Financial Risk Measurement Research Articles

Bankruptcy prediction with financial systemic risk

ABSTRACTFinancial systemic risk – defined as the risk of collapse of an entire financial system vis-à-vis any one individual financial institution – is making inroads into academic research in the aftermath of the late 2000s Global Financial Crisis. We shed light on this new concept by investigating the value of various systemic financial risk measures in the corporate failure predictions of listed nonfinancial firms. Our sample includes 225,813 firm-quarter observations covering 8,604 US firms from 2000 Q1 to 2016 Q4. We find that financial systemic risk is incrementally useful in forecasting corporate failure over and above the predictions of the traditional accounting-based and market-based factors. Our results are stronger when the firm in consideration has higher equity volatility relative to financial sector volatility, smaller size relative to the market, and more debts in current liabilities. The combined evidence suggests that systemic risk is a useful supplementary source of information in capital markets.

Financial systemic risk measurement based on causal network connectedness analysis

We construct the causal complex network of financial institution based on the Granger-causality network as well as the principal component analysis, and further analyze the network topology structure characteristics using centrality indicators. From the perspective of time dimension and space dimension, the causal complex network among financial institutions is analyzed with the dynamic variation of systemic risk measured. One improvement is that the CAPM is employed to filter out the market risk. Moreover, we study the contribution of individual financial firm to the systemic risk through three well known systemic risk measures containing the systemic risk index (SRISK), the marginal expected shortfall (MES) and the conditional Value at Risk (CoVaR). The proposed methodology framework is applied to the banks, securities and insurance companies in Chinese financial markets. Empirical researches find that the causal network of Chinese financial entities possesses small world and scale free properties, with the number of connections increasing dramatically in turmoil periods, indicating stronger interconnectedness in the financial system during crisis. Moreover, the network based connectedness measures through topology structure analysis can identify and quantify the financial crisis, serving as significant systemic risk indicators. In addition, different systemic risk measures employed in our framework can identify similar rankings of risky financial firms quantitatively, which provide references for regulations.

Financial risk measures for a network of individual agents holding portfolios of light-tailed objects

We investigate a financial network of agents holding portfolios of independent light-tailed risky objects whose losses are asymptotically exponentially distributed with distinct tail parameters. We show that the asymptotic distributions of portfolio losses belong to the class of functional exponential mixtures which we introduce in this paper. We also provide results for value-at-risk and expected shortfall risk measures, as well as for their conditional counterparts. Compared to heavy-tail settings, we establish important qualitative differences in the asymptotic behaviour of portfolio risks under a light-tail assumption which have to be accounted for in practical risk management.

Controlling risks to ensure financial stability and reducing volatility

AbstractThe 11th International Risk Management Conference was held at the University of Paris‐Dauphine and the EM LYON Business School (Paris Campus) in June 2018. The conference theme “1968–2018: From Z‐Score to Contemporary Risk Management, 50 Years of Risk Measurement and Management” was selected as a means to showcase the many ways that financial risk measurement has evolved and impacted financial risk management in banks and corporations as well as the tools used by asset managers and market regulators. The four papers included in this special issue of the Journal of International Financial Management and Accounting focus on a wide range of issues: How small differences in credit ratings effect the speed of adjustment in corporate leverage, the impact of using soft variables to assess default probabilities in European corporations, a new approach to measuring a company's intrinsic risk, and the effectiveness of short sale circuit breaker rules to limit stock price volatility. Our Foreword highlights the main findings of these papers and offers our assessment of the lessons they provide for financial managers and policymakers.

Three regime bivariate normal distribution: a new estimation method for co-value-at-risk, CoVaR

We propose a new distribution for estimation of co-value-at-risk, CoVaR, a financial system risk measure conditional on an institution in a financial distress: a three regime bivariate normal (3RN) distribution which is composed of three bivariate normal distributions with asymmetric variance matrices for the right-tail, left-tail and mid-part corresponding to the return of an institution. The distribution captures explicitly the asymmetric correlation of system return and institution return: usually stronger for bad times than for good times. The 3RN distribution allows simple evaluations of the CoVaR taking full advantage of asymmetric correlation. An implementation for the quasi maximum likelihood estimator (QMLE) is provided. The proposed estimation method is applied to stock price data sets consisting of one financial system and four financial institutions: the US S&P 500 index, Bank of America Corporation, JP Morgan Chase & Co., Goldman Sachs Group, Inc. and Citigroup Inc. The data analysis shows that the proposed method has better in-sample and out-of-sample violation performance than existing methods and some other possible candidates.

MACHINE LEARNING METHODS FOR SYSTEMIC RISK ANALYSIS IN FINANCIAL SECTORS

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.

Microeconomic Costs, Insurance, and Catastrophic Health Spending Among Patients With Acute Myocardial Infarction in India

Ischemic heart disease is the leading cause of death in India, and treatment can be costly. To evaluate individual- and household-level costs and impoverishing effects of acute myocardial infarction among patients in Kerala, India. This investigation was a prespecified substudy of the Acute Coronary Syndrome Quality Improvement in Kerala study, a stepped-wedge, cluster randomized clinical trial conducted between November 2014 and November 2016 across 63 hospitals in Kerala, India. In this cross-sectional substudy, individual- and household-level cost data were collected 30 days after hospital discharge from a sample of 2114 respondents from November 2014 to July 2016. Data were analyzed from July through October 2018 and in March 2019. Health insurance status. The primary outcomes were detailed direct and indirect cost data associated with acute myocardial infarction and respondent ability to pay as well as catastrophic health spending and distress financing. Catastrophic health spending was defined as 40% or more of household expenditures minus food costs spent on health, and distress financing was defined as borrowing money or selling assets to cover health costs. Hierarchical regression models were used to evaluate the association between health insurance and measures of financial risk. Costs were converted from Indian rupees to international dollars (represented herein as "$"). Among 2114 respondents, the mean (SD) age was 62.3 (12.7) years, 1521 (71.9%) were men, 1144 (54.1%) presented with an ST-segment elevation myocardial infarction, and 1600 (75.7%) had no health insurance. The median (interquartile range) expenditure among respondents was $480.4 ($112.5-$1733.0) per acute myocardial infarction encounter, largely driven by in-hospital expenditures. There was greater than 15-fold variability between the 25th and 75th percentiles. Individuals with or without health insurance had similar monthly incomes and annual household expenditures, yet individuals without health insurance had approximately $400 higher out-of-pocket cardiovascular health care costs (median [interquartile range] total cardiovascular expenditures among uninsured, $560.3 [$134.1-$1733.6] vs insured, $161.4 [$23.2-$1726.9]; P < .001). Individuals without health insurance also had a 24% higher risk of catastrophic health spending (adjusted risk ratio, 1.24; 95% CI, 1.07-1.43) and 3-fold higher risk of distress financing (adjusted risk ratio; 3.05; 95% CI, 1.45-6.44). The results of this study indicate that acute myocardial infarction carries substantial financial risk for patients in Kerala. Expansion of health insurance may be an important strategy for financial risk protection to disrupt the poverty cycle associated with cardiovascular diseases in India.

Analysis on the Early Warning Index System of Financial Risk of New Energy Enterprises

Financial risk measurement, as the core content of enterprise financial risk management, plays a vital role in preventing financial risks. Compared with other industries and traditional energy industries, new energy enterprises have their own characteristics and financial risks, which cannot neglect the development of new energy enterprises. Combined with the characteristics of new energy enterprises, this paper constructs financial risk early warning indicators that meet the characteristics of new energy enterprises.

Estimation of value-at-risk using single index quantile regression

ABSTRACTValue-at-Risk (VaR) is one of the best known and most heavily used measures of financial risk. In this paper, we introduce a non-iterative semiparametric model for VaR estimation called the single index quantile regression time series (SIQRTS) model. To test its performance, we give an application to four major US market indices: the S&P 500 Index, the Russell 2000 Index, the Dow Jones Industrial Average, and the NASDAQ Composite Index. Our results suggest that this method has a good finite sample performance and often outperforms a number of commonly used methods.

