nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒01‒09
27 papers chosen by
Stan Miles
Thompson Rivers University

  1. Estimation of Systemic Shortfall Risk Measure using Stochastic Algorithms By Sarah Kaakai; Anis Matoussi; Achraf Tamtalini
  2. Risk measurement of joint risk of portfolios: a liquidity shortfall aspect By Shuo Gong; Yijun Hu; Linxiao Wei
  3. A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications By Yu Zhao; Huaming Du
  4. Event management - Beyond Risk Management, Crisis Management & Business Continuity By Raphael de Vittoris
  5. The Choice of GARCH Models to Forecast Value-at-Risk for Currencies (Euro Exchange Rates), Crypto Assets (Bitcoin and Ethereum), Gold, Silver and Crude Oil: Automated Processes, Statistical Distribution Models and the Specification of the Mean Equationn By Andreas Marcus Gohs
  6. Intergenerational Sharing of Unhedgeable Inflation Risk By Damiaan Chen; Roel Beetsma; Sweder van Wijnbergen
  7. Measuring adequately the benefit of diversification in the extreme quantiles: An inquiry into covariation on the brink of catastrophe By Pierre-Charles Pradier; Guillaume Rideau; Sakina Rrguiti
  9. Bank risk-taking and monetary policy transmission: Evidence from China By Li, Xiaoming; Liu, Zheng; Peng, Yuchao; Xu, Zhiwei
  10. Revisiting SME default predictors: The Omega Score By Altman, Edward I.; Balzano, Marco; Giannozzi, Alessandro; Srhoj, Stjepan
  11. ESG Factors and Firms’ Credit Risk By Laura Bonacorsi; Vittoria Cerasi; Paola Galfrascoli; Matteo Manera
  12. Understanding stock market instability via graph auto-encoders By Dragos Gorduza; Xiaowen Dong; Stefan Zohren
  13. Does a financial crisis change a bank's exposure to risk? A difference-in-differences approach By Mäkinen, Mikko
  14. Risk management, expectations and global finance: The case of Deutsche Bank 1970-1990 By Nützenadel, Alexander
  15. Growth at Risk: Forecast Distribution of GDP Growth in Israel By Michael Gurkov; Osnat Zohar
  16. Smooth and Abrupt Dynamics in Financial Volatility: the MS-MEM-MIDAS By L. Scaffidi Domianello; G.M. Gallo; E. Otranto
  17. Deep learning of Value at Risk through generative neural network models : the case of the Variational Auto Encoder By Pierre Brugière; Gabriel Turinici
  18. The Non-monotonic Relationship Between ESG Disclosure and Stock Price Crash Risk By Hui Zhou; Jun Nagayasu
  19. On the Psychology of the Relation between Optimism and Risk Taking By Dohmen, Thomas; Quercia, Simone; Willrodt, Jana
  20. "Amulti-Agent Incomplete Equilibriummodel and Its Applications to Reinsurance Pricing and Life-Cycle Investment" By Keisuke Kizaki; Taiga Saito; Akihiko Takahashi
  21. A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection By Marc Wildi; Branka Hadji Misheva
  22. Sentiment, Mispricing and Excess Volatility in Presence of Institutional Investors By Hervé Roche; Juan Sotes-Paladino
  23. Weak error estimates for rough volatility models By Peter K. Friz; William Salkeld; Thomas Wagenhofer
  24. How do tax technology and controversy expertise affect tax disputes? By Dyck, Daniel; Lorenz, Johannes; Sureth, Caren
  25. Risk Transfer for Multilateral Development Banks: Obstacles and Potential By Galizia, Federico; Perraudin, William; Powell, Andrew; Turner, Timothy
  26. Score-based calibration testing for multivariate forecast distributions By Malte Kn\"uppel; Fabian Kr\"uger; Marc-Oliver Pohle
  27. Narrative Triggers of Information Sensitivity By Kim Ristolainen

  1. By: Sarah Kaakai (LMM - Laboratoire Manceau de Mathématiques - UM - Le Mans Université); Anis Matoussi (LMM - Laboratoire Manceau de Mathématiques - UM - Le Mans Université); Achraf Tamtalini (LMM - Laboratoire Manceau de Mathématiques - UM - Le Mans Université)
    Abstract: Systemic risk measures were introduced to capture the global risk and the corresponding contagion effects that is generated by an interconnected system of financial institutions. To this purpose, two approaches were suggested. In the first one, systemic risk measures can be interpreted as the minimal amount of cash needed to secure a system after aggregating individual risks. In the second approach, systemic risk measures can be interpreted as the minimal amount of cash that secures a system by allocating capital to each single institution before aggregating individual risks. Although the theory behind these risk measures has been well investigated by several authors, the numerical part has been neglected so far. In this paper, we use stochastic algorithms schemes in estimating MSRM and prove that the resulting estimators are consistent and asymptotically normal. We also test numerically the performance of these algorithms on several examples.
    Keywords: Multivariate risk measures, shortfall risk, stochastic algorithms, stochastic root finding, risk allocations
    Date: 2022–11–25
  2. By: Shuo Gong; Yijun Hu; Linxiao Wei
    Abstract: This paper presents a novel axiomatic framework of measuring the joint risk of a portfolio consisting of several financial positions. From the liquidity shortfall aspect, we construct a distortion-type risk measure to measure the joint risk of portfolios, which we referred to as multivariate distortion joint risk measure, representing the liquidity shortfall caused by the joint risk of portfolios. After its fundamental properties have been studied, we axiomatically characterize it by proposing a novel set of axioms. Furthermore, based on the representations for multivariate distortion joint risk measures, we also propose a new class of vector-valued multivariate distortion joint risk measures, as well as with sensible financial interpretation. Their fundamental properties are also investigated. It turns out that this new class is large enough, as it can not only induce new vector-valued multivariate risk measures, but also recover some popular vector-valued multivariate risk measures known in the literature with alternative financial interpretation. Examples are given to illustrate the proposed multivariate distortion joint risk measures. This paper mainly gives some theoretical results, helping one to have an insight look at the measurement of joint risk of portfolios.
    Date: 2022–12
  3. By: Yu Zhao; Huaming Du
    Abstract: Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation.
    Date: 2022–11
  4. By: Raphael de Vittoris (CleRMa - Clermont Recherche Management - ESC Clermont-Ferrand - École Supérieure de Commerce (ESC) - Clermont-Ferrand - UCA - Université Clermont Auvergne)
    Date: 2022–12–01
  5. By: Andreas Marcus Gohs (University of Kassel)
    Abstract: Regular or automated processes require reliable software applications that provide accurate volatility and Value-at-Risk forecasts. The univariate and multivariate GARCH models proposed in the literature are reviewed and the suitability of selected R functions for automated forecasting systems is discussed. With the Markov-switching GARCH function constructed for modelling regime changes, parameter estimates are reliably obtained in studies with moving time windows. In contrast, in the case of structural breaks or outliers, the algorithm of the ordinary GARCH function often does not return valid parameter estimates and fails. VaR prognoses are produced for extreme quantiles (up to 99.9%) and three alternative distribution assumptions (Skew Student-T, Student-T and Gaussian). Accurate one-day-ahead VaR predictions up to the 99% quantile are generally obtained for the time series when Skew Student-T distributed innovations are assumed. The VaR exceedance rates and their percentage deviations from the target alpha as well as the mean and median excess loss are reported. The accompanying mean equation is often omitted when fitting GARCH models to heteroskedastic time series. The impact of this on the accuracy of VaR forecasts is investigated. Coefficients of the ordinary (Pearson) and the default correlation are calculated for moving time windows. Since the calculated default correlation depends on the VaR forecasts, analyses are performed for different quantiles, the ordinary and the MS-GARCH function and specifications of mean equations.
