Archive for the ‘Finance’ Category

Financial Shock – A 360⁰ look at subprime mortgage implosion, and how to avoid the next financial crisis – authored by Mark Zandi has successfully captured the essence of Subprime Mortgage Crisis and created description of every significant area of crisis with special attention to mortgage, financial instruments and speculation in real estate market.

The book has tried to capture the shock even before its inception and with its triggering causes and detailed description of the events that lead to the burst. A chapter on risk mitigation technique and policy decisions has been provided at the end smartly.

Book describes the triggering event after the terrorist attach of September 11, 2001 when Americans perceived staying at home was safer than travelling. At this point FED lowered the interest rate to put the liquidity in the economy so that the economy could be revitalized. Inflation was not considered a problem at that point because due to shift of manufacturing to China, Deflation was at the corner.

It describes the Tech-Stock-Bubble-Burst and low returns on saving accounts as a major catalyst for Citizens to get allured towards investing in their homes, as they had prioritize staying at home so they wanted it to be better and beautiful. Government policies and Tax Codes promoted home ownership. Guidelines from Bush administration clearly promoted Lending for house ownership and perceived it as a good macroeconomic policy that would boost economy.

After the trigger when people started seeing owning home as a good investment, they started speculating on the prices and understood it as a safe investment tool. Government was happy watching the rising ownership. Lending organization saw it as an opportunity and started lending to weak financial households called Sub-Prime Borrowers, having less perfect credit history or no credit history.

As the book describes, the Lending spectrum caught hold of securitization and started developing innovative financial instruments like Alt-A, CDO, ABS, CPDOs etc. The regulating bodies were also happy because they thought that risk is being diversified due to securitization.

Figure- Borrowing and Lending Mechanism followed during the subprime-lending period.

The Book describes the lending Process in following steps :

  1. Borrower obtains loan from lender with/without help of mortgage broker.
  2. Lender sells loan to Issuer, and borrower begins making monthly payment to servicer.
  3. Issuer makes the mortgage backed securities and sells it to investors, with assistance of underwriter, rated by Credit rating agencies.
  4. The servicer collects monthly payment from borrower and remits payment to issuer.

As described the above mechanism of lending, this mechanism was looked by global investors and they also took interest in the financial instruments created by the issuers, as the interest rates were low so they preferred high yielding mortgage based investments.

Flowing with the flow lending organizations wanted to take out the most of it and started lending in non-prudent manner as they had to pass the loan to others and they were safe from the risk of non-payment. Issuers also were glad as the prices of the houses were going high so borrowers were almost sure to pay back.

One important point that author highlights is that everyone involved in the system was of the opinion that someone else was in control of system and since the mechanism is diversified all over the world so it was too big to fail.

Towards the end of 2006 the housing market started feeling the heat of lowering of housing prices, default in loan payment, but instead of seeing this as a signal builders over-optimistically argued that people were migrating to USA in large numbers, and they would require homes to live and the housing market would never go down. Point to be noted at this point is that Regulators were sleeping at this moment.

Shutting down of two hedge funds by Bear Stern it clearly signaled the something wrong flag, the default of borrowers had rose from 775000 in 2005 to 1 million in the year end 2006. Houses prices were down by 20-30%, default and foreclosure were significantly visible.

The catalyst for default and foreclosure were due to negative equity, as the borrowers of home loan owed more in loans then the present value of the houses they had bought. It was no point in paying back the loan.

Figure- Subprime Mortgage Crisis: “Vicious Cycles” of Foreclosure and Bank Instability.

The author has also gave some recommendations to avoid another sub-prime crisis:

  • The lending rules should be clear and regulators must watch the prudent lending.
  • Data collection on lender, location, income, ethnicity of borrower etc. should be done.
  • Investment on financial literacy should be done so that people take informed financial decisions.
  • Mark to Market accounting standard should be adjusted as it creates un-necessary pressure on financial institutions to quickly adjust the book value of their assets to reflect market price.

My personal opinion is that the author did a magnificent job in unveiling the sub-prime mortgage chain and discovered the loopholes that had led to the crisis. While reading the book I did not find that the author is doing Monday morning quarterbacking. The flow of the novel was coherent but derailed from the rhythm sometimes.

On a critical note I felt the content sometimes were repetitive in nature and did not cover much of the rating agencies failure issue, just indicating few times that rating agencies misjudged the risk.

Mark Zandi’s critical remarks on Alan Greenspan’s monetary policy in my opinion was a bit too harsh as in that particular situation I don’t find better way of stabilizing the economy when you have been hit by terrorist attack, S&L crisis, having wars at one end along with the threat of deflation.

Zandi should be applauded for the easy way of writing the book that even a financial illiterate person can understand and comprehend. He has also been able to throw the ball in the right court by exposing the actual culprit for the crisis i.e, ‘Human Greed’ instead of blaming certain class of investors, bankers or regulators.

Overall ‘The Book Is A Masterpiece’.



Market Risk and VaR Measurement at Bank

Posted: December 29, 2010 in Finance

Lehman Down , Merill Lynch acquired, Dubai Crisis ..We have been hearing them so often that I have started anticipating them as the next Hollywood thriller with a tagline ‘Releasing Soon’ and I pray Bollywood don’t release any thriller like SBI crippled or HDFC catastrophe !!

Thanks to BASEL II norms and conservative RBI !!

With Risk being the epicenter of discussion, We would briskly educate ourselves with  type of risk faced by any bank on a global basis on account of the exposure of banks to less regulated environment and thus swaying  to encounter financial and non-financial risk.

Risk can be broadly classified into Credit Risk, Operational Risk and Market Risk with a slight overlap in classification here and there. I hope till now you are able to recall the boredom  packed lecture of your professor at B-School  who used to shout :

Credit Risk is the uncertainty of losing of an investor’s fortune if the borrower defaults on payment”

Operational Risk is uncertainty of losing due to people, system or processes. Self explanatory term like fraud risk, legal risk, environmental risk are part of the operational risk space.”

Market Risk is uncertainty of losing due to change in the key market conditions such as interest rate, stock market index, forex rates etc. “

Having said the definitions, let’s get down to the integrities of our area of love “ Market Risk”.

Market which  can be either Money Markey or Share Market have the same philosophy ‘No Risk, No Gain’ and to play with the market is like playing with the fire, so not to get ourselves burnt we need to equip ourselves with statistical and mathematical coolant.

We have always known Banks to be the organization to push , pull or stabilize liquidity in economy, what about banks’ own liquidity? Hence the liquidity management comes into picture, which is dependent on the inflow and outflow of funds of the bank. The obligation of banking organization to provide for funds whenever withdrawn by customer, providing sums on maturity of schemes like Fixed Deposits, consideration of pre-payment of loan, all this makes the tasks of management of liquidity tough and bears a risk of non able to honour the commitments made by bank. And hence the famous term ‘Liquidity Risk’ needs attention.

Asset-Liability Committee (ALCO) of a bank while considering liquidity risk, keeps an eye on a continuous basis on the money in flow , the match between money borrowed and lent, so as  not to face liquidity crisis at any point of time. Liquidity-Adjusted Value At Risk incorporates exogenous liquidity risk into Value At Risk.(Not to disturb the flow of article we will discuss VaR later). It can be defined at VaR + ELC (Exogenous Liquidity Cost). ELC  refers to liquidity fluctuations driven by factors beyond the bank’s control..Another adjustment is to consider VaR over the period of time needed to liquidate the portfolio. VaR can be calculated over this time period.


Moving further,  We come across a very important parameter that effect the bank’s asset from loan to investment in bonds, i.e, the interest rate. Interest rate is the one which affects earning, value of the asset, value of liabilities and the cash flow. So, managing the Interest Rate Risk becomes a crucial job.

Banks face basically four types of interest rate risks :

Basis Risk : It is faced when the interest rate on asset and liabilities depend on two different bases such as LIBOR and MIBOR.

Yield Curve Risk : Considering the fact that short term interest risk is less than long term interest rate, Bank Borrows on short term and invest in long term, but this relationship can shift quickly and can cause erratic change in revenue and expenses.

Re-pricing Risk: The risk is presented by assets and liabilities that re-price at different times and rates.  For instance, a loan with a variable rate will generate more interest income when rates rise and less interest income when rates fall. If the loan is funded with fixed rated deposits, the bank’s interest margin will fluctuate.

Option Risk : Option of pre-payment of loans and foreclosure of deposits before their stated maturities constitute the Option risk.

There are several methods to calculate the impact of changing interest rate on bank’s portfolio consisting Assets and liabilities.

