[MUSIC] Now let's go back and re introduce this other concept value at risk. There is market value at risk and then there's credit value at risk. We more commonly associate VaR vis a are we more commonly associate that with market risk? And so our basic definition of market VaR would be for a portfolio of securities and derivatives positions in it. Those and the subject to market risk. We want to define what is the worst case loss in market value in those securities and derivatives trading positions over a defined time period based on market factors. Okay, so we have methodology and approaches to calculating that if I have a portfolio of $100 million 10 day period with 99% probability, what's the worst that we can lose? And then we go through either computer simulation, historical simulation. We can also calculate variance and volatility and there's a sort of a standard or common practices in terms of calculating market var. What we're trying to do now is transfer that into credit VaR. So now I have a portfolio of investments. Those investments are subject to credit risk, not necessarily market risk, the credit risk and deteriorate. And it can have an impact on the market price of the bond. I've already explained that if I have a bond, if the credit risk increases, the probability increases, the credit spread increases, they don't have impact on the price of the bond. So to what extent that can credit deterioration have impact on the price of that credit asset? More specifically in this case would be the bond if there's a deterioration. My bond is a single A. If there's a deterioration, it could be downgraded to triple B. And that trip will be don will require a higher credit spread is going to have impact on the bond price. What is that value? And so credit var takes the same approach as market var what is the worst case loss in value over time defined time period based on deteriorating credit worthiness or decline in the credit rating. And then we can look at different models and different process just like with market bar to try to determine what that worst case loss would actually be. So we're kind of transitioning from market var to the credit var. And the way that market risk managers seek to compute a var will try to do that for credit var. Again, the assistant substitute for financial analysis and credit analysis. What this does is that based on historical data, historical transitions and migrations and deterioration of credit risk. How much can I lose in the value of that asset as a result of that credit deterioration. So so we can first, as we do with Market Bar that can set a time limit in terms of over what time period, one day, five days, 10 days or 30 days. What is my worst case decline in value? And in a confidence level? To what degree, what Probability? What confidence, 95 97 99% confidence is my worst case lost. Very similar to how market bar is calculated in that respect. And then I can look at my distribution of possible losses, establish a confidence rain and then I can do this calculation of credit bar similar to how we do it with market bar. So whatever the approaches credit var is the maximum loss as a result of credit deterioration, it's not default, it's deterioration, it's actually deterioration. Remember with default we're looking at the case where there's a non payment credit deterioration is very likely that the company, the var of the counter product will continue to make payments. It's just that the probability of default has actually increased. Now one way we can do this, here's one way we can do is looking at what we call a transition matrix, transition matrix. And what that is, is that is that this is information that's provided by the rating agency here. In this case it's moody's And Moody's looked at this over a over 45 year period. And based on like we did default frequencies. This is about ratings, movement, ratings, migration. And so what happened to a company's credit ratings? What happens to a company's credit rating one year later, one year later. So my time period is going to be one year. And so let's look at the case of a triple B 85% of the time. A triple B rated company or triple B rated debt 85% it remains the same 3.7% of the time it deteriorated to a double B .69%. It deteriorated to a single be now ratings migration. In some cases, the the the company's rating could improve. Not necessarily decline. It can actually improve. So you do see that 4.3% of the time the ratings for this volume of issues in this particular group of triple B's, 4.3% of time it was improved the rating got better. It went to single A. What we're doing is that we're looking at risk of loss and so we're focusing not on improvement. We're looking at deterioration and so 85% of the time it remained the same, three point 7%, let's say 4%. It deteriorates a double B. So I could argue just by a snapshot look and say that migration transition. And so I could say that 89% of the time a triple b remained the same or deteriorated to double B. I can add what's the what's the probability or the frequency that it deteriorated to Single B. Let's call that 1%. So I can say 85% stayed the same. 4% had deteriorated two double b. And then 1% of the time it deteriorated with single big. So this leads me to say that it's based on past data, 90% of the time it's it's the worst it got was a single big. That helps me understand in terms of my credit deterioration. So I could draw A conclusion based on the past 90 percent of the time A triple B got no worse than single be. What we're doing is that if we say with 90 percent confidence, the worst it got was single B. Well the deterioration and the value of the asset from triple B. Two single be that What is the price of a single Be Asset one Year from Now? If I say that 90% of times going to the worst that could happen based on past statistics is that, deteriorate the single be the price of that bond one year from now we'll have deteriorated because of the ratings decline and the expectation of a higher credit spread. So therefore I'm going to say it's going to say my credit var is going to be the loss in the value of a Triple B asset that is now declined or deteriorated to a Single day. What is the price of that single B asset one year from now? That's how I will implement or use an approach for a model to calculate my credit var. And I'm using, I'm using historical the data to determine just what we were doing market far. We use historical data for to understand market volatility here is not market volatility but understand the potential for deterioration. And what is the expected rating of that name? The worst expected rating of that night With certain degree of Probability one Year from now. And what will be the price of that bond based on that rating one year from now. So let's summarize that credit var is yet another tool to assess possible losses and a bond portfolio as a result of not interest rate volatility, not not market volatility but credit deterioration. So in some my credit, my radius migration data, I'm using the moody's data can be used to determine that level of confidence of how far the company's issuer rating can deteriorate over that time frame, time frame, we used an example is one year. If the rating declines, then the market value of the bond or the loan will decline to account for the higher spread and embraced on prior data and the confidence level that we're going to use. The analysts or whoever the risk manager can determine the greatest migration or lowest bond rating over that period and then compute the expected price of that bond one year from now at that new rating and that lost in value is going to be referred to as credit var. Sometimes they call this unexpected loss in the same way that we call that in the market bar when you do that particular calculation. And so sometimes it's referred to unexpected loss as it relates to credit Mark. I talked about credit Bar. I'm talking about unexpected loss as it relates to Basel three Bank regulation. That's a different approach. This could also be an approach to calculating a credit var or unexpected loss in the portfolio. Different approaches of trying to do this. There is a company it's called credit metrics. And what credit metrics will do is that it does calculations of credit var using a very similar approach that this is a little bit more complicated in terms of the presentation. But ultimately what it's doing is that's based on a portfolio of credit risk of bonds in the portfolio credit assets. Then it can use data transition matrices to come up with a credit virus. So what the credit metrics, it's just a model. It will do the calculations, it will look at the credit rating of the bar, it will look at the migration tables and statistics that we just described. It will establish a migration probability, what's the probability that it's triple B. It will decline to double B. Or single B. And establish a established a confidence level. And then what it will also do. It'll make assumptions about the price of that asset, about or expected price of the asset one year from now. So it will do so in a more complicated approach than the simplistic approach. What I'm trying to trying to do is just an introduced sort of a generic approach about how we can calculate credit var and then what does it actually mean? What does actually entail? So this concludes less than one, module three and a quick takeaways. We introduced the essential concepts of portfolio management, portfolio credit risk, we introduce expected loss, unexpected loss concentration and correlation. And we also talked about how Basil three Bank regulation in the U. S. Is adopted under Dodd frank for large banks. What is the what is the objective in terms of capital requirements for credit risk, unexpected loss? The worst case scenario 99 .9% confidence which we can lose in that particular portfolio name by name and then across the entire portfolio and then also account for degrees of correlation. That's going to be the capital requirement, unexpected loss. Worst case scenario regulators financial regulators want to see capital as a cushion to absorb that once in a century. Sometimes we call it once in a century type of loss. And then in this section we concluded with credit var