    Keywords: Conditional volatility, Skew Student T, Markov Switching MS-GARCH, Multivariate GARCH, Mean Excess Loss, Default Correlation, Software R
    JEL: G17 F31 G01 G11
    Date: 2022
  6. By: Damiaan Chen; Roel Beetsma; Sweder van Wijnbergen
    Abstract: We explore how members of a collective pension scheme can share inflation risks in the absence of suitable ï¬ nancial market instruments. Using intergenerational risk sharing arrangements, risks can be allocated better across the various participants of a collective pension scheme than would be the case in a strictly individual- or cohort-based pension scheme, as these can only lay off risks via existing ï¬ nancial market instruments. Hence, intergenerational sharing of these risks enhances welfare. In view of the sizes of their funded pension sectors, this would be particularly beneï¬ cial for the Netherlands and the U.K.
    Keywords: pension funds; intergenerational risk sharing; unhedgeable inflation risk; incom- plete markets; welfare loss
    JEL: C61 E21 G11 G23
    Date: 2022–12
  7. By: Pierre-Charles Pradier (Centre d'Economie de la Sorbonne); Guillaume Rideau (Groupe BPCE, Département Risques Participations Non Bancaires); Sakina Rrguiti (Université Paris 1 Panthéon-Sorbonne, Centre d'Economie de la Sorbonne et Groupe BPCE)
    Abstract: The aim of this work is to better understand the nature of covariation in the vicinity of extremes on financial data and assess whether the usual assumptions and covariation measures fits the actual data. For simplicity, we consider pairs of random variables. In order to identify the shape of the covariation all along the distribution, and particularly as the extreme quantiles are approached, we describe the contribution of each of the variables from a random couple to the quantiles of the weighted sum of these variables. This approach makes sense since it can be interpreted in terms of Value-at-Risk in a financial institution: the VaR of the sum of variables may represent the capital requiremet for a diversified conglomerate, while the sum of VaR of the variables would correspond to the capital requirements for the components of the conglomerate, without taking diversification into account. The ratio of these two quantities appears as a good measure of both the benefit of diversification and the decorrelation of variables. We thus compare the values of quantiles and ratio taken from a representative dataset to the values obtained from various simulations relying on the usual assumptions. The result of this comparison is that the usual assumptions do not correctly model the covariation of the real-word data. In particular, the usual assumptions tend to exaggerate the correlation in the vicinity of extreme loss while the benefit of diversification is uniform across distribution. Additional simulations and modelling assumptions may be required to assess the generality of this result
    Keywords: Financial Conglomerates; Diversification; Value-at-Risk; Capital requirements
    JEL: G20 G21 G22 G28
    Date: 2022–11
  8. By: Minasyan Vigen (Russian Presidential Academy of National Economy and Public Administration)
    Abstract: The paper proposes new measures of risk VaR in various degrees, investigates properties, proposes formulas for their calculation and application.
    Keywords: economectrics, theoretical econometrics, VAR-models
    Date: 2021–01
  9. By: Li, Xiaoming; Liu, Zheng; Peng, Yuchao; Xu, Zhiwei
    Abstract: We study the impact of China's 2013 implementation of Basel III on bank risk-taking and its responses to monetary policy shocks using confidential loan-level data from a large Chinese bank. Guided by theory, we use a difference-in-difference identification, exploiting cross-sectional differences in lending behaviors between highrisk and low-risk bank branches before and after the new regulations. We find that, through a risk-weighting channel, changes in regulations significantly reduced bank risktaking, both on average and conditional on monetary policy easing. However, banks reduce risk-taking by increasing lending to ostensibly low-risk state-owned enterprises (SOEs) under government guarantees, despite their low average productivity.