Evolución de la medición del riesgo financiero en los últimos 40 años: una panorámica con especial mención en la banca

El objetivo de este artículo es ofrecer una panorámica de la evolución de la medición y gestión del riesgo financiero en los últimos 40 años, con especial mención a la banca. Tras una etapa basada en los principios de la Modern Portfolio Theory (MPT), hace 25 años se produce una revolución con la introducción del Value-at-Risk (VaR). Desde entonces, la introducción de nuevas medidas cuantitativas, con complejidad matemática creciente, no se ha detenido, en una interacción continua entre académicos, profesionales y reguladores, como respuesta a las sucesivas crisis financieras y bancarias. Entre ellas, destacan las medidas coherentes del riesgo (concretamente el Expected Shortfall), espectrales y basadas en expectiles. Se concluye que el VaR y el Expected Shortfall (ES) continúan siendo, a pesar de sus limitaciones, las dos medidas más utilizadas tanto desde el punto de vista interno de los bancos, como por parte del regulador y supervisor de su solvencia. Finalmente, se plantean algunas de las líneas de investigación en este campo que tratan de abordar los retos en el futuro de la medición del riesgo financiero en banca.

Bayesian Risk Forecasting for Long Horizons

We present an accurate and efficient method for Bayesian forecasting of two financial risk measures, Value-at-Risk and Expected Shortfall, for a given volatility model. We obtain precise forecasts of the tail of the distribution of returns not only for the 10-days-ahead horizon required by the Basel Committee but even for long horizons, like one-month or one-year-ahead. The latter has recently attracted considerable attention due to the different properties of short term risk and long run risk. The key insight behind our importance sampling based approach is the sequential construction of marginal and conditional importance densities for consecutive periods. We report substantial accuracy gains for all the considered horizons in empirical studies on two datasets of daily financial returns, including a highly volatile period of the recent financial crisis. To illustrate the flexibility of the proposed construction method, we present how it can be adjusted to the frequentist case, for which we provide counterparts of both Bayesian applications.

Political Identity and Financial Risk Taking: Insights from Social Dominance Orientation

This article investigates how people’s political identity is associated with their financial risk taking. The authors argue that conservatives’ financial risk taking increases as their self-efficacy increases because of their greater social dominance orientation, whereas liberals’ financial risk taking is invariant to their self-efficacy. This central hypothesis is verified in six studies using different measures of political identity, self-efficacy, and financial risk taking. The studies also use different samples of U.S. consumers, including online panels, a large-scale data set spanning five election cycles, and a secondary data set of political donations made by managers at companies. Finally, the authors articulate and demonstrate the mediating effect of individuals’ focus on the upside potential of a decision among conservatives but not liberals.

Hedging parameter risk

Abstract The accurate measurement and effective control of financial risk are of crucial importance to risk managers and regulators. However, risk measures are potentially affected by errors in the estimation of model parameters from limited samples, leading to parameter risk. The key contribution of this paper is the formulation of a general framework to hedge this parameter risk. Applying the new framework to credit portfolio modeling, we highlight the importance of parameter risk, estimation methods, and diversification effects.

Forecasting economic time series using score-driven dynamic models with mixed-data sampling

We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of low-frequency time series variables through the use of timely information from high-frequency variables. We verify the in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation and GDP growth. In particular, we forecast monthly headline inflation using daily oil prices and quarterly GDP growth using a measure of financial risk. The forecasting results and other findings are promising. Our proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of both point and density forecasts.

Auditor sanctions and the management of riskiness of audit client portfolios

This study examines the effect of auditor sanctions imposed on the riskiness of auditors' client portfolios. We identify auditors/audit firms that were subject to disciplinary actions by the China Securities Regulatory Commission (CSRC) and empirically examine the change in the riskiness of their client portfolios after the sanctions were imposed. The empirical results suggest a decrease in the overall clientele financial risk, measured by both aggregate bankruptcy measures and individual financial risk measures, after enforcement releases were issued. Moreover, we find that the decrease in client portfolios' risk is greater when a disciplinary action is imposed in the period of higher legal liability than when it is imposed in the period of lower legal liability. This study contributes to prior studies on the effects of audit failure on auditors' behaviour by examining the association between auditor sanctions and the management of audit client portfolios risk.

