Assessing Financial Model Risk

Model risk has a huge impact on any risk measurement procedure and its quantification is therefore a crucial step. In this paper, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the relative measure of model risk and the local measure of model risk. Each of the measures has a specific purpose and so allows for flexibility. We illustrate the various notions by studying some relevant examples, so as to emphasize the practicability and tractability of our approach.Comment: 23 pages, 6 figure

How Risky Is the Value at Risk?

The recent financial crisis has raised numerous questions about the accuracy of value-at-risk (VaR) as a tool to quantify extreme losses. In this paper we present empirical evidence from assessing the out-of-sample performance and robustness of VaR before and during the recent financial crisis with respect to the choice of sampling window, return distributional assumptions and stochastic properties of the underlying financial assets. Moreover we develop a new data driven approach that is based on the principle of optimal combination and that provides robust and precise VaR forecasts for periods when they are needed most, such as the recent financial crisis.Value at Risk, model risk, optimal forecast combination

Not Everything that Counts Can be Counted: A Critical Look at Risk Ratings and Governance Indicators

Accurately evaluating country risk and assessing the quality of governance in emerging market economies has become a priority of international corporations, investment banks and multilateral financial institutions. The rating system of the Political Risk Services (PRS) Group, the International Country Risk Guide (ICRG), constitutes one of the most influential time-series databases of country risk analysis. This study assesses the accuracy and predictive powers of the ICRG model, evaluating its ability to discern trends and highlight structural vulnerabilities, and thus to warn of impending crises. Three major crises are examined: the Brazilian financial crisis of 1999, the Argentine economic meltdown in December 2001 and the Peruvian political crisis of 2000. The study finds mixed results, which have important implications for research and policy.

Clearing algorithms and network centrality

I show that the solution of a standard clearing model commonly used in contagion analyses for financial systems can be expressed as a specific form of a generalized Katz centrality measure under conditions that correspond to a system-wide shock. This result provides a formal explanation for earlier empirical results which showed that Katz-type centrality measures are closely related to contagiousness. It also allows assessing the assumptions that one is making when using such centrality measures as systemic risk indicators. I conclude that these assumptions should be considered too strong and that, from a theoretical perspective, clearing models should be given preference over centrality measures in systemic risk analyses

Effects Of A Decision Aid For The Assessment Of Fraudulent Financial Reporting: An Application Of SAS No. 82

The Statement on Auditing Standards (SAS) No.82, Consideration of Fraud in a Financial Statement Audit, requires the auditor to assess the risk of material misstatement due to a fraud and to consider the assessment in designing appropriate audit procedures to be performed. The SAS No. 82 has thus explicitly made the detection of material fraud the auditor’s responsibility. The purpose of the study is to use the risk factors identified in SAS No. 82 as the foundation to develop a decision aid to help auditors assess the likelihood of fraudulent financial reporting and to empirically test the effects of the decision aid on assessing the likelihood of fraudulent financial reporting. Using a sample of 45 fraud engagements and 206 nonfraud engagements, we developed and tested a logistic regression model that estimates the likelihood of fraudulent financial reporting. We found that the logistic model (proxied as a decision aid in the study) outperforms the practicing auditors in assessing risk for fraud and nonfraud cases

A New Framework for Analyzing and Managing Macrofinancial Risks of an Economy

The high cost of international economic and financial crises highlights the need for a comprehensive framework to assess the robustness of national economic and financial systems. This paper proposes a new comprehensive approach to measure, analyze, and manage macroeconomic risk based on the theory and practice of modern contingent claims analysis (CCA). We illustrate how to use the CCA approach to model and measure sectoral and national risk exposures, and analyze policies to offset their potentially harmful effects. This new framework provides economic balance sheets for inter-linked sectors and a risk accounting framework for an economy. CCA provides a natural framework for analysis of mismatches between an entity's assets and liabilities, such as currency and maturity mismatches on balance sheets. Policies or actions that reduce these mismatches will help reduce risk and vulnerability. It also provides a new framework for sovereign capital structure analysis. It is useful for assessing vulnerability, policy analysis, risk management, investment analysis, and design of risk control strategies. Both public and private sector participants can benefit from pursuing ways to facilitate more efficient macro risk accounting, improve price and volatility discovery, and expand international risk intermediation activities.

