Risk Measurement Methods Research Articles

Risk Spillover Effect of Chinese Commercial Banks: Based on Indicator Method and CoVaR Approach

This paper took the thirteen listed commercial banks in China as the research objects, and used the financial risk measurement method CoVaR to study the risk spillover effect of commercial banks. Firstly, we employed an indicator-method to study the systemic importance changes of the listed banks in the normal and risk periods. It is found that the systemic importance is always closely related to the size of bank’s assets. The application of Cluster Analysis on the indicator score showed that they were divided into two part: four state-owned banks and nine joint-stock banks groups. From the perspective of financial market risk spillover effect caused by relevance, the risk contributions of each bank to the bank groups were calculated by using the quantile regression and CoVaR approaches in two time periods. The results showed that not only the systemically important banks with big asset scale had great risk spillover effect, in the risk period, some of the smaller scale banks had a significant increase in the risk contribution, which would also become an important source of risk. From the normal period to risk period, the risk spillover effects of each bank were obviously enhanced, and the risk contagion between banks rose generally. Therefore, regulators should not only focus on large-scale banks, but also pay attention to those banks with high risk contributions.

Liquidity Forms and Bank Size

The article deals with relationship between bank liquidity and variables representing the size of banks – such a total assets, gross volume of loans and clients deposits. For higher complexity, multiple dependent variables are used. The values are calculated based on a specific method of liquidity risk measurement – gross liquidity flows. To determine the possible relations the robust panel regression analysis together with the time series analysis are performed. The differences have been showed not just among different size groups but also among the same size groups in the different banking sectors.

Alternative Risk Measurement Methods: Theoretical Aspects of the Margin of Safety


 
 Investment risk measurement using only quantitative methods does not always work properly measuring risk and avoiding losses. In order to effectively measure and reduce risk it is appropriate to use alternative methods for risk measurement. This paper discusses the safety limit, which applies as the method of risk measurement and reduction. Review of empirical studies has shown that B. Graham’s proposed fundamental criteria to measure and reduce risk improve investment portfolio returns and efficiency.
 

Introducing cybernomics: A unifying economic framework for measuring cyber risk

This is the first in a series of papers on the risk measures and unifying economic framework encompassing the cross-disciplinary field of “Cybernomics”. This is also the first academic paper to formally propose measurement units for cyber risk. In this paper, multidisciplinary methodologies are used to apply proven risk measurement methods in finance and medicine to define novel risk units central to cybernomics. Leveraging established risk units – MicroMort (MM) for measuring medical risk and Value-at-Risk (VaR) for measuring market risk – BitMort (BM) and hekla (named after an Icelandic volcano) are defined as cyber risk units. Risk calculation methods and examples are introduced in this paper to measure cost-effectiveness of control factors, articulate an entity's “willingness-to-pay” (risk pricing) for cyber risk reduction, cyber risk limit, and cyber risk appetite. Built around BM and hekla, cybernomics integrates cyber risk management and economics to study the requirements of a databank in order to improve risk analytics solutions for: 1) the valuation of digital assets; 2) the measurement of risk exposure of digital assets; and 3) the capital optimization for managing residual cyber risk. Establishing adequate, holistic and statistically robust data points on the entity, portfolio and global levels for the development of a cybernomics databank are essential for the resilience of our shared digital future. This paper explains the need to establish data schemes such as International Digital Asset Classification (IDAC) and International Classification of Cyber Incidents (ICCI).

Stock Market Risk Measurement Method Based on Improved Genetic Algorithm

Stock Market Risk Measurement Method Based on Improved Genetic Algorithm

AN IMPROVED APPROACH TO EVALUATE DEFAULT PROBABILITIES AND DEFAULT CORRELATIONS WITH CONSISTENCY

We provide (i) a simplified analytic closed form formula for evaluating joint default probability, (ii) an improved method to resolve the inconsistency between the univariate process underlying firm-specific default probability and the correlated bivariate process of the first-passage-time default correlation model, (iii) illustration of risk management implications from misspecification of the default state space. Our closed form formula provides a natural extension of previous structural first-passage-time models and shows the sensitivities of default correlation numerically with respect to the underlying asset correlation, obligor credit quality and time horizon. We emphasize the disparate credit risk management implications of our result in contrast to commonly used risk measurement methods.

Probability of Default and Default Correlations

We consider a system where the asset values of firms are correlated with the default thresholds. We first evaluate the probability of default of a single firm under the correlated assets assumptions. This extends Merton’s probability of default of a single firm under the independent asset values assumption. At any time, the distance-to-default for a single firm is derived in the system, and this distance-to-default should provide a different measure for credit rating with the correlated asset values into consideration. Then we derive a closed formula for the joint default probability and a general closed formula for the default correlation via the correlated multivariate process of the first-passage-time default correlation model. Our structural model encodes the sensitivities of default correlations with respect to the underlying correlation among firms’ asset values. We propose the disparate credit risk management from our result in contrast to the commonly used risk measurement methods considering default correlations into consideration.

Liquidity Determinants of the Selected Banking Sectors and their Size Groups

The article focuses on the factors affecting the liquidity of selected bank sectors, as well as their size groups, using panel regression analysis. For higher complexity of the results, multiple dependent variables are used: liquidity creation, outflow and net change. The values are calculated based on the specific method of liquidity risk measurement – gross liquidity flows. The results indicate both multiple effects of some factors on the given variables, as well as isolated influence of factors on a single liquidity form or size group. Thus, when looking for determinants using just one form of liquidity, such as creation, the results need not necessarily comprehensively show the influence of the given factors, and can lead to erroneous conclusions. The results also point to the differing behaviours of the size groups and their different sensitivity on the factors; smaller banks have shown higher sensitivity on macroeconomic variables. Higher flexibility in regulation could lead to higher optimization.

Quantile Regression Model for Measurement of Equity Portfolio Risk a Case Study of Nairobi Securities Exchange

Quantile regression provides a method of estimating quantiles from a conditional distribution density. It is achieves this by minimizing asymmetrically weighted sum of absolute errors thus partitioning the conditional distribution into quantiles. Lower conditional quantiles are of interest in estimation of Value-at-Risk because they indicate downward movement of financial returns. Current risk measurement methods do not effectively estimate the VaR since they make assumptions in the distribution tails. Financial data is sampled frequently leading to a heavier tailed distribution compared to a normal and student t distribution. A remedy to this is to use a method that does not make assumptions in the tail distribution of financial returns. Little research has been done on the usage of quantile regression in the estimation of portfolio risk in the Nairobi Securities Exchange. The main aim of this study was to model the portfolio risk as a lower conditional quantile, compare the performance of this model to the existing risk measurement methods and to predict the Value-at-Risk. This study presents summary of key findings and conclusion drawn from the study. From the fitted conditional quantile GARCH model 62.4% of VaR can be explained by past standard deviation and absolute residual of NSE 20 share index optimal portfolio returns. The fitted model had less proportion of failure of 7.65% compared to commonly used VaR models.

Kalkulacja wymogu kapitałowego z tytułu ryzyka operacyjnego w zgodzie z dyrektywą w sprawie wymogów kapitałowych - CRD IV

Operational risk is defined as the possibility of losses resulting from the failure, deficiency or inadequacy of internal processes, people and systems, or from external events. Operational risk measurement methods differ from techniques applied to other types of risk. The importance of operational risk has increased substantially in recent years due to huge losses suffered by renowned financial institutions. These led to the publication of the European Union Capital Requirements Directive (CRD), introduced by the so-called Basel II-based definition of banks' capital requirements. The main objective of the paper is to analyse the operational risk measurement methods proposed in the New Basel Capital Accord: the Basic Indicator Approach, the Standardised Approach, and Advanced Measurement Approaches (AMA). The author analysed, in successive stages of the paper, the definition and characteristics of operational risk and methods of calculating capital requirements.

A Comparative Analysis of Quantitative Operational Risk Measurement Methods

The number of well-known firms joining the list of entities such as Enron, WorldCom, Sumitomo Corp., all of which lost millions of dollars as a result of inadequate operational risk management systems, increases dramatically with each passing year. This has increased the amount of interest in this marginalised aspect of risk. The quantitative estimation and measurement of operational risk proved necessary with the implementation of new operational risk management strategies in companies and financial institutions. Basel Committee on Banking Supervision recommendations, which request banks to use quantitative methods in their operational risk management are an additional motivation to use quantitative methods of estimating operational risk. This article reviews the most important quantitative methods of estimating and measuring operational risk.

Simulation of Traffic Flow in Hurricane Emergency Evacuation Use Dynamic Control Strategy

Based on the hurricane evacuation process, the dynamic characteristics of hurricane disaster risk were analyzed. The degree of harm and endanger regional are the two aspects of dynamic characteristics of hurricane disaster risk and by use the hurricane center and hurricane levels, the risk measurement method for a given point was proposed. The new emergency evacuation strategy was based on the largest decline of unit risk which was also the main idea of vehicle steering decision function. By the hurricane evacuation examples, the case that traffic net with traffic bottlenecks was studied by simulation. The results show that the proposed hurricane emergency evacuation strategy of this paper is feasible and have practical implications.

Analysis of Credit Risk Measurement Models in the Evaluation of Credit Demands

The mission of the loan associations is simply to convert deposits collected with lower interest rates, into credits with higher interest rates. However, return of this cycle involves high potential of risk. In order to minimize the risk, several financial models are utilized. Since these models are insufficient, new models are required to be developed. The purpose of this study is to compile literature reviews on credit risk measurement methods used for assessment of credit demands. The method used is literature scan. This scan was conducted with printed publications and articles from the databases scanned. The information is compiled from these sources. It is discussed that risk mitigation should be given importance instead of guarantee, for evaluation of credit demands with Basel II criteria. A credit requesting entity's future prediction by examining financial statements can only be possible utilizing financial statements analysis techniques (comparative financial statements analysis, vertical analysis, ratio analysis and cash flow statements) together. As a consequence, in 2009 a software ,named Scoring Model has been developed for giving credit decisions which uses the techniques together mentioned above. This software merely based on a model is proposed to be integrated into banking system after being reprogrammed as commercial software.

Optimizing the technical benefits of the insurance company using genetic algorithms

In this paper, we present a new method for dynamic optimization to achieve maximum technical benefit to the insurer using genetic algorithms. The objective of this work is to select, from a database containing forms of reinsurance, pricing models and credit ratings parameters (risk measurement methods, such as Value at Risk (VaR), Conditional Value at Risk (CVaR) or ruin probability) a form of reinsurance, a way of pricing and a solvency parameter to maximize technical benefits of the insurance company.

Building Safety Road Maps Based on Difference of Judgment of Road Users by their Smartphone

Recently, there has been a growing demand and interest in developing methods for analyzing smartphone logs to extract traffic safety information. Because the log is high time resolution and closely related to user activities but fragmentary and myopic, it is difficult for currently available collision probability based quantitative risk assessment methods to create traffic safety maps automatically from the driving log which require all of concrete information about a collision for example, size of vehicle, speed of pedestrian. This paper proposes a computable risk measurement method for building traffic safety maps with the logs of different users' driving, which does not discuss collision probability. The proposal is designed to compute differences in the recognition of the road environment among road users mathematically. Drivers differ in their recognition, judgment, and handling of a given situation. Suppose that a difference in recognition among users in the same situation is a signal of danger. This signal is easy to calculate by Poisson process. Each user's recognition of road environment and the safety map integrated from the collection of the recognition are generated fully automated. A real-world experiment was carried out, and the results show that the assumption and the proposed method succeeded in generating an accurate and effective traffic safety map.

A Decision Support Approach for Accounts Receivable Risk Management

Financial disasters in private firms led to increased emphasis on various forms of risk management, to include market risk management, operational risk management, and credit risk management. Financial institutions are motivated by the need to meet increased regulatory requirements for risk measurement and capital reserves. This paper describes and demonstrates a model to support risk management of accounts receivable. We present a decision support model for a large bank enabling assessment of risk of default on the part of loan recipients. A credit scoring model is presented to assess account creditworthiness. Alternative methods of risk measurement for fault detection are compared, and a logistic regression model selected to analyze accounts receivable risk. Accuracy results of this model are presented, enabling accounts receivable managers to confidently apply statistical analysis through data mining to manage their risk.

Operational risk: Emerging markets, sectors and measurement

The role of decision support systems in mitigating operational risks in firms is well established. However, there is a lack of investment in decision support systems in emerging markets, even though inadequate operational risk management is a key cause of discouraging external investment. This has also been exacerbated by insufficient understanding of operational risk in emerging markets, which can be attributed to past operational risk measurement techniques, limited studies on emerging markets and inadequate data.In this paper, using current operational risk techniques, the operational risk of developed and emerging market firms is measured for 100 different companies, for 4 different industry sectors and 5 different countries. Firstly, it is found that operational risk is consistently higher in emerging market firms than in the developed markets. Secondly, it is found that operational risk is not only dependent upon the industry sector but also that market development is the more dominant factor. Thirdly, it is found that the market development and the sector influence the shape of the operational risk distribution, in particular tail and skewness risk. Furthermore, an operational risk measurement method is provided that is applicable to emerging markets. Our results are consistent with under investment in decision support systems in emerging markets and imply operational risk management can be improved by increased investment.

Stackelberg game of buyback policy in supply chain with a risk-averse retailer and a risk-averse supplier based on CVaR.

This paper considers a decentralized supply chain in which a single supplier sells a perishable product to a single retailer facing uncertain demand. We assume that the supplier and the retailer are both risk averse and utilize Conditional Value at Risk (CVaR), a risk measure method which is popularized in financial risk management, to estimate their risk attitude. We establish a buyback policy model based on Stackelberg game theory under considering supply chain members' risk preference and get the expressions of the supplier's optimal repurchase price and the retailer's optimal order quantity which are compared with those under risk neutral case. Finally, a numerical example is applied to simulate that model and prove related conclusions.

An Information-Entropy-based Risk Measurement Method of Software Development Project

Risk is the major reason for the failure of software development projects. The risk measurement can provide the decision-making data for the risk management and control. The traditional risk measurement method usually assesses in terms of the occurrence probability and the loss of the risk factors, which usually are assessed by experts subjectively. Therefore, they are greatly influenced by artificial factors. In this way, it is difficult to realize the objective and effective measurement. This paper proposes one risk measurement method of software development project based on information entropy, which makes up for the shortcomings of the former studies. This method makes use of information entropy to measure the amount of information so as to measure the software development project risk. In this paper, a new risk checklist is given. First of all, it obtains the risk information through the checklist. Then it obtains the information amount of risk factors by aid of information entropy. This paper concludes that the larger the information amount of the risk factors is, the larger the removed uncertainty is, the smaller the information entropy is and the smaller the risk is. In short, there is a positive correlation between risk and entropy. This paper demonstrates the scientificity and rationality of the method theoretically. Compared with other risk measurement methods, this method manifests certain advantage. The case analysis prove the rationality and feasibility of this method.

The long‐term extreme price risk measure of portfolio in inventory financing: An application to dynamic impawn rate interval

Different from the short‐term risk measure for traditional financial assets (stocks, bonds, etc.), the key to illiquid inventory portfolio traded in the over‐the‐counter markets is to estimate the long‐term extreme price risk with time varying volatility. In this article, a new long‐term extreme price risk (value at risk and conditional value at risk) measure method for inventory portfolio and an application to dynamic impawn rate interval are proposed. To realize this, we first establish AutoRegressive Moving Average‐Exponential Generalized Autoregressive Conditional Heteroskedasticity‐Extreme Value Theory model and multivariatet‐Copula to depict the autocorrelation, fat tails, and volatility clustering of returns of inventories and the nonlinear dependence structure of inventories. Furthermore, we obtain the long‐term extreme price risk with time varying volatility via Monte Carlo simulation instead of square‐root‐of time rule. The results show that, first, benefits from risk diversification is significant; second, long‐term extreme price risk measure of inventory portfolio via Monte Carlo method outperforms the square‐root‐of time rule; the last is that the dynamic rate interval based on the long‐term price risk is superior to the crude rules of thumb in terms of reducing efficiency loss and improving risk coverage. In summary, this article provides a new quantitative framework for managing the risk of portfolio in inventory financing practice for banks constrained by risk limitation. © 2014 Wiley Periodicals, Inc. Complexity 20: 17–34, 2015