nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒02‒27
eighteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Macroprudential Regulation: A Risk Management Approach By Sweder van Wijnbergen; Daniël Dimitrov
  3. Conditional generalized quantiles based on expected utility model and equivalent characterization of properties By Qinyu Wu; Fan Yang; Ping Zhang
  4. Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions By Jan Pr\"user; Florian Huber
  5. Robust Mean-Variance Approximations By Simone Cerreia-Vioglio; Fabio Maccheroni; Massimo Marinacci
  6. Taming Overconfident CEOs Through Stricter Financial Regulation By Bernhard Kassner
  7. Factor Model of Mixtures By Cheng Peng; Stanislav Uryasev
  8. Forecasting Value-at-Risk using deep neural network quantile regression By Chronopoulos, Ilias; Raftapostolos, Aristeidis; Kapetanios, George
  9. Oil Prices Uncertainty, Endogenous Regime Switching, and Inflation Anchoring By Yoosoon Chang; Ana Maria Herrera; Elena Pesavento
  10. Multivariate risk preferences in the quality-adjusted life year model By Arthur E. Attema; Jona J. Frasch; Olivier L’haridon
  11. The Market-Based Probability of Stock Returns By Olkhov, Victor
  12. Limitations of implementing an expected credit loss model By Bischof, Jannis; Haselmann, Rainer; Kohl, Frederik; Schlueter, Oliver
  13. Stationary Heston model: Calibration and Pricing of exotics using Product Recursive Quantization By Vincent Lemaire; Thibaut Montes; Gilles Pagès
  14. The Capital Asset Pricing Model: A New Empirical Investigation By Zarifhonarvar, Ali
  15. "Market Reaction to Capital Expenditure: Evidence from Company in Bankruptcy Risk " By Juniarti
  16. Three layers of uncertainty By Ilke AYDOGAN; Loïc BERGER; Valentina BOSETTI; Ning LIU
  17. An MCMC Approach to Classical Estimation By Victor Chernozhukov; Han Hong
  18. Adopting good practices on public debt management in Asia and the Pacific By Charan Singh; Vatcharin Sirimaneetham

  1. By: Sweder van Wijnbergen (University of Amsterdam); Daniël Dimitrov (University of Amsterdam)
    Abstract: We address the problem of regulating the size of banks’ macroprudential capital buffers by using market-based estimates of systemic risk and by developing a modeling mechanism through which capital buffers can be allocated efficiently across systemic banks. First, a Distance-to-Default type measure relates a bank’s default risk to its capital requirements. Second, a correlation structure in the default dependencies between banks is estimated from co-movements in the single-name CDS spreads of the underlying banks. Third, risk minimization and equalization approaches are adopted to allocate the capital requirements in line with a policy balancing the social costs and benefits of higher capital requirements. The model is applied to the European banking sector.
    Keywords: systemic risk, regulation, implied market measures, financial institutions, CDS rates
    JEL: G01 G20 G18 G38
    Date: 2023–01–20
  2. By: Jumbe, George
    Abstract: Credit risk, also known as default risk, is the likelihood of a corporation losing money if a business partner defaults. If the liabilities are not met under the terms of the contract, the firm may default, resulting in the loss of the company. There is no clear way to distinguish between organizations that will default and those that will not prior to default. We can only make probabilistic estimations of the risk of default at best. There are two types of credit risk default models in this regard: structural and reduced form models. Structural models are used to calculate the likelihood of a company defaulting based on its assets and liabilities. If the market worth of a company's assets is less than the debt it owes, it will default. Reduced form models often assume an external cause of default, such as a Poisson jump process, which is driven by a stochastic process. They model default as a random event with no regard for the balance sheet of the company. This paper provides a Review of credit risk default models.
