nep-rmg New Economics Papers
on Risk Management
Issue of 2019‒09‒09
eighteen papers chosen by
Stan Miles
Thompson Rivers University

  1. The Evaluation of Model Risk for Probability of Default and Expected Loss By Gourieroux, Christian; Tiomo, Andre
  2. Do reserve requirements reduce the risk of bank failure? By Glocker, Christian
  3. Pricing Financial Derivatives Subject to Multilateral Credit Risk and Collateralization By Xiao,Tim
  4. Robust Inference about Conditional Tail Features: A Panel Data Approach By Yuya Sasaki; Yulong Wang
  5. Predicting Consumer Default: A Deep Learning Approach By Stefania Albanesi; Domonkos F. Vamossy
  6. The risk-taking channel of international financial flows By Pietro Cova; Filippo Natoli
  7. Lifetime Ruin Problem Under High-watermark Fees and Drift Uncertainty By Junbeom Lee; Xiang Yu; Chao Zhou
  8. ChainNet: Learning on Blockchain Graphs with Topological Features By Nazmiye Ceren Abay; Cuneyt Gurcan Akcora; Yulia R. Gel; Umar D. Islambekov; Murat Kantarcioglu; Yahui Tian; Bhavani Thuraisingham
  9. Stress Testing and Bank Lending By Shapiro, Joel; Zeng, Jing
  10. Fourier transform MCMC, heavy tailed distributions and geometric ergodicity By Denis Belomestny; Leonid Iosipoi
  11. Predicting financial distress of companies: Comparison between multivariate discriminant analysis and multilayer perceptron for Tunisian case By Fayçal Mraihi; Inane Kanzari
  12. An algorithm for construction of a portfolio with a fundamental criterion By Pawel Kliber; Anna Rutkowska-Ziarko
  13. A Risk-Hedging View to Refinery Capacity Investment By Hamed Ghoddusi; Franz Wirl
  14. Relationship between optimal portfolios which can maximize and minimize the expected return By Takashi Shinzato
  15. An Intertemporal CAPM with stochastic volatility By Campbell, John Y.; Giglio, Stefano; Polk, Christopher; Turley, Robert
  16. Detecting stock market bubbles based on the cross-sectional dispersion of stock prices By Takayuki Mizuno; Takaaki Ohnishi; Tsutomu Watanabe
  17. Yield Risk, Price Risk, and Demand for Crop Insurance By Rosch, Stephanie D.; Crane-Droesch, Andrew
  18. QCNN: Quantile Convolutional Neural Network By G\'abor Petneh\'azi

  1. By: Gourieroux, Christian; Tiomo, Andre
    Abstract: The quanti�cation of model risk is still in its infancy. This paper provides an operational quanti�cation of this risk for credit portfolio, when the objective is to approximate the average loss. The methodology is easy to implement and does not require the construction of any worst-case model. The required capital computed to cover for model risk depends on three components, that are an estimated impact of the incorrect model, an evaluated risk of inaccurate estimation of model risk and the prediction error hedge factor. The approach is illustrated by an application to a portfolio of corporate loans segmented by grades.
    Keywords: Model Risk, Estimation Risk, Speci�cation Risk, Expected Loss, Probability of Default, Required Capital, Prudential Regulation, Difference Estimator. 1
    JEL: G30
    Date: 2019–08–30
  2. By: Glocker, Christian
    Abstract: There is an increasing literature proposing reserve requirements for financial stability. This study assesses their effects on the probability of bank failure and compares them to those of capital requirements. To this purpose a banking model is considered that is subject to legal reserve requirements. In general, higher reserve requirements promote risk-taking as either borrowers or banks have an incentive to choose riskier assets, so banks' probability of failure rises. Borrowers' moral hazard problem augments the adverse effects. They are mitigated when allowing for imperfectly correlated loan-default as higher interest revenues from non-defaulting loans curb losses from defaulting loans.
    Keywords: Reserve requirements, liquidity regulation, capital requirements, bank failure, default correlation
    JEL: E43 E58 G21 G28
    Date: 2019–08
  3. By: Xiao,Tim
    Abstract: This article presents a new model for valuing financial contracts subject to credit risk and collateralization. Examples include the valuation of a credit default swap (CDS) contract that is affected by the trilateral credit risk of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most pervasive threats in financial markets. We also show that a fully collateralized CDS is not equivalent to a risk-free one. In other words, full collateralization cannot eliminate counterparty risk completely in the CDS market.
    Keywords: asset pricing,credit risk modeling,collateralization,comvariance,comrelation,correlation,CDS
    JEL: E44 G21 G12 G24 G32 G33 G18 G28
    Date: 2018
  4. By: Yuya Sasaki; Yulong Wang
    Abstract: We develop a new extreme value theory for panel data and use it to construct asymptotically valid confidence intervals (CIs) for conditional tail features such as conditional extreme quantile and conditional tail index. As a by-product, we also construct CIs for tail features of the coefficients in the random coefficient regression model. The new CIs are robustly valid without parametric assumptions and have excellent small sample coverage and length properties. Applying the proposed method, we study the tail risk of the monthly U.S. stock returns and find that (i) the left tail features of stock returns and those of the Fama-French regression residuals heavily depend on other stock characteristics such as stock size; and (ii) the alpha's and beta's are strongly heterogeneous across stocks in the Fama-French regression. These findings suggest that the Fama-French model is insufficient to characterize the tail behavior of stock returns.
    Date: 2019–08
  5. By: Stefania Albanesi; Domonkos F. Vamossy
    Abstract: We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation.
    Date: 2019–08
  6. By: Pietro Cova (Bank of Italy); Filippo Natoli (Bank of Italy)
    Abstract: From the second half of the 1990s, the high saving propensity in emerging economies triggered massive inflows towards safe assets in the United States; then, from the early 2000s, global banks also increased investment in US markets targeting riskier securities. We investigate to what extent the global saving glut and the global banking glut have stimulated risk taking, and find significant effects on credit spreads, market volatility and bank leverage. In a VAR framework, we also detect linkages between foreign inflows, US household indebtedness and house prices, suggesting a substan- tial risk-taking channel. Our findings provide evidence of the autonomous role of foreign financial flows during the run-up to the global financial crisis.
    Keywords: saving glut, banking glut, capital flows, banking leverage, risk-taking channel
    JEL: F32 F33 F34
    Date: 2019–08–08
  7. By: Junbeom Lee; Xiang Yu; Chao Zhou
    Abstract: This paper aims to make a new contribution to the study of lifetime ruin problem by considering investment in two hedge funds with high-watermark fees and drift uncertainty. Due to multi-dimensional performance fees that are charged whenever each fund profit exceeds its historical maximum, the value function is expected to be multi-dimensional. New mathematical challenges arise as the standard dimension reduction cannot be applied, and the convexity of the value function and Isaacs condition may not hold in our ruin probability minimization problem with drift uncertainty. We propose to employ the stochastic Perron's method to characterize the value function as the unique viscosity solution to the associated Hamilton Jacobi Bellman (HJB) equation without resorting to the proof of dynamic programming principle. The required comparison principle is also established in our setting to close the loop of stochastic Perron's method.
    Date: 2019–09
  8. By: Nazmiye Ceren Abay; Cuneyt Gurcan Akcora; Yulia R. Gel; Umar D. Islambekov; Murat Kantarcioglu; Yahui Tian; Bhavani Thuraisingham
    Abstract: With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other financial networks, such as stock and currency trading, blockchain based cryptocurrencies have the entire transaction graph accessible to the public (i.e., all transactions can be downloaded and analyzed). A natural question is then to ask whether the dynamics of the transaction graph impacts the price of the underlying cryptocurrency. We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. In contrast, the new graph associated topological features computed using the tools of persistent homology, are found to exhibit a high utility for predicting Bitcoin price dynamics. %explain higher order interactions among the nodes in Blockchain graphs and can be used to build much more accurate price prediction models. Using the proposed persistent homology-based techniques, we offer a new elegant, easily extendable and computationally light approach for graph representation learning on Blockchain.
