nep-rmg New Economics Papers
on Risk Management
Issue of 2019‒01‒21
ten papers chosen by
Stan Miles
Thompson Rivers University

  1. Systemic risk measures with markets volatility By Fei Sun
  2. Financial Networks and Systemic Risk in China’s Banking System By Sun, Lixin
  3. Systemic risk governance in a dynamical model of a banking system By Lorella Fatone; Francesca Mariani
  4. Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting By Yaodong Yang; Alisa Kolesnikova; Stefan Lessmann; Tiejun Ma; Ming-Chien Sung; Johnnie E. V. Johnson
  5. Financial ratios and the prediction of bankruptcy By Jeyhun A. Abbasov
  6. Do country risk factors attenuate the effect of taxes on corporate risk-taking? By Osswald, Benjamin; Sureth, Caren
  7. Effective energy commodities’ risk management: Econometric modeling of price volatility By Halkos, George; Tzirivis, Apostolos
  8. Early warning system for the European Insurance Sector By Lorenzo Danieli; Petr Jakubik
  9. Dynamic Tail Inference with Log-Laplace Volatility By Gordon V. Chavez
  10. Causal Impact of Risk Oversight Functions on Bank Risk: Evidence from a Natural Experiment By Balasubramanyan, Lakshmi; Daniel, Naveen; Haubrich, Joseph G.; Naveen, Lalitha

  1. By: Fei Sun
    Abstract: As systemic risk has become a hot topic in the financial markets, how to measure, allocate and regulate the systemic risk are becoming especially important. However, the financial markets are becoming more and more complicate, which makes the usual study of systemic risk to be restricted. In this paper, we will study the systemic risk measures on a special space $L^{p(\cdot)}$ where the variable exponent $p(\cdot)$ is no longer a given real number like the space $L^{p}$, but a random variable, which reflects the possible volatility of the financial markets. Finally, the dual representation for this new systemic risk measures will be studied. Our results show that every this new systemic risk measure can be decomposed into a convex certain function and a simple-systemic risk measure, which provides a new ideas for dealing with the systemic risk.
    Date: 2018–11
  2. By: Sun, Lixin
    Abstract: In this paper, using two alternative methods, we investigate the contagion effects and systemic risk in China’s commercial banks system based on the balance sheet data and the estimation on interbank exposures. First, we calculate various indicators in terms of the balance sheets of individual commercial banks to quantify contagiousness and vulnerability for China’s banking system without considering the detailed topology of interbank networks. Second, we estimate the detailed bilateral exposures matrix of the interbank network to examine the domino effects and snowball effects of financial contagion. The simulation results from two alternative approaches are consistent. Both suggest that the contagious risk arising from an assumed bank failure is trivial in Chinese banking system, whereas the amplification effects of the losses due to the financial interlinkage are non-trivial. In particular, we identify the systemic important banks in terms of a relative contagion index and the measures capturing the topological features of the interbank networks, respectively. Our study provides insights for the prevention of systemic risk and the implementation of macroprudential oversights in China’s banking system.
    Keywords: Balance Sheets; Interbank Networks; Financial Contagion; Systemic Risk; China’s Banking System
    JEL: D85 G21 G28
    Date: 2018–01–06
  3. By: Lorella Fatone; Francesca Mariani
    Abstract: We consider the problem of governing systemic risk in a banking system model. The banking system model consists in an initial value problem for a system of stochastic differential equations whose dependent variables are the log-monetary reserves of the banks as functions of time. The banking system model considered generalizes previous models studied in [5], [4], [7] and describes an homogeneous population of banks. Two distinct mechanisms are used to model the cooperation among banks and the cooperation between banks and monetary authority. These mechanisms are regulated respectively by the parameters $\alpha$ and $\gamma$. A bank fails when its log-monetary reserves go below an assigned default level. We call systemic risk or systemic event in a bounded time interval the fact that in that time interval at least a given fraction of the banks fails. The probability of systemic risk in a bounded time interval is evaluated using statistical simulation. A method to govern the probability of systemic risk in a bounded time interval is presented. The goal of the governance is to keep the probability of systemic risk in a bounded time interval between two given thresholds. The governance is based on the choice of the log-monetary reserves of a kind of "ideal bank" as a function of time and on the solution of an optimal control problem for the mean field approximation of the banking system model. The solution of the optimal control problem determines the parameters $\alpha$ and $\gamma$ as functions of time, that is defines the rules of the borrowing and lending activity among banks and between banks and monetary authority. Some numerical examples are discussed. The systemic risk governance is tested in absence and in presence of positive and negative shocks acting on the banking system.
    Date: 2018–12
  4. By: Yaodong Yang; Alisa Kolesnikova; Stefan Lessmann; Tiejun Ma; Ming-Chien Sung; Johnnie E. V. Johnson
    Abstract: The success of deep learning for unstructured data analysis is well documented but little evidence has emerged related to the structured, tabular datasets used in decision support. We address this research gap by considering the potential of deep learning to support financial risk management. In particular, we develop a deep learning model for predicting whether individual spread traders are likely to secure profits from future trades. This embodies typical modeling challenges faced in risk and behavior forecasting. Conventional machine learning requires data that is representative of the feature-target relationship and relies on the often costly development, maintenance, and revision of handcrafted features. Consequently, modeling highly variable, heterogeneous patterns such as the behavior of traders is challenging. Deep learning promises a remedy. Learning hierarchical distributed representations of the raw data in an automatic manner (e.g. risk taking behavior), it uncovers generative features that determine the target (e.g., trader's profitability), avoids manual feature engineering, and is more robust toward change (e.g. dynamic market conditions). The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over powerful machine learning benchmarks. Empirical results suggest that the financial institution which provided the data can increase annual profits by 16% through implementing a deep learning based risk management policy. The findings demonstrate the potential of applying deep learning methods for management science problems in finance, marketing, and accounting.
    Date: 2018–12
  5. By: Jeyhun A. Abbasov (Central Bank of Azerbaijan Republic)
    Abstract: In this work 10 financial ratios of 835 companies (48 companies were default and 787 companies were non-default) were used for prediction of bankruptcy. On the base of different combinations of these ratios which were formed by the taking one ratio from each financial factor such as financial leverage, capital turnover, cash position, etc., 16 z-score models estimated. Unfortunately, there was small compliance for predictability power of these bankruptcy models. On the other hand, we separately used all ratios (for example; X3 – cash/Total Assets, X6 – cash/Sales) classified by the same factor (for X3 and X6, cash position) in different models and found that it doesn’t change the result of the predictability power of the bankruptcy models. Fortunately, this result shows the same pattern with most of the papers in this area.
    Keywords: Kappa test, Altman’s z-score, Edmister’s z-score, predictability power, prediction of bankruptcy
    JEL: G33 C21
    Date: 2017–12–20
  6. By: Osswald, Benjamin; Sureth, Caren
    Abstract: This study examines whether country-specific risk attenuates the association between tax policies and corporate risk-taking. We define country-specific risk (political and fiscal budget risk) as taxpayer's risk that tax refunds on losses cannot be paid due to the institutional environment or fiscal reasons. We exploit a cross-country panel with 234 changes in corporate tax rates and 49 changes in loss offset rules. We investigate whether government risk-sharing via loss offset rules and tax rates affects risk-taking conditional on country risk. We also examine whether tax rate changes, that scale the risk-sharing effect, influence the propensity to conduct risky projects in different country-level risk environments. Our results suggest that country-level risk fully attenuates the previously documented association between tax policies and corporate risk-taking. It attenuates both the effectiveness of loss offset rules and tax rate changes on corporate risk-taking. While changes in tax policy are attractive to policymakers because alternative instruments to encourage risk-taking cannot as easily be adjusted, we provide significant evidence that country risk considerably limits policymakers' ability to induce firm risk-taking via changes in tax policies.
    Keywords: corporate risk-taking,country risk,cross-country study,fiscal risk,risky investments
    JEL: H25 H32 G32
    Date: 2018
  7. By: Halkos, George; Tzirivis, Apostolos
    Abstract: The current study emphasizes on the importance of the development of an effective price risk management strategy regarding energy products, as a result of the high volatility of that particular market. The study provides a thorough investigation of the energy price volatility, through the use of GARCH type model variations and the Markov-Switching GARCH methodology, as they are presented in the most representative academic researches. A large number of GARCH type models are exhibited together with the methodology and all the econometric procedures and tests that are necessary for developing a robust and precise forecasting model regarding energy price volatility. Nevertheless, the present research moves another step forward, in an attempt to cover also the probability of potential shifts in the unconditional variance of the models due to the effect of economic crises and several unexpected geopolitical events into the energy market prices.
    Keywords: Energy commodities, WTI oil, Brent oil, electricity, natural gas, gasoline, risk management, volatility modeling, ARCH-GARCH models, Markov-Switching GARCH models.
    JEL: C01 C58 D8 G3 O13 P28 Q43 Q47 Q58
    Date: 2018–12
  8. By: Lorenzo Danieli; Petr Jakubik (EIOPA)
    Abstract: This article proposes an Early Warning System model composed of macro-financial and company-specific indicators that could help to anticipate a potential market distress in the European insurance sector. A distress is defined as periods in which insurance companies’ equity prices crash and CDS spreads spike simultaneously. The model is estimated using a sample of 43 insurance companies that are listed. Based on a panel binomial logit specification, empirical evidence shows that economic overheating that could be manifested by high economic growth and inflation as well as high interest rates have negative impact on insurance sector stability. At the company level, increasing operating expenses increase the likelihood of distress occurrence.
    Keywords: early warning system, insurance sector, financial distress
    JEL: G01 G12 G22 E44
    Date: 2018–12
  9. By: Gordon V. Chavez
    Abstract: We propose a family of stochastic volatility models that enable direct estimation of time-varying extreme event probabilities in time series with nonlinear dependence and power law tails. The models are a white noise process with conditionally log-Laplace stochastic volatility. In contrast to other, similar stochastic volatility formalisms, this process has an explicit, closed-form expression for its conditional probability density function, which enables straightforward estimation of dynamically changing extreme event probabilities. The process and volatility are conditionally Pareto-tailed, with tail exponent given by the reciprocal of the log-volatility's mean absolute innovation. These models thus can accommodate conditional power law-tail behavior ranging from very weakly non-Gaussian to Cauchy-like tails. Closed-form expressions for the models' conditional polynomial moments also allows for volatility modeling. We provide a straightforward, probabilistic method-of-moments estimation procedure that uses an asymptotic result for the process' conditional large deviation probabilities. We demonstrate the estimator's usefulness with a simulation study. We then give empirical applications to financial time series data, which show that this simple modeling method can be effectively used for dynamic tail inference in nonlinear, heavy-tailed time series.
    Date: 2019–01
  10. By: Balasubramanyan, Lakshmi (Weatherhead School of Management, Case Western Reserve University); Daniel, Naveen (LeBow College of Business, Drexel University); Haubrich, Joseph G. (Federal Reserve Bank of Cleveland); Naveen, Lalitha (Fox School of Business, Temple University)
    Abstract: Our goal is to document the causal impact of having a board-level risk committee (RC) and a management-level executive designated as chief risk officer (CRO) on bank risk. The Dodd Frank Act requires bank holding companies with over $10 billion of assets to have an RC, while those with over $50 billion of assets are additionally required to have a CRO to oversee risk management. The innovation that allows us to document a causal impact is our research design. First, we use the passage of the Dodd Frank Act as a natural experiment that forced noncompliant firms to adopt an RC and appoint a CRO. We adopt the difference-in-difference approach to estimate the change in risk following RC and CRO adoption. Second, we use the regression discontinuity approach centered on the $10 billion and $50 billion thresholds whereby firms that were just below the threshold were not required by the law to install an RC and to recruit a CRO, while those just above the thresholds had to comply with the regulation. Our contribution is to document that neither the RC nor the CRO have a causal impact on risk near these thresholds. However, we do find strong evidence of risk reduction following the passage of the law.
    Keywords: Bank Holding Companies; Risk; Chief Risk Officer; Risk Committee; Dodd Frank Act; Bank Risk;
    JEL: G21 G34 G38
    Date: 2019–01–10

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