Given the risk considerations provided in the RCD tool and the Portfolio Theory, the next step should be understanding the available risk/return metrics and determining an optimal mix of assets. Risk Metrics and Advantage/Disadvantages There are two risk metrics used in the model, Conditional Tail Expectation (CTE) and Value at Risk (VaR). These two metrics both look at the tail of the distribution. VaR is a measure of particularly poor outcomes in a stochastic projection. Its major shortcoming is its lack of statistical coherency.
In the research “Interest rate risk in the Indian banking system” of Ila Patnaik and Ajay Shah (2002) studied the interest rate risk measurement of sample of major banks in Indian, they used two methods. The first method consists of estimation on the impact upon equity capital of standardized interest rate shocks to find that “approximately two-thirds of the banks in the sample stand to gain or lose over 25% of equity capital if the interest rate moves 320 basis points”. It is found that with the use of the second method to measure the elasticity of bank stock prices to interest rate fluctuations, “the stock prices of about one-third of the sample banks had significant sensitivities”. Regards to application of the repricing model and gap analysis
Efficiency Ratios The efficiency ratio is used to measure how the company uses its assets and liabilities internally, these ratios to measure the performance in short term. • Accounts Receivable Turnover This ratio used to measure the firm's effectiveness in extending credit and in collecting debts. The receivables turnover ratio is an activity ratio measuring how successfully a In collecting its AR during the year, if the company has AR turnover 2 that means the AR turned over two times during the year. Accounts Receivable Turnover= Credit sales AR average (assume that 75% sales are credit) AVON= 9.1 ULTA= 41.1 REVLON= 4.12 • Fixed Asset Turnover, Reflecting how efficiently a company has used its assets to generate revenue, a higher ratio indicate of greater efficiency in managing and investing fixed-asset. Fixed Asset Turnover= Net sales/ net assets EVON= 1.63 ULTA= 1.9 REVLON= .77 • Inventory turnover Inventory turnover is a ratio showing how many times a company's inventory is replaced over a specific period of time, the higher ratio the more success is the company in selling its inventory.
(1965) The Valuation of Risk Assets & the Selection of Risky Investments in Stock Portfolios and Capital Budgets. Review of Economics and Statistics, 47, 2, 13-37. McElory, M.B., Burmeister, E. & Wall, K.D. (1985) Two Estimators for the APT when Factors are Measured. Economic Letters, 19, 271-275.
This model allows the heterocedasticity of data that are usually happened in time series commodity price, this model also can isvestigate time different attributes expected price and price volatility. Exponential Generalized Autoregressive conditional heteroscedasticity (EGARCH) model check volatility spillover and provided evidence of volatility spillovers in agricultural markets (Buguk et al, 2013). EGARCH models is one form of development of ARCH/GARCH models that can identify the presence of symmetric effects among the variables used in the study, so that the EGARCH models can be used to determine the level of volatility spillover between the International coffee prices and the price of coffee in Indonesia. Based on the study above, this research is greatly important with the aim to: 1. Estimate the best model of Indonesia’s coffee price volatility using ARCH/GARCH
(2004) develop fraud prediction models using financial ratios; however, their models suffer from high misclassification rates. Skousen and Wright (2008) use logistic regression to predict fraud roughly 69 percent of the time. Fraud triangle component can’t be research directly, so there are some additional test to determine whether the significant proxy variables could actually be used in the prediction of financial statement fraud (skousen, 2009). Based on skousen’s study (2009) examining the effectiveness of the fraud risk factor framework adopted in SAS No. 99 in detection of financial statement
RPN is a decision factor based on three ratings: Severity (S), Occurrence (O) and Detection (D). These ratings are scaled with numbers between 1 and 10 . The analysis starts from the basic structure of the system and particularly from those system elements for which accurate information about failure mode and its causes are available. By analyzing the functional relationships among these elements, it is possible to identify the possibility of propagation of each type of failure to predict its effects on the production performance of the entire system. This is an inductive method to analyze failure modes using down-top methodology .
W. And Changeiywo, J. M. (2008) conducted a study aimed at finding out yhe effect of Mastery Learning Approach (MLA) on students’ achievement in physics. Agboghoroma, T. E. (2009) examines the interaction effects of instructional mode and school setting on students’ knowledge of integrated science. Gok, T. and Silay, I. (2010) study was to examine the effects of teaching of the problem solving strategies on the students’ physics achievement, strategy level, attitude and achievement motivation. Hence, we see that all the studies reported above were conducted in different subject either to verify the effectiveness of the various methods or to find out the gaps.
3.5 VAR Model Vector autoregression model( Sims,1980) commonly used in the analysis of multivariate time series. it is using the model to characterize the number of relationships between vectors. In another word, a VAR is the extension of the autoregressive model to the case in which there is more than one variable under study .Different with AR model which involved one depended variable and depended only on lags of itself, a VAR has more then one dependent variable and thus has more than one equation. Each equation uses as its explanatory variables lags of all the variables under study. However, When use VAR model in time series, the first step is to determine the lag order.
Their study covers period from 1986 to 2010, of market data on daily market equity data, which they obtain from CRSP. They find that a higher volatility, repo spread and lower market return to be the main indicators of a financial crisis, and any organization involved in an environment full of these market variable to be considered as SIFI. In their study they compared the VaR of each institution against the CoVaR which implies when the institution fails, and conclude that they are very different and authorities need to focus on the VaR of the companies which doesn’t reflect the spill over effect, rather they need to focus and measure the CoVaR. By associating their CoVaR measure with balance sheet data, they claim they were able to predict the occurrence of the financial crisis in 2007. Using the combination of balance sheet data and their SIFI identification methodology, they find that organizations having higher leverage (more debt), more maturity mismatch and that are big in size, to contribute a lot to the systemic