The banking industry is clearly evolving to a higher level of risk management techniques and approaches than had been in place in the past. Yet, as this review indicates, there is significant room for improvement. Before the areas of potential value added are enumerated, however, it is worthwhile to reiterate an earlier point. The risk […]
(TABLE 1) The following are definitions of the risk levels of Borrowing Facility: 1. Substantially Risk Free Borrowers of unquestioned credit standing at the pinnacle of credit quality. Basically, governments of major industrialized countries, a few major world class banks and a few multinational corporations.
(TABLE 9) A. Introduction The purpose of this policy is to provide the basis for the bank to responsibly manage the investments in accordance with the philosophy and objectives stated below. Unless stated otherwise, the terms “investment” and “investment portfolio” will refer to both cash management activities and longer-term investment securities. The term “capital “ […]
The goal of modelling portfolio risk is to obtain the distribution of the portfolio returns at the horizon, set at the liquidation period. This implies a forward revaluation at the horizon date of all instruments once market parameters change. Since these are the value drivers of individual assets within the portfolio, the prerequisite is to […]
The standard representation of the diversification effect applies to portfolio and asset returns. The principles date from Markowitz principles (see Markowitz, 1952). The portfolio return varies with common factors and because of specific risk, the risk independent of common factors. Extending the number of assets creates diversification because the specific risks of individual asset returns […]
Delta VaR makes the individual returns a linear function of market parameter returns. The Delta-normal VaR technique assumes normality of returns distributions, resulting in a normal distribution of the portfolio returns. The volatility of the portfolio return uses the general formula that applies to any set of linear combinations of random variables. Since we can […]
The assumptions of the Delta VaR technique make it analytical, using closed-form formulas for determining loss percentiles. Since there are as many covariances and correlations as there are pairs of random variables, it is convenient to group these statistics in matrices. The matrices cross-tabulate the variables in rows and columns. The covariance, or the correlation […]
The matrix format helps when using a general formula applying to any number of exposures. The starting point is the sensitivity vector of individual exposures. The variance-covariance matrix summarizes the interdependence between the entire set of market parameters. Using the above example, the portfolio volatility is the row vector of sensitivities multiplied by the variance-covariance […]
There are several extensions of this basic analytical model. E-VaR is the expectation of VaR conