The Basel Committee on Banking Supervision (BCBS) released the revised capital accord, also called, Basel II, on June 26, 2004. The document is called “International Convergence of Capital Measurement and Capital Standards: A Revised Framework”. The significant features of Basel II are:
The rules of the original 1988 Accord are generally acknowledged to be flawed for various reasons, discussed below: Credit risk assessment under Basel I is not risk-sensitive enough. Capital need assessment under Basel I accord was not being able to differentiate between banks with lower risks and banks with higher risks. For example, exposure on […]
In 1996, Basel Committee on Banking Supervision (BCBS) published an amendment to the 1988 Basel Accord to provide an explicit capital cushion for the price risks to which banks are exposed, particularly those arising from their trading activities. This amendment was brought into effect in 1998. Salient features of the amendment are given below:
The 1988 Basel Accord, also known as Basel I, established minimum capital standards for the banking industry by linking the banks’ capital requirements to their capital exposures. Basel I primarily focused on credit risk. The credit exposures were divided into five categories that represented similar types of borrowers. Each category is tied to a specific […]
What is the Basel Committee for Banking Supervision? The Basel Committee on Banking Supervision is a committee of banking supervisory authorities, which was established by the central bank governors of the Group of Ten (G10) countries in 1975. It consists of senior representatives of bank supervisory authorities and central banks from Belgium, Canada, France, Germany, […]
Computing VaR with Monte Carlo Simulations very similar to Historical Simulations. The main difference lies in the first step of the algorithm – instead of using the historical data for the price (or returns) of the asset and assuming that this return (or price) can re-occur in the next time interval, we generate a random number that will be used to estimate the return (or price) of the asset at the end of the analysis horizon.
In the previous post, we learned the algorithm to compute VaR using Monte Carlo Simulation. Let us compute VaR for one share to illustrate the algorithm.
We apply the algorithm to compute the monthly VaR for one stock. We will only consider the share price and thus work with the assumption we have only one share in our portfolio. Therefore the value of the portfolio corresponds to the value of one share.
The fundamental assumption of the Historical Simulations methodology is that you base your results on the past performance of your portfolio and make the assumption that the past is a good indicator of the near-future.
The below algorithm illustrates the straightforwardness of this methodology. It is called Full Valuation because we will re-price the asset or the portfolio after every run. This differs from a Local Valuation method in which we only use the information about the initial price and the exposure at the origin to deduce VaR.
We earlier saw how VaR can be calculated using the parametric method. We will now look at this method in detail, and also understand how VaR can be easily calculated using matrices. VaR of a Single Asset VaR of a single asset is the value of the asset multiplied by its volatility. Here, the volatility […]
Define the concept of Value-at-Risk (VaR) Value-at- Risk (VaR) is a general measure of risk developed to equate risk across products and to aggregate risk on a portfolio basis. VaR is defined as the predicted worst-case loss with a specific confidence level (for example, 95%) over a period of time (for example, 1 day). For […]