Assets of a bank consist of the loans dispersed. Stocks, bonds, debentures etc. that the bank owns. Cash in vault and real state possessions are also part of the asset of bank, as all these are kind of receivables income of the bank.

Liabilities of a bank consist of demand deposit, time deposit, saving and current bank account deposits, letters of credit and acceptances etc. as these has to be paid by the bank at respective times.

let’s se e a standard approach of calculating impact of changing interest rate  :-

a) Mark  the portfolio to market. b) stress test this marked to market portfolio. c) Calculate VaR of Portfolio. d) Calculate the multi period cash flow or financial accrual income and expense for N periods forward in a deterministic set of future yield curves. e) Do step ‘d’ with random yield curve movements and measure the probability distribution of cash flows and financial accrual income over time. f) Measuring the mismatch of the interest sensitivity gap of assets and liabilities, by classifying each asset and liability by the timing of interest rate reset or maturity, whichever comes first.

Appreciating the fact that banking industry is a global entity with millions of transactions in foreign currency, this has exposed us to the risk related with foreign exchange risk. This arises due to volatile structure of foreign exchange rate, which may change unfavorably for the bank before receivables are realized or payments are made in foreign currency.

Understanding the different types of market risks faced by banks we need to mitigate it if we are not able to remove it. So we need to measure it. The most common risk measurement technique prevalent in industry is Value at Risk technique.

Value at Risk ( VaR ) is the statistical technique to define a threshold value such that there is certain percentage surety that there will not be loss more than the threshold value in the defined time horizon.

For banking organization the threshold value is calculated on the time frame of 10 days ( also called holding period ) and 99% level of confidence.

There are three main approaches to calculate value at risk :

a)  Analytical VaR or Parametric VaR . b)  Historical Simulation VaR  c) Monte Carlo Simulation VaR

Analytical VaR or Parametric VaR – This method assumes a normal distribution of portfolio returns, which requires estimating the expected return and standard deviation of returns for each asset. This is suitable for simple and linear portfolio.( No complex and non-linear products, like convertible bonds, Default Swap, Options, etc ).

The  formula for the analytical VAR is:

VAR = – (Zα* σ + μ)*S

Zα is the lowest percentile of the distribution

σ is the standard deviation of the distribution (volatility)

μ is the mean of the distribution.

Historical  Simulation VaR – The historical method simply re-organizes actual historical returns, putting them in order from worst to best. It then assumes that history will repeat itself, from a risk perspective.  Depending upon the level of confidence, i.e, if LOC is 99% we take out 1% of total number of data. Let the value we have got is x, then we find the xth value from the top of the list ( worst to best ) , this is the Value at Risk at certain level of confidence for one day.

Understanding the fact that recent data has better relevance to the present market condition and past data has less, we add numerical weights to the worst to best list, giving more weightage to the recent data, and then follow the same process for calculating VaR as in Historical Simulation Methed. This is called Weighted Historical Simulation Method.

Monte Carlo Simulation VaR –  A Monte Carlo simulation refers to any method that randomly generates trials, but by itself does not tell us anything about the underlying methodology.

In a Monte Carlo simulation, a random value is selected for each of the tasks based on the range of estimates. The model is calculated based on this random value. The result of the model is recorded and the process is repeated. When the simulation is complete, we have a large number of results from the model, each based on random input values. These results are used to describe the likelihood, or probability, of reaching various results in the model.

Then the results that we have got is re-arranged from top to bottom in worst to best format and then we select the VaR value as discusses in historical simulation method.


The VaR we have got by different methods is a single day VaR, to convert it to 10 days Holding Period VaR we need to apply the following formula : VaR = VaR(daily) x √(No of days )

As mandated by Basel II, we need Var with a holding period of 10 days and 99% level of confidence, so the formula becomes :  VaR = VaR(daily) x √10 .

The numerical value of VaR that we have achieved by different models can be used for put restriction on traders. They should be not allowed to trade above the VaR limit and can be given full independence within the VaR limit.


The reckless lending and business practices of financial institutions, practices that once seemed acceptable, have led the global economy into unprecedented turmoil. The initial shock has been replaced by resignation and the knowledge that the impact of this crisis will be felt for years to come.