Assessment of Financial Risk and Its Impact on an Informal Finance Institutions Profitability

This study examined the connection between financial risk and the profitability of informal financial institutions in Ondo state, Nigeria. Theory assumes risk to have a negative relationship with profitability; however, some studies have proved otherwise. This study used Anuoluwapo cooperative society located in Akure, over a quarterly 5-year period spanning 2015Q1 to 2020Q4. To assess the relationship between financial risk & profitability, the study employed the Pearson correlation to analyse the level of correlation. In assessing the relationship between financial risk & profitability, a data regression model was also used. The correlation coefficients for the variables were positive (+1) & negative (-1). The significance showing a clear indication that there is a strong correlation between financial risk & profitability in Anuoluwapo Cooperative Society. The data regression model shows that P value (0.00) is greater than 0.05; there is an insignificant but positive relationship between the profitability & the financial risk of Anuoluwapo Cooperative Society. This implies that the test considered the random effect model as the most appropriate estimator. The study found out that a unit increase in financial risk would lead to an increase in profitability. From the finding, the study concludes that financial risk positively affects profitability of Anuoluwapo Cooperative Society. The study suggests that since a high level of risk, yield high returns, the process of dealing with risk should be continuous & developing with time

Calculating Value-at-Risk

The market risk of a portfolio refers to the possibility of financial loss due to the joint movement of systematic economic variables such as interest and exchange rates. Quantifying market risk is important to regulators in assessing solvency and to risk managers in allocating scarce capital. Moreover, market risk is often the central risk faced by financial institutions. The standard method for measuring market risk places a conservative, one-sided confidence interval on portfolio losses for short forecast horizons. This bound on losses is often called capital-at-risk or value-at-risk (VAR), for obvious reasons. Calculating the VAR or any similar risk metric requires a probability distribution of changes in portfolio value. In most risk management models, this distribution is derived by placing assumptions on (1) how the portfolio function is approximated, and (2) how the state variables are modeled. Using this framework, we first review four methods for measuring market risk. We then develop and illustrate two new market risk measurement models that use a second-order approximation to the portfolio function and a multivariate GARCH(l,1) model for the state variables. We show that when changes in the state variables are modeled as conditional or unconditional multivariate normal, first-order approximations to the portfolio function yield a univariate normal for the change in portfolio value while second-order approximations yield a quadratic normal. Using equity return data and a hypothetical portfolio of options, we then evaluate the performance of all six models by examining how accurately each calculates the VAR on an out-of-sample basis. We find that our most general model is superior to all others in predicting the VAR. In additional empirical tests focusing on the error contribution of each of the two model components, we find that the superior performance of our most general model is largely attributable to the use of the second-order approximation, and that the first-order approximations favored by practitioners perform quite poorly. Empirical evidence on the modeling of the state variables is mixed but supports usage of a model which reflects non-linearities in state variable return distributions. This paper was presented at the Financial Institutions Center's October 1996 conference on "

Assessing bankruptcy risk for Romanian metallurgical companies

In this paper it propose to evaluate the bankruptcy risk of the companies which operate within the Romanian metallurgy industry, over the period 2001-2012, highlighting the impact of financial crisis on this sector. The bankruptcy risk assessing by Altman Model gives us a pessimistic view of the Romanian metallurgical industry. A little more optimistic perspective on the risk of bankruptcy in Romanian metallurgy is provided by the Conan Holder Model according to which the best two Romanian metallurgical companies traded at BSE (ALR and ART) face a low risk of bankruptcy. According to this model the financial crisis seems to have affected the first two Romanian metallurgical companies only in 2009, then in the following years the bankruptcy risk degrease, achieving satisfactory levels