Partially Censored Posterior for Robust and Efficient Risk Evaluation

A novel approach to inference for a specific region of the predictive distribution is introduced. An important domain of application is accurate prediction of financial risk measures, where the area of interest is the left tail of the predictive density of logreturns. Our proposed approach originates from the Bayesian approach to parameter estimation and time series forecasting, however it is robust in the sense that it provides a more accurate estimation of the predictive density in the region of interest in case of misspecification. The first main contribution of the paper is the novel concept of the Partially Censored Posterior (PCP), where the set of model parameters is partitioned into two subsets: for the first subset of parameters we consider the standard marginal posterior, for the second subset of parameters (that are particularly related to the region of interest) we consider the conditional censored posterior. The censoring means that observations outside the region of interest are censored: for those observations only the probability of being outside the region of interest matters. This quasi-Bayesian approach yields more precise parameter estimation than a fully censored posterior for all parameters, and has more focus on the region of interest than a standard Bayesian approach. The second main contribution is that we introduce two novel methods for computationally efficient simulation: Conditional MitISEM, a Markov chain Monte Carlo method to simulate model parameters from the Partially Censored Posterior, and PCP-QERMit, an Importance Sampling method that is introduced to further decrease the numerical standard errors of the Value-at-Risk and Expected Shortfall estimators. The third main contribution is that we consider the effect of using a time-varying boundary of the region of interest, which may provide more information about the left tail of the distribution of the standardized innovations. Extensive simulation and empirical studies show the ability of the introduced method to outperform standard approaches.

The effect of organizational studies on financial risk measures estimation

Purpose – The objective of this research is to establish a link between risk models and the paradigms of organizational studies. Design/methodology/approach – To achieve this goal, a discussion about risk in organizations was presented, based on organizational studies. Additionally, an illustration was provided to evaluate how organizational paradigms influence risk models. Findings – There are three main organizational perspectives: Modernist, Postmodernist, and Neo-modernist. Based on the empirical analysis, it was observed that the use of unrealistic assumptions (Modernist perspective) in risk management increases model risk, and is thus not suitable for risk model estimation. However, the absolute lack of measurement of the Postmodernist paradigm can be too radical in the sense that, in the practical field, there is a crucial need for quantitative information to enable financial institutions and investors to protect their investments. Thus, it was found that the solution is the Neo-modernist paradigm, which employs more realistic assumptions about data behavior. Originality/value – The main contribution of this research is to raise the influence of other attributes of financial risk estimation that go beyond mathematical and statistical grounds. Previous studies do not relate the traditional literature on financial risk management estimation with literature focused on the sociological issues of organizational studies. Hence, this study can help build a bridge for future integration between these two related research subjects.

Bankruptcy Prediction with Financial Systemic Risk

Financial systemic risk – defined as the risk of collapse of an entire financial system vis-a-vis any one individual financial institution – is making inroads into academic research in the aftermath of the late 2000s Global Financial Crisis. We shed light on this new concept by investigating the value of various systemic financial risk measures in the corporate failure predictions of listed nonfinancial firms. Our sample includes 225,813 firm-quarter observations covering 8,604 US firms from 2000 Q1 to 2016 Q4. We find that financial systemic risk is incrementally useful in forecasting corporate failure over and above the predictions of the traditional accounting-based and market-based factors. Our results are stronger when the firm in consideration has higher equity volatility relative to financial sector volatility, the smaller size relative to the market, and more debts in current liabilities. The combined evidence suggests that systemic risk is a useful supplementary source of information in capital markets.

Twitter versus Traditional News Media: Evidence for the Sovereign Bond Markets

This paper compares news in Twitter with traditional news outlets and then emphasizes their differential impact on Eurozone's sovereign bond market for a homogeneous news topic. We find a two-way information flow between Twitter's “Grexit” tweets and the respective mentions in traditional news outlets. The influence of Twitter on the traditional news is consistently more prolonged, especially in high-activity periods. We also assess the differential impact of the two news sources on sovereign spreads over and above the impact of economic/financial fundamentals, namely measures of default risk, liquidity risk and global financial risk. Our focus is on the borrowing costs of Eurozone's periphery; for comparison reasons, we also consider France as a core Eurozone country. The effect of Twitter on the Greek sovereign spread is positive and of higher magnitude than that of traditional news outlets. Weak contagion effects are recorded primarily for the case of Portugal and Ireland.

In Search of a Better Volatility

We derive an alternative volatility index from options on E-Mini S&P 500 futures and compare it with the VIX to see which index could provide a more efficient measure of volatility and risk. VCME, our alternative volatility measure, and the VIX are very similar in price and trend, are quite efficient at forecasting future volatility in the short-term, but lose their effectiveness over longer periods of time. We do not find any meaningful relationship with volatility and future stock returns. However, we propose that VCME may be more attractive to a financial institution seeking responsive risk measures, while on average generating less deviations from actual volatility at any time frame from 1 to 21 days forward.