Prospective view with retrospection :

Regulators will be expected to intervene and prevent a boom-and-bust cycle rather than watch it from the sidelines. Policymakers will have to reconsider the roles and responsibilities of different agencies monitoring the health of the economy.

The financial system collapsed due to poor business decisions, excessive leverage, a large concentration of toxic positions and weak control structure. Some of these were the same factors that caused previous market shocks, and our irrationality was that we did not address them sooner, sufficiently or aggressively enough.

The risk management framework at many large institutions failed to control these factors. At the same time, the government and the regulators  so called “risk managers for the economy”,  did not step up and intervene decisively when there were early warning signals.

As we move forward to revamp our risk management, we need to break away from the current failed framework and culture. In the past decade we have progressively moved toward a risk management structure that is procedural, fixed to quantitative models, irrespective of the business model, instruments or markets. Further, the regulatory framework has added to the systemic risk by legitimizing flawed quantitative approaches.

It is easy to fall prey to associating risk management with calculating quantitative metrics, producing reports and managing systems and market data. In principle, risk management should be about helping institutions determine a prudent risk/return operating spectrum. It should put them in a position of strength, at all times, by avoiding catastrophic risks. Good risk management practices should aim to introduce a risk-aware culture, rather than an accident-prone culture. It should enforce disciplined risk taking/profit making within the stated business goals.

Two key Parameters that that needs to be addressed for future market volatilities:-

Leverage :  Issues related to excessive leverage or poorly executed trades are often discovered only during times of crisis, when the portfolio is experiencing stress. Although it is common to discuss leverage as if it represents risk, in reality it is the other way around. Leverage is simply the ability to buy and sell securities on margin. It is not meaningful to compare the leverage of two portfolios or institutions, even if they were potentially pursuing similar strategies.

Liquidity :  The market participant’s decision to exit could be driven by events that are fundamental, technical or sentimental. Whatever the impetus, the situation invariably has a tendency to go into a downward spiral as market events and portfolio events feed into each other. Liquidity risk is difficult to measure, partly because the liquidity observed during normal times cannot always be relied upon to estimate risk during market stress. An instrument might be liquid during normal times – but if it is complex, it is almost always illiquid during market shocks.

Well established Risk measures that needs reconsideration:-

VaR : . The industry standard metric, VaR, is not practical many a times for the purposes of limits.  VaR is not a coherent risk measure, and it doesn’t provide any information relative to the tails of the distribution. It does not lend itself to pre-trade application and is a poor risk control tool in many scenarios. Many relative value or long/short trades can be constructed so that VaR is reduced when in fact the risk is going up.

Ratio Of  Sensitivity : DV01 (A bond valuation calculation showing the dollar value of a one basis point decrease in interest rates), Delta (The delta of an option is the rate of change in an option’s price relative to a one unit change in the price of the underlying asset) or Vega (Vega measures the sensitivity of an option’s price to changes in Implied Volatility) can also be erroneous  because some of these measures are based on models that fail during gapping events.

Basis Risk : Basis risk is the risk that the change in price of a hedge may not match the change in price of the asset it hedges, can remain hidden and hence create a big challenge.

Prior Quantitative Approach failure learning that needs to be taken care in future :

There is one thing we have learned from market shocks, it is that quantitative models always fail when markets break down. While such a model is perhaps a useful measure for a business unit to understand its return profile in normal market conditions for a class of instruments, these types of models failed during the Long-Term Capital Management (LTCM) crisis to estimate many dimensions of risk.

VaR models are not effective risk control tools to use in stressed markets; they mainly provide information on performance metrics in normal market conditions. A 2% VaR at 95% confidence interval indicates that the loss will be less than 2% of capital with a 95% probability, but provides no information on the tails.

Scenario analysis and what-if analysis can be insightful for taking tactical action. This is not a new concept and has been widely discussed since the post-LTCM crisis days.

The bottom line is the analysis and assessment of risk requires combining art with science and a willingness to go against the herd (or majority view) within the institution.

If there is one risk issue that has been ignored or not sufficiently addressed in our risk management structure, it is the human element. Human behavior is always unpredictable and can bring about the escalation of a crisis. Shifting sentiments, extreme reaction and herd behavior will break correlation structures in unexpected ways. Moreover, the effects of the media and political forces can prevent management from effectively responding to a crisis.


Posted: April 28, 2010 in Finance

I would be blogging on finance topics here !!