    Date: 2023–01–13
  3. By: Qinyu Wu; Fan Yang; Ping Zhang
    Abstract: As a counterpart to the (static) risk measures of generalized quantiles and motivated by Bellini et al. (2018), we propose a new kind of conditional risk measure called conditional generalized quantiles. We first show their well-definedness and they can be equivalently characterised by a conditional first order condition. We also discuss their main properties, and, especially, We give the characterization of coherency/convexity. For potential applications as a dynamic risk measure, we study their time consistency properties, and establish their equivalent characterizations among conditional generalized quantiles.
    Date: 2023–01
  4. By: Jan Pr\"user; Florian Huber
    Abstract: Modeling and predicting extreme movements in GDP is notoriously difficult and the selection of appropriate covariates and/or possible forms of nonlinearities are key in obtaining precise forecasts. In this paper, our focus is on using large datasets in quantile regression models to forecast the conditional distribution of US GDP growth. To capture possible non-linearities we include several nonlinear specifications. The resulting models will be huge dimensional and we thus rely on a set of shrinkage priors. Since Markov Chain Monte Carlo estimation becomes slow in these dimensions, we rely on fast variational Bayes approximations to the posterior distribution of the coefficients and the latent states. We find that our proposed set of models produces precise forecasts. These gains are especially pronounced in the tails. Using Gaussian processes to approximate the nonlinear component of the model further improves the good performance in the tails.
    Date: 2023–01
  5. By: Simone Cerreia-Vioglio; Fabio Maccheroni; Massimo Marinacci
    Abstract: We study mean-variance approximations for a large class of preferences. Compared to the standard mean-variance approximation that only features a risk variability term, a novel index of variability appears. Its neglect in an empirical estimation may result in puzzling in ated risk terms of standard mean-variance approximations.
    Date: 2023
  6. By: Bernhard Kassner (LMU Munich)
    Abstract: A large body of literature finds that managerial overconfidence increases risk-taking by financial institutions. This paper shows that financial regulation can be effective at mitigating this type of risk. Exploiting regulatory changes introduced after the financial crisis as a natural experiment, I find that overconfidence-induced risk-taking decreases in financial institutions subject to stricter regulation. Following the easing of these regulations, overconfidence-induced risk-taking increases again. These findings confirm the effectiveness of financial regulation at correcting overconfident behavior, but also suggest that the impact fades away quickly once removed.
    Keywords: overconfidence; risk; regulation; financial sector;
    JEL: G28 G32 G38 G40
    Date: 2023–01–27
  7. By: Cheng Peng; Stanislav Uryasev
    Abstract: This paper considers the problem of estimating the distribution of a response variable conditioned on observing some factors. Existing approaches are often deficient in one of the qualities of flexibility, interpretability and tractability. We propose a model that possesses these desirable properties. The proposed model, analogous to classic mixture regression models, models the conditional quantile function as a mixture (weighted sum) of basis quantile functions, with the weight of each basis quantile function being a function of the factors. The model can approximate any bounded conditional quantile model. It has a factor model structure with a closed-form expression. The calibration problem is formulated as convex optimization, which can be viewed as conducting quantile regressions of all confidence levels simultaneously and does not suffer from quantile crossing by design. The calibration is equivalent to minimization of Continuous Probability Ranked Score (CRPS). We prove the asymptotic normality of the estimator. Additionally, based on risk quadrangle framework, we generalize the proposed approach to conditional distributions defined by Conditional Value-at-Risk (CVaR), expectile and other functions of uncertainty measures. Based on CP decomposition of tensors, we propose a dimensionality reduction method by reducing the rank of the parameter tensor and propose an alternating algorithm for estimating the parameter tensor. Our numerical experiments demonstrate the efficiency of the approach.
    Date: 2023–01
  8. By: Chronopoulos, Ilias; Raftapostolos, Aristeidis; Kapetanios, George
    Abstract: In this paper we use a deep quantile estimator, based on neural networks and their universal approximation property to examine a non-linear association between the conditional quantiles of a dependent variable and predictors. This methodology is versatile and allows both the use of different penalty functions, as well as high dimensional covariates. We present a Monte Carlo exercise where we examine the finite sample properties of the deep quantile estimator and show that it delivers good finite sample performance. We use the deep quantile estimator to forecast Value-at-Risk and find significant gains over linear quantile regression alternatives and other models, which are supported by various testing schemes. Further, we consider also an alternative architecture that allows the use of mixed frequency data in neural networks. This paper also contributes to the interpretability of neural networks output by making comparisons between the commonly used SHAP values and an alternative method based on partial derivatives.