    Keywords: bank risk-taking,banking regulations,risk-weighting,monetary policy,difference-in-difference,China
    JEL: E52 G21 G28
    Date: 2021
  10. By: Altman, Edward I.; Balzano, Marco; Giannozzi, Alessandro; Srhoj, Stjepan
    Abstract: SME default prediction is a long-standing issue in the finance and management literature. Proper estimates of the SME risk of failure can support policymakers in implementing restructuring policies, rating agencies and credit analytics firms in assessing creditworthiness, public and private investors in allocating funds, entrepreneurs in accessing funds, and managers in developing effective strategies. Drawing on the extant management literature, we argue that introducing management- and employee-related variables into SME prediction models can improve their predictive power. To test our hypotheses, we use a unique sample of SMEs and propose a novel and more accurate predictor of SME default, the Omega Score, developed by the Least Absolute Shortage and Shrinkage Operator (LASSO). Results were further confirmed through other machine-learning techniques. Beyond traditional financial ratios and payment behavior variables, our findings show that the incorporation of change in management, employee turnover, and mean employee tenure significantly improve the model's predictive accuracy.
    Keywords: Default prediction modeling,small and medium-sized enterprises,machine learning techniques,LASSO,logit regression
    Date: 2022
  11. By: Laura Bonacorsi (Department of Social and Political Sciences, Bocconi University); Vittoria Cerasi (Italian Court of Auditors and CefES & O-Fire, University of Milano-Bicocca); Paola Galfrascoli (Department of Economics, Management and Statistics and CefES & O-Fire, University of Milano-Bicocca); Matteo Manera (Department of Economics, Management and Statistics, University of Milano-Bicocca and Fondazione Eni Enrico Mattei)
    Abstract: We study the relationship between the risk of default and Environmental, Social and Governance (ESG) factors using Supervised Machine Learning (SML) techniques on a cross-section of European listed companies. Our proxy for credit risk is the z-score originally proposed by Altman (1968). We consider an extensive number of ESG raw factors sourced from the rating provider MSCI as potential explanatory variables. In a first stage we show, using different SML methods such as LASSO and Random Forest, that a selection of ESG factors, in addition to the usual accounting ratios, helps explaining a firm’s probability of default. In a second stage, we measure the impact of the selected variables on the risk of default. Our approach provides a novel perspective to understand which environmental, social responsibility and governance characteristics may reinforce the credit score of individual companies.
    Keywords: Credit risk, Z-scores, ESG factors, Machine learning
    JEL: C5 D4 G3
    Date: 2022–11
  12. By: Dragos Gorduza; Xiaowen Dong; Stefan Zohren
    Abstract: Understanding stock market instability is a key question in financial management as practitioners seek to forecast breakdowns in asset co-movements which expose portfolios to rapid and devastating collapses in value. The structure of these co-movements can be described as a graph where companies are represented by nodes and edges capture correlations between their price movements. Learning a timely indicator of co-movement breakdowns (manifested as modifications in the graph structure) is central in understanding both financial stability and volatility forecasting. We propose to use the edge reconstruction accuracy of a graph auto-encoder (GAE) as an indicator for how spatially homogeneous connections between assets are, which, based on financial network literature, we use as a proxy to infer market volatility. Our experiments on the S&P 500 over the 2015-2022 period show that higher GAE reconstruction error values are correlated with higher volatility. We also show that out-of-sample autoregressive modeling of volatility is improved by the addition of the proposed measure. Our paper contributes to the literature of machine learning in finance particularly in the context of understanding stock market instability.
    Date: 2022–12
  13. By: Mäkinen, Mikko
    Abstract: Can a major financial crisis trigger changes in a bank's risk-taking behavior? Using the 2008 Global Financial Crisis as a quasi-natural experiment and a difference-in-differences approach, I examine whether the worst crisis-hit Russian banks - the banks that have strong incentives to behavior-altering changes - can decrease their post-crisis exposure to risk. A shift in risk-taking behavior by these banks indicates the learning hypothesis. The findings are mixed. The evidence concerning credit risk is inconsistent with the learning hypothesis. On the other hand, the evidence concerning solvency risk is consistent with the learning hypothesis and corroborates evidence from the Nordic countries (Berglund and Mäkinen, 2019). As such, bank learning from a financial crisis may not depend on the institutional context and the level of development of national financial market. Several robustness checks with alternative regression specifications are provided.