    Date: 2019–08
  9. By: Shapiro, Joel; Zeng, Jing
    Abstract: Bank stress tests are a major form of regulatory oversight. Banks respond to the toughness of the tests by changing their lending behavior. Regulators care about bank lending; therefore, banks' reactions to the tests affect the tests' design and create a feedback loop. We demonstrate that stress tests may be (1) soft, in order to encourage lending in the future, or (2) tough, in order to deter excessive risk-taking in the future. There may be multiple equilibria due to strategic complementarity. Regulators may strategically delay stress tests. We also analyze bottom-up stress tests and banking supervision exams.
    Keywords: bank lending; Bank Regulation; reputation; stress tests
    JEL: G21 G28
    Date: 2019–08
  10. By: Denis Belomestny; Leonid Iosipoi
    Abstract: Markov Chain Monte Carlo methods become increasingly popular in applied mathematics as a tool for numerical integration with respect to complex and high-dimensional distributions. However, application of MCMC methods to heavy tailed distributions and distributions with analytically intractable densities turns out to be rather problematic. In this paper, we propose a novel approach towards the use of MCMC algorithms for distributions with analytically known Fourier transforms and, in particular, heavy tailed distributions. The main idea of the proposed approach is to use MCMC methods in Fourier domain to sample from a density proportional to the absolute value of the underlying characteristic function. A subsequent application of the Parseval's formula leads to an efficient algorithm for the computation of integrals with respect to the underlying density. We show that the resulting Markov chain in Fourier domain may be geometrically ergodic even in the case of heavy tailed original distributions. We illustrate our approach by several numerical examples including multivariate elliptically contoured stable distributions.
    Date: 2019–09
  11. By: Fayçal Mraihi (Higher School of Economics and Business Sciences of Tunis); Inane Kanzari (Higher School of Economics and Business Sciences of Tunis)
    Abstract: In this study, we try to develop a model for predicting corporate default based on a multivariate discriminant analysis (ADM) and a multilayer perceptron (MLP). The two models are applied to the Tunisian cases. Our sample consists of 212 companies in the various industries (106 ‘healthy’ companies and 106 “distressed” companies) over the period 2005-2010. The results of the use of a battery of 87 ratios showed that 16 ratios can build the model and that liquidity and solvency have more weight than profitability and management in predicting the distress. Despite the slight superiority of the results provided by the MLP model, on the control sample, the results provided by the two models are good either in terms of correct percentage of classification or in terms of stability of discriminating power over time and space.
    Date: 2019–08–21
  12. By: Pawel Kliber (Poznan University of Economics); Anna Rutkowska-Ziarko (University of Warmia and Mazury)
    Abstract: The classical models for construction of investment portfolio do not take into account fundamental values of considered companies. In our approach we extend the portfolio choice by adding this dimension to the classical criteria of profitability and risk. It is assumed that an investor selects stock according to their attractiveness, measured by some fundamental values of companies. In this approach portfolios are assessed according to three criteria: their profitability, risk (measured by variance of returns) and fundamental value (measured by some indicators of fundamental value). In this article we consider earnings to price ratio as the measure of the fundamental value of a company. In the paper we consider an algorithm for constructing portfolios with fundamental criterion based on analytical solutions for appropriate optimization problems. In the optimization problem we consider minimizing variance with constrains on expected return and attractiveness of investment, measured with some indicators of fundamental values of companies in a portfolio. We also present empirical examples of calculating effective portfolios of stocks listed on the Warsaw Stock Exchange.