    Keywords: Quantile regression, machine learning, neural networks, value-at-risk, forecasting
    Date: 2023–02–07
  9. By: Yoosoon Chang (Indiana University); Ana Maria Herrera (University of Kentucky); Elena Pesavento (Emory University)
    Abstract: Using a novel approach to model regime switching with dynamic feedback and interactions, we extract latent mean and volatility factors in oil price changes. We illustrate how the volatility factor constitutes a useful measure of oil market risk (or oil price uncertainty) for policy makers and analysts as it captures uncertainty not reflected in other economic/financial uncertainty measures. Then, in the context of a VAR, we investigate the role of oil price uncertainty in driving inflation expectations and inflation anchoring. We show that shocks to the mean factor lead to higher expected inflation and inflation disagreement among professional forecasters and households. In contrast, shocks to the volatility factor act as aggregate demand shocks in that they result in lower expected inflation, yet they do increase disagreement about future inflation among professional forecasters and, especially, among households. We also provide econometric evidence suggesting the proposed endogenous volatility switching model can outperform other regime switching models.
    Keywords: oil price volatility, endogenous regime switching, expected inflation, inflation anchoring
    Date: 2023–02
  10. By: Arthur E. Attema (Erasmus School of Health Policy and Management |Rotterdam]); Jona J. Frasch (Erasmus School of Health Policy and Management |Rotterdam]); Olivier L’haridon (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR1 - Université de Rennes 1 - UNIV-RENNES - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)
    Abstract: The interest in multivariate and higher-order risk preferences has increased. A growing body of literature has demonstrated the relevance and impact of these preferences, but for health the evidence is lacking. We measure multivariate and higher-order risk preferences for quality of life (QoL) and longevity, the two attributes of the Quality-Adjusted Life Year (QALY) model. We observe preferences for a positive correlation between these attributes and for pooling together a fixed loss in one of the attributes and a mean-zero risk in the other, and for pooling together mean-zero risks in QoL and longevity. The findings indicate that higher-order risk preferences are stronger for health than for money. Furthermore, we test if preferences for a risky treatment for a disease affecting only QoL, depend on life expectancy. We find no such a relation, but there is a positive relation between riskiness of a comorbidity affecting life expectancy and risk aversion for a QoL treatment. We therefore observe no definitive deviation from the QALY model, although the model is more robust when expected longevity is high. Our findings suggest that the current practice of cost-effectiveness analysis should be generalized to account for risk aversion in QoL and longevity, and higher-order preferences.
    Keywords: comorbidities, correlation attitude, prudence, QALYs, risk apportionment, risk aversion, temperance, treatment intensity
    Date: 2022–02
  11. By: Olkhov, Victor
    Abstract: We show how time-series of random market trade values and volumes completely describe stochasticity of stock returns. We derive equation that links up returns with current and past trade values and show how statistical moments of the trade values and volumes determine statistical moments of stock returns. We estimate statistical moments of the trade values and volumes by the conventional frequency-based probability. However we believe that frequencies of stock returns don’t define its probabilities as market and financial concepts. We present the market-based treatment of the probability of stock returns that defines average returns during “trading day” that completely match conventional notion of the weighted value return of the portfolio. We derive how statistical moments of the market trade values and volumes define approximations of the characteristic functions and probability density functions of stock returns. We derive volatility of stock returns, autocorrelations of stock returns, returns-volume and returns-price correlations through corresponding relations between statistical moments of the market trade values and volumes. The market-based probability of stock returns reveals direct dependence of statistical properties of stock returns on market trade randomness and economic uncertainty. Any reasonable forecasting of stock returns should be based on well-grounded predictions of the market trades and economic environment.
    Keywords: stock returns; volatility; correlations; probability; market trades
    JEL: C00 D40 E43 E50 G00 G12 G15
    Date: 2023–02–06
  12. By: Bischof, Jannis; Haselmann, Rainer; Kohl, Frederik; Schlueter, Oliver
    Abstract: The loan impairment rules recently introduced by IFRS 9 require banks to estimate their future credit losses by using forward-looking information. We use supervisory loan-level data from Germany to investigate how banks apply their reporting discretion and adjust their lending upon the announcement of the new rules. Our identification strategy exploits a cut-off for the level of provisions at the investment grade threshold based on banks' internal rating of a borrower. We find that banks required to adopt the new rules assign better internal ratings to exactly the same borrowers compared to banks that do not apply IFRS 9 around this cut-off. This pattern is consistent with a strategic use of the increased reporting discretion that is inherent to rules requiring forward-looking loss estimation. At the same time, banks also reduce their lending exposure to exactly those borrowers at the highest risk of experiencing a rating downgrade below the cutoff. These loans would be associated with additional provisions in future periods, both in the intensive and extensive margin. The lending change thus mitigates some of the negative effects of increased reporting opportunism on banks' crisis resilience. However, when these firms with internal ratings around the investment grade cut-off obtain less external funding through banks, the introduction of IFRS 9 will likely also be associated with real economic effects.
    Keywords: Bank Accounting, CECL, Expected credit losses, IFRS 9, Impairments, Loans
    JEL: G01 G21 G28 K23 M41
    Date: 2022
  13. By: Vincent Lemaire (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique); Thibaut Montes (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique); Gilles Pagès (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)
    Abstract: A major drawback of the Standard Heston model is that its implied volatility surface does not produce a steep enough smile when looking at short maturities. For that reason, we introduce the Stationary Heston model where we replace the deterministic initial condition of the volatility by its invariant measure and show, based on calibrated parameters, that this model produce a steeper smile for short maturities than the Standard Heston model. We also present numerical solution based on Product Recursive Quantization for the evaluation of exotic options (Bermudan and Barrier options).
    Date: 2022–04–03
  14. By: Zarifhonarvar, Ali
    Abstract: In financial economics, numerous theoretical models explain the relationship between investment risk and return in the capital market, one of the most common being the Capital Asset Pricing Model (CAPM). After reviewing the literature in this area, this study discusses the theoretical background of the CAPM model. After explaining the relationship between systematic corporate risk in different industries, the hypotheses for a positive linear correlation between stock returns and systematic risk and the relation of these coefficients to the CAPM model predictions are tested. Thus, after data sampling to obtain the monthly rate of return of stocks in the Tehran Stock Exchange, the monthly rate of return of the market portfolio and the return on risk-free investment are obtained from April 2008 to March 2013. Finally, it will be shown that the systematic risk variable and its square are also crucial to explaining stock return fluctuations. A nonlinear quadratic correlation is confirmed between the rate of return and systematic risk in the stock data of companies sampled from the obtained sample of the Tehran Stock Exchange.
    Keywords: CAPM, Beta, Stock Market, Premium, Risk
    JEL: G10 G11 G12
    Date: 2023
  15. By: Juniarti (Petra Christian University, Jl. Siwalankerto 121-131, 60236, Surabaya, Indonesia Author-2-Name: Yulius Jogi Christiawan Author-2-Workplace-Name: Petra Christian University, Jl. Siwalankerto 121-131, 60236, Surabaya, Indonesia Author-3-Name: Hendri Kwistianus Author-3-Workplace-Name: Petra Christian University, Jl. Siwalankerto 121-131, 60236, Surabaya, Indonesia Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)
    Abstract: " Objective - This study aims to examine whether the condition of the bankruptcy risk of a company will influence the market response to capital expenditure. The main hypothesis of this research is that the positive market reaction to the level of capital expenditure issued will be different in companies with a high level of bankruptcy risk and companies with low bankruptcy risk. Methodology/Technique - The study was conducted on 56 companies with large capitalization on the Indonesia Stock Exchange for 2018-2021. Findings - The results of hypothesis testing indicate that the market responds positively to capital expenditures and the company's bankruptcy risk conditions. In addition, it is proven that in companies at risk of bankruptcy, the market reacts positively to capital expenditures made by companies. In contrast, in companies that are not in a state of bankruptcy, the market does not respond to capital expenditures made by companies. The results of this study are expected to be used by market participants when they analyze the information on capital expenditures made by the company. Novelty - This study contributes to the literature by providing empirical evidence which explores a company's bankruptcy risk as the unique factor that affects the relationship between capital expenditure and market response. Type of Paper - Empirical."
    Keywords: Capital Expenditure, Bankruptcy Risk, Market Response, Capital Investment
    JEL: G30 G31
    Date: 2022–12–31
  16. By: Ilke AYDOGAN (IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM - Lille Economie Management, F-59000 Lille, France; and iRisk Research Center on Risk and Uncertainty); Loïc BERGER (CNRS, Univ. Lille, IESEG School of Management, UMR 9221 - LEM - Lille Economie Management, F-59000 Lille, France; iRisk Research Center on Risk and Uncertainty; RFF-CMCC European Institute on Economics and the Environment (EIEE), and Centro Euro-Mediterraneo sui Cambiamenti Climatici, Italy); Valentina BOSETTI (Department of Economics and IGIER, Bocconi University, and RFF-CMCC European Institute on Economics and the Environment (EIEE), Centro Euro-Mediterraneo sui Cambiamenti Climatici, Italy); Ning LIU (School of Economics and Management, Beihang University and Laboratory for Low-carbon Intelligent Governance, Beihang University, China)
    Abstract: We explore decision-making under uncertainty using a framework that decomposes uncertainty into three distinct layers: (1) risk, which entails inherent randomness within a given probability model; (2) model ambiguity, which entails uncertainty about the probability model to be used; and (3) model misspecification, which entails uncertainty about the presence of the correct probability model among the set of models considered. Using a new experimental design, we isolate and measure attitudes towards each layer separately. We conduct our experiment on three di?erent subject pools and document the existence of a behavioral distinction between the three layers. In addition to providing new insights into the underlying processes behind ambiguity aversion, we provide the first empirical evidence of the role of model misspecification in decision-making under uncertainty.
    Keywords: : Ambiguity aversion, model uncertainty, model misspecification, non-expected utility, reduction of compound lotteries
    JEL: D81
    Date: 2022–11
  17. By: Victor Chernozhukov; Han Hong
    Abstract: This paper studies computationally and theoretically attractive estimators called the Laplace type estimators (LTE), which include means and quantiles of Quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods. The approach generates an alternative to classical extremum estimation and also falls outside the parametric Bayesian approach. For example, it offers a new attractive estimation method for such important semi-parametric problems as censored and instrumental quantile, nonlinear GMM and value-at-risk models. The LTE's are computed using Markov Chain Monte Carlo methods, which help circumvent the computational curse of dimensionality. A large sample theory is obtained for regular cases.
    Date: 2023–01
  18. By: Charan Singh; Vatcharin Sirimaneetham (Macroeconomic Policy and Financing for Development Division, United Nations Economic and Social Commission for Asia and the Pacific)
    Abstract: Amid rising public debt levels in many Asia-Pacific economies, this policy brief highlights public debt management practices that Asia-Pacific countries could adopt to benefit from lower financing costs and better risk management. It shows that the region has introduced a wide range of initiatives to enhance fiscal-monetary policy coordination, ensure separate and accountable debt management offices, improve public debt reporting, deal with public debt management risks, and manage cash flows and financial liquidity. Yet, available assessments suggest that overall public debt management has become less effective in several Asia-Pacific countries. Multilateral development partners could provide more technical assistance to enhance debt reporting transparency and facilitate the implementation of medium-term debt management strategies that fully incorporate fiscal contingency liability risks.
    Date: 2021–09

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