    Keywords: financial crisis,bank learning,bank risk,Russian banks
    JEL: G01 G21 G32
    Date: 2021
  14. By: Nützenadel, Alexander
    Abstract: What impact do past experiences have on the expectation formation of banks? This article analyses the risk management of Germany's largest bank during the 1970 and 1980s. In this period, financial deregulation and globalization increased the likelihood of credit defaults and forced banks to implement new strategies of risk assessment. The Herstatt failure of 1974 triggered a series of new regulations, partly based on initiatives of the banks themselves. After the sovereign debt crisis of the 1980s, banks introduced a comprehensive strategy of country-risk assessment. They systematically professionalized their information resources and integrated risk and liability management. Economic forecasting was often based on historical data used for the classification and diversification of risks. However, learning from past experiences had limitations, as recent events were often overrated. This had the effect that the banks' country risk assessment focused mainly on developing countries while the industrial world was not included in the schemes. This might explain why many banks have continually underestimated the financial risks present in developed countries since the 1990s.
    Keywords: Risk management,financial markets,banks,expectations,historical experience
    JEL: F65 G15 G17 G32 N2
    Date: 2022
  15. By: Michael Gurkov (Bank of Israel); Osnat Zohar (Bank of Israel)
    Abstract: We estimate the distribution of future GDP growth given current financial and macroeconomic conditions. The distribution is generally symmetric, indicating that upside and downside risks are balanced. Its dispersion, which captures forecast uncertainty, rises when the median forecast decreases. The model allows us to study the connection between financial variables and GDP growth. We find that accommodative financial conditions, either in the local or the global economy, contribute to downside risks to growth within three years.
    Keywords: downside risks, macro-financial linkages, volatility paradox
    JEL: E17 E32 E44 G1
    Date: 2022–02
  16. By: L. Scaffidi Domianello; G.M. Gallo; E. Otranto
    Abstract: In this paper we remark that the evolution of the realized volatility is characterized by a combination between high–frequency dynamics and a smoother persistent dynamics evolving at a lower–frequency. We suggest a new Multiplicative Error Model which combines the mixed frequency features of a MIDAS with Markovian dynamics. When estimated in–sample on the realized kernel volatility of the S&P500 index, this model dominates other simpler specifications, especially when monthly aggregated realized volatility is used. The same pattern is confirmed in the out–of–sample forecasting performance which suggests that adding an abrupt change in the average level of volatility better helps in tracking extreme episodes of volatility and a relative quick absorption of the shocks.
    Keywords: Short– and Long–Run Components;realized volatility;Multiplicative Error Model;MIDAS;markov switching
    Date: 2022
  17. By: Pierre Brugière (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique); Gabriel Turinici (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We present in this paper a method to compute, using generative neural networks, an estimator of the "Value at Risk" for a nancial asset. The method uses a Variational Auto Encoder with a 'energy' (a.k.a. Radon- Sobolev) kernel. The result behaves according to intuition and is in line with more classical methods.
    Date: 2022–12–01
  18. By: Hui Zhou; Jun Nagayasu
    Abstract: We show a theoretical relationship between ESG disclosure and stock price crash risk and provide statistical evidence to support their non-monotonic relation using firm-level data from China. The weak relationship between these two indicators reported in previous studies is attributable to the linearity assumption used in the analysis.
    Date: 2022–12
  19. By: Dohmen, Thomas (University of Bonn and IZA); Quercia, Simone (University of Verona); Willrodt, Jana (Düsseldorf Institute for Competition Economics (DICE))
    Abstract: In this paper, we provide an explanation for why risk taking is related to optimism. Using a laboratory experiment, we show that the degree of optimism predicts whether people tend to focus on the positive or negative outcomes of risky decisions. While optimists tend to focus on the good outcomes, pessimists focus on the bad outcomes of risk. The tendency to focus on good or bad outcomes of risk in turn affects both the self-reported willingness to take risk and actual risktaking behavior. This suggests that dispositional optimism may affect risk taking mainly by shifting attention to specific outcomes rather than causing misperception of probabilities. In line with this, in a second study we find evidence that dispositional optimism is related to elicited parameters of rank dependent utility theory suggesting that focusing may be among the psychological determinants of decision weights. Finally, we corroborate our findings with process data related to focusing showing that optimists tend to remember more and attend more to good outcomes and this in turn affects their risk taking.