    Keywords: portfolio analysis, fundamental value, multicriterial choice, fundamental analysis
    JEL: C61 C63 G11
    Date: 2019–07
  13. By: Hamed Ghoddusi (School of Business, Stevens Institute of Technology); Franz Wirl (Energy Environment, Faculty of Business, Economics and Statistics, University of Vienna)
    Abstract: Should oil-rich members of OPEC invest in the oil refinery industry? This is a crucial energy policy question for such economies. We offer theoretical models for a vertical integration strategy within an oil-producing economy, based on a risk-hedging view. The first model highlights the trade-off between return and risk-reduction features of upstream/downstream sectors. The dynamic model demonstrates the volatility of total budgetary revenue of each sector. Our theory-guided empirical analysis shows that though the average markup in the refining sector is significantly smaller than the profits in the upstream, downstream investment can provide some hedging value. In particular, the more stable and mean-reverting refining margins provide a partial revenue cushion when crude oil prices are low. We discuss the risk-hedging feature of the refinery industry when the crude oil market faces supply versus demand shocks.
    Date: 2019–08–21
  14. By: Takashi Shinzato
    Abstract: In recent years, the evaluation of the minimal investment risk of the quenched disordered system of a portfolio optimization problem and the investment concentration of the optimal portfolio has been actively investigated using the analysis methods of statistical mechanical informatics. However, the work to date has not sufficiently compared the optimal portfolios of different portfolio optimization problems. Therefore, in this paper, we use the Lagrange undetermined multiplier method and replica analysis to examine the relationship between the optimal portfolios of the expected return maximization problem and the expected return minimization problem with constraints of budget and investment risk. In particular, we derive the mean square error and the correlation coefficient of the optimal portfolios of these maximization and minimization problems as functions of a variable (the degree of risk tolerance) that can characterize the feasible subspace defined by the two constraints.
    Date: 2019–08
  15. By: Campbell, John Y.; Giglio, Stefano; Polk, Christopher; Turley, Robert
    Abstract: This paper studies the pricing of volatility risk using the Örst-order conditions of a long-term equity investor who is content to hold the aggregate equity market rather than overweighting value stocks and other equity portfolios that are attractive to short-term investors. We show that a conservative long-term investor will avoid such overweights in order to hedge against two types of deterioration in investment opportunities: declining expected stock returns, and increasing volatility. Empirically, we present novel evidence that low-frequency movements in equity volatility, tied to the default spread, are priced in the cross-section of stock returns.
    JEL: F3 G3
    Date: 2018–05–01
  16. By: Takayuki Mizuno (National Institute of Informatics); Takaaki Ohnishi (Graduate School of Information Science and Technology, University of Tokyo); Tsutomu Watanabe (Graduate School of Economics,University of Tokyo)
    Abstract: A statistical method is proposed for detecting stock market bubbles that occur when speculative funds concentrate on a small set of stocks. The bubble is defined by stock price diverging from the fundamentals. A firm’s financial standing is certainly a key fundamental attribute of that firm. The law of one price would dictate that firms of similar financial standing share similar fundamentals. We investigate the variation in market capitalization normalized by fundamentals that is estimated by Lasso regression of a firm’s financial standing. The market capitalization distribution has a substantially heavier upper tail during bubble periods, namely, the market capitalization gap opens up in a small subset of firms with similar fundamentals. This phenomenon suggests that speculative funds concentrate in this subset. We demonstrated that this phenomenon could have been used to detect the dot-com bubble of 1998-2000 in different stock exchanges.
    Keywords: Stock market; Financial bubble; Nowcast; Power law
    Date: 2019–08
  17. By: Rosch, Stephanie D.; Crane-Droesch, Andrew
    Keywords: Agricultural and Food Policy
    Date: 2019–06–25
  18. By: G\'abor Petneh\'azi
    Abstract: A dilated causal one-dimensional convolutional neural network architecture is proposed for quantile regression. The model can forecast any arbitrary quantile, and it can be trained jointly on multiple similar time series. An application to Value at Risk forecasting shows that QCNN outperforms linear quantile regression and constant quantile estimates.
    Date: 2019–08

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