechnique Fédérale de Lausanne)
    Abstract: We develop a novel methodology for extracting information from option implied volatility (IV) surfaces for the cross-section of stock returns, using image recognition techniques from machine learning (ML). The predictive information we identify is essentially uncorrelated with most of the existing option-implied characteristics, delivers a higher Sharpe ratio, and has a significant alpha relative to a battery of standard and option-implied factors. We show the virtue of ensemble complexity: Best results are achieved with a large ensemble of ML models, with the out-of-sample performance increasing in the ensemble size, saturating when the number of model parameters significantly exceeds the number of observations. We introduce principal linear features, an analog of principal components for ML and use them to show IV feature complexity: A low-rank rotation of the IV surface cannot explain the model performance. Our results are robust to short-sale constraints and transaction costs.
    Date: 2023–08
  16. By: Chao Zhang; Xingyue Pu; Mihai Cucuringu; Xiaowen Dong
    Abstract: We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks. The proposed model offers the benefits of incorporating spillover effects from multi-hop neighbors, capturing nonlinear relationships, and flexible training with different loss functions. Our empirical findings provide compelling evidence that incorporating spillover effects from multi-hop neighbors alone does not yield a clear advantage in terms of predictive accuracy. However, modeling nonlinear spillover effects enhances the forecasting accuracy of realized volatilities, particularly for short-term horizons of up to one week. Moreover, our results consistently indicate that training with the Quasi-likelihood loss leads to substantial improvements in model performance compared to the commonly-used mean squared error. A comprehensive series of empirical evaluations in alternative settings confirm the robustness of our results.
    Date: 2023–08
  17. By: Andrea Castellano; Gustavo Ferro; Maximiliano Miranda Zanetti
    Abstract: The central hypothesis of this article is that liability regulation can foster firms’ incentives to study the (potential) dangers of their products. We discuss alternative views and develop a formal model to analyze a firm´s incentive structure under the application of hindsight liability. We find a new role for liability regulation: to foster voluntary investment in research aimed at detecting potential risks in new products. The model allows us to analyze the firm´s investment decisions in research under different scenarios, each of which has varying expected costs. We offer some alternatives for institutional design seeking incentive compatibility with the aim proposed.
    Keywords: risk, regulation, product liability, incentives, asymmetric information
    JEL: K12 K22
    Date: 2023–08
  18. By: Beaver, William H; Cascino, Stefano; Correia, Maria; McNichols, Maureen F.
    Abstract: We examine bankruptcy within business groups. Groups have incentives to support financially distressed subsidiaries, as the bankruptcy of a subsidiary may impose severe costs on the group as a whole. This is in part because, in several countries, bankruptcy courts often “pierce the corporate veil” and hold groups liable for their distressed subsidiaries’ obligations as if they were their own. Using a large cross-country sample of group-affiliated firms, we show that, by reallocating resources within the corporate structure, business groups actively manage intra-group credit risk to prevent costly within-group insolvencies. Moreover, we document that recent regulatory changes in the approval and disclosure of related party transactions are costly for business groups in that they constrain their ability to shield their subsidiaries from credit-risk shocks. Our study informs the current regulatory debate on related party transactions by highlighting an important cost of anti-self-dealing regulation.
    Keywords: bankruptcy; credit risk; business groups; subsidiaries; veil piercing; related party transactions; regulation; Springer deal
    JEL: G14 G15 G38 M41
    Date: 2023–07–26

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