    Keywords: risk taking behavior, optimism, preference measure
    JEL: D91 C91 D81 D01
    Date: 2022–11
  20. By: Keisuke Kizaki (Graduate School of Economics, The University of Tokyoy); Taiga Saito (Faculty of Economics, The University of Tokyo); Akihiko Takahashi (Faculty of Economics, The University of Tokyoy)
    Abstract: This paper develops an incomplete equilibrium model with multi-agents' different risk attitudes and heterogeneous income/payout profiles. Particularly, we apply its concrete and computationally tractable model to reinsurance derivatives pricing and life-cycle investment, which are important for insurance and asset management companies in practice. In numerical experiments, we explicitly obtain agents' specific reinsurance prices with their stochastic discount factors (SDF), optimal life-cycle trading strategies, and endogenously determined expected returns of an risky asset in equilibrium. Moreover, we investigate how each agent's degree of risk aversion and income/payout profile, and correlations between economic or insurance factors and the risky asset price affect reinsurance claims pricing and optimal portfolios in life-cycle investment.
    Date: 2022–12
  21. By: Marc Wildi; Branka Hadji Misheva
    Abstract: Artificial intelligence is creating one of the biggest revolution across technology driven application fields. For the finance sector, it offers many opportunities for significant market innovation and yet broad adoption of AI systems heavily relies on our trust in their outputs. Trust in technology is enabled by understanding the rationale behind the predictions made. To this end, the concept of eXplainable AI emerged introducing a suite of techniques attempting to explain to users how complex models arrived at a certain decision. For cross-sectional data classical XAI approaches can lead to valuable insights about the models' inner workings, but these techniques generally cannot cope well with longitudinal data (time series) in the presence of dependence structure and non-stationarity. We here propose a novel XAI technique for deep learning methods which preserves and exploits the natural time ordering of the data.
    Date: 2022–12
  22. By: Hervé Roche (Universidad Adolfo Ibáñez); Juan Sotes-Paladino (Sotes-Paladino)
    Abstract: e study the equilibrium implications on asset prices of institutions’ trading with sentimentdriven retail investors. In the model, both the benchmarking concerns of institutions and the (irrational) optimism of retail investors boost the aggregate demand for a stock. We show that the ensuing demand pressure has a depressing effect on the stock market price of risk but an ambiguous effect on volatility. The overall effect on volatility results from the interplay of a benchmarking and a relative-wealth channels on the transmission of fundamental news to prices. This interplay can induce a negative relation between the degree of irrationality of the sentimentdriven investors and the stock return’s excess volatility, in stark contrast with a well-known prediction of models with no institutional investors. It further creates novel countercyclical patternsin stock volatility that cannot be explained in the absence of sentiment. Our results have a number of implications for the interpretation of the empirically documented dynamics of mispricing and excess volatility of financial assets.
    Keywords: Indexing, Sentiment, Excess Volatility, Institutional Investors.
    JEL: G11 G12 G18 G41
    Date: 2022–12
  23. By: Peter K. Friz; William Salkeld; Thomas Wagenhofer
    Abstract: We consider a class of stochastic processes with rough stochastic volatility, examples of which include the rough Bergomi and rough Stein-Stein model, that have gained considerable importance in quantitative finance. A basic question for such (non-Markovian) models concerns efficient numerical schemes. While strong rates are well understood (order $H$), we tackle here the intricate question of weak rates. Our main result asserts that the weak rate, for a reasonably large class of test function, is essentially of order $\min \{ 3H+\tfrac12, 1 \}$ where $H \in (0,1/2]$ is the Hurst parameter of the fractional Brownian motion that underlies the rough volatility process. Interestingly, the phase transitation at $H=1/6$ is related to the correlation between the two driving factors, and thus gives additional meaning to a quantity already of central importance in stochastic volatility modelling. Our results are complemented by a lower bound which show that the obtained weak rate is indeed optimal.
