Quantitative analysis for probability of default of India economy


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can you introduce an expert who could understanding financial engineering and quantitative finance, as well as financial mathematics and yet at the same time has experience in Mathlab, familiar with bloomberg. On one small part of my dissertation, I need to talk about the probability of default, distance to default which was integrated from NUS-RMI credit models, hence the variable of India economy must all link to this models. We need to choose two companies with the lowest PDs as case study. I have attached some reading materials, if the expert cannot understand the reading materials, please dont engage them, because it is very technical and this is the first time I engaged your service, I didn’t know that I need to spell out so clearly for this portion, and hence, I also didn’t blame the expert for not covering this portion on the probability of default. This person must be a finance and statistic expert, do you have people in this area? As the report is only 4 pages, I expert that your company will get someone to do for me free, if not, maybe a slight top up. If your expert cannot understand the report, no way they can take up this job. The current expert doing this report need not know this arrangement as I dont want her to feel bad. Another good report for this probability of default model. I am not sure why for whatever reason, the technical report could not be sent. Possible for me to send via email? The task for this 4-5 pages report will be as follows; 1. Get the real default data and compare with default predictions (data from RMI) 2. Study the economy performance and its impact on the companies with data and statistics 3. Compare the two companies with lowest PD and do a case study. 4. Use the variables from the technical reports to analyse the india economy 1) https://docs.google.com/viewer?a=v&q=cache:inm85ZXQVBQJ:www.vikalpa.com/pdf/articles/2006/2006_jul_sep_45_56.pdf+&hl=en&pid=bl&srcid=ADGEEShEQqiv83Pjvr1u0HEGhgyQmtKOWkvzY3GXsqKmcTMuhXYUyQ8DZJ9Ob3Emw7IJRyPpCeypbh08NS-lemAWMv3yatrYuDs8ejyN3-L-llLTnq3-_oGYsXjqmBX3HZtz-JjG-BWC&sig=AHIEtbTNYDE2k8YTuWhvxleJdVM5y9dIWA 2) http://rmi.nus.edu.sg/gcr/files/04%20GCR%20vol%201.pdf 3) http://www.crede.com.tr/Documents/joseph-maurice%20(Credit%20Scoring%20and%20Credit%20Control%20IX).pdf Please see the technical report II. INPUT VARIABLES AND DATA Subsection 2.1 describes the input variables used in the quantitative model. Currently, the same set of input variables is common to all of the economies under the CRI’s coverage. Future enhancements to the CRI system will allow different input variables for different economies. The effect of each of the variables on the PD output is discussed in the empirical analysis of Section 4. Subsection 2.2 gives the data sources and relevant details of the data sources. There are two categories of data sources: current and historical. Data sources used for current data need to be updated in a timely manner so that daily updates of PD forecasts are meaningful. They also need to be comprehensive in their current coverage of firms. Data sources that are comprehensive for current data may not necessarily have comprehensive historical coverage for different economies. Other data sources are thus merged in order to obtain comprehensive coverage for historical and current data. Subsection 2.3 indicates the fields from the data sources that are used to construct the input variables. For some of the fields, proxies need to be used for a firm if the preferred field is not available for that firm. Subsection 2.4 discusses the definition and sources of defaults and of other exits used in the CRI. 2.1. Input Variables Following the notation that was introduced in Section 1, firmi’s input variables at timet=n?t are represented by the vectorXi(n)=(W(n),Ui(n)) consisting of a vectorW(n) that is common to all firms in the same economy, and a firm-specific vectorUi(n) which is observable from the date the firm’s first financial statement is released, until the month end before the month in which the firm exits, if it does exit. In Duanet al. (2012), different variables that are commonly used in the literature were tested as candidates for the elements ofW(n) andUi(n). Two common variables and ten firm-specific variables, as described below, were selected as having the greatest predictive power for corporate defaults in the United States. In the current stage of development, this same set of twelve input variables is used for all economies. Future development will include variable selection for firms in different economies. Common variables The vectorW(n) contains two elements, consisting of: 1. Stock index return: the trailing one-year simple return on a major stock index of the economy. 2. Interest rate: a representative three-month short-term interest rate with the historical mean subtracted to obtain a de-meaned time series. Firm-specific variables The ten firm-specific input variables are transformations of measures of six different firm characteristics. The six firm characteristics are: (i) volatility-adjusted leverage; (ii) liquidity; (iii) profitability; (iv) relative size; (v) market misvaluation/future growth opportunities; and (vi) idiosyncratic volatility. Volatility-adjusted leverage is measured as the distance- to-default (DTD) in a Merton-type model. The calculation of DTD used by the CRI allows a meaningful DTD for financial firms, a critical group that must be excluded from most DTD computations. This calculation is detailed in Section 3. Liquidity is measured as a ratio of cash and shortterm investments to total assets, profitability is measured as a ratio of net income to total assets, and relative size is measured as the logarithm of the ratio of market capitalization to the economy’s median market capitalization. Duanet al. (2012) transformed these first four characteristics into level and trend versions of the measures. For each of these, the level is computed as the one-year average of the measure, and the trend is computed as the current value of the measure minus the one-year average of the measure. The level and trend of a measure has seldom been used in the academic or industry literature for default prediction, and Duanet al. (2012) found that using the level and trend significantly improves the predictive power of the model for short-term horizons. To understand the intuition behind using level and trend of a measure as opposed to using just the current value, consider the case of two firms with the same current value for all measures. If the level and trend transformations were not performed, then only the current values would be used and the two firms would have identical PD. Suppose that for the first firm the DTD had reached its current level from a high level, and for the second firm the DTD had reached its current level from a lower level (see Figure 2). The first firm’s leverage is increasing (worsening) and the second firm’s leverage is decreasing (improving). If there is a momentum effect in DTD, then firm 1 should have a higher PD than firm 2. Duanet al. (2012) found evidence of the momentum effiect in DTD, liquidity, profitability and size. For the other two firm characteristics, applying the level and trend transformation did not improve the predictive power of the model. One of the remaining two firm characteristics is the market mis-valuation/future growth opportunities characteristic, which is taken as the market-to-book asset ratio and measured as a ratio of market capitalization and total liabilities to total assets. One can see whether the market mis-valuation effect or the future growth opportunities effect dominates this measure by looking at whether the parameter for this variable is positive or negative. This is further discussed in the empirical analysis of Section 4. The final firm characteristic is the idiosyncratic volatility which is taken as sigma, following Shumway (2001). Sigma is computed by regressing the monthly returns of the firm’s market capitalization on the monthly returns of the economy’s stock index, for the previous 12 months. Sigma is defined to be the standard deviation of the residuals of this regression. Shumway (2001) reasons that sigma should be logically related to bankruptcy since firms with more variable cash flows and therefore more variable stock returns relative to a market index are likely to have a higher probability of bankruptcy. Finally, the vectorUi(n) contains ten elements, consisting of: 1. Level of DTD. 2. Trend of DTD. 3. Level of (Cash+Short-term investments)/Total assets, abbreviated as CASH/TA. 4. Trend of CASH/TA. 5. Level of Net income / Total Assets, abbreviated as NI/TA. 6. Trend of NI/TA. 7. Level of log (Firm market capitalization/ Economy’s median market capitalization), abbreviated as SIZE. 8. Trend of SIZE. 9. Current value of (Market capitalization+total liabilities)/Total asset, abbreviated as M/B. 10. Current value of SIGMA. The data fields that are needed to compute DTD and short-term investments are described in Subsection 2.3. The remaining data fields required are straightforward and standard. The computation for DTD is explained in Section 3. Attachments: Technical-Rep….pdf MF-MLEDerivat….pdf Slides-28-Jan….pdf PD-behaviour…..xlsx RMI-CRI-Dec-2….pptm

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