    Date: 2022–12
  24. By: Dyck, Daniel; Lorenz, Johannes; Sureth, Caren
    Abstract: Given the rising number, magnitude, and harshness of tax disputes between firms and tax authorities, firms increasingly call on tax technology and controversy expertise to try to resolve these disputes. This study investigates how tax technology embedded in the firm's Tax Risk Management System (TRMS) and the expertise of tax controversy managers affect dispute outcomes and compliance incentives. Using a game-theoretic model, we derive equilibrium strategies for a tax manager's compliance effort, a controversy manager's dispute resolution effort, and a tax authority's litigation decision. Absent a controversy manager, we find that improving a firm's TRMS quality unambiguously decreases the litigation probability. However, in the presence of a controversy manager, we surprisingly find that improving TRMS quality crowds out compliance efforts and can increase litigation probability. Overall we find that a high-quality TRMS is essential to take advantage of the dispute resolution function of a controversy manager.
    Keywords: tax dispute resolution,tax risk management,tax technology,controversy expertise,litigation
    JEL: H25 H26 C72 K34
    Date: 2022
  25. By: Galizia, Federico; Perraudin, William; Powell, Andrew; Turner, Timothy
    Abstract: Long-term development finance provided by Multilateral Development Banks (MDBs) is key to advancing the United Nations 2015 Sustainable Development Goals. However, MDBs are constrained in their lending by the availability of capital. This paper argues that Risk Transfer, as a complement to equity injections, could permit higher MDB lending by attracting a broader class of investors. We describe selected examples of actual Risk Transfer transactions and provide estimates of the potential expansion in lending these techniques could yield. But we also identify obstacles that limit investors willingness and ability to participate in these transactions. Therefore, we recommend an agenda for international policymakers to open the way for the wider use of Risk Transfer. Still, we recognize this will be a gradual process which cannot substitute for MDB expansion through additional ordinary capital resources.
    JEL: F53 O16 G15
    Date: 2021–11
  26. By: Malte Kn\"uppel; Fabian Kr\"uger; Marc-Oliver Pohle
    Abstract: Multivariate distributional forecasts have become widespread in recent years. To assess the quality of such forecasts, suitable evaluation methods are needed. In the univariate case, calibration tests based on the probability integral transform (PIT) are routinely used. However, multivariate extensions of PIT-based calibration tests face various challenges. We therefore introduce a general framework for calibration testing in the multivariate case and propose two new tests that arise from it. Both approaches use proper scoring rules and are simple to implement even in large dimensions. The first employs the PIT of the score. The second is based on comparing the expected performance of the forecast distribution (i.e., the expected score) to its actual performance based on realized observations (i.e., the realized score). The tests have good size and power properties in simulations and solve various problems of existing tests. We apply the new tests to forecast distributions for macroeconomic and financial time series data.
    Date: 2022–11
  27. By: Kim Ristolainen (Department of Economics, Turku School of Economics, University of Turku, Finland)
    Abstract: Economic research has shown that debt markets have an information sensitivity property that allows these markets to work properly when price discovery is absent and opaqueness is maintained. Dang, Gorton and Holmström (2015) argue that sufficiently âbad newsâ can switch debt to become information sensitive and start a financial crisis. We identify narrative triggers in the news by utilizing machine learning methods and daily information about firm default probability, the publicâs information acquisition and newspaper articles. We find state-specific generalizable triggers whose effect is determined by the language used by journalists. This language is associated with different psychological thinking processes.
    Keywords: information sensitivity, debt markets, financial crisis, machine learning, news data, primordial thinking process
    JEL: G01 G14 G41
    Date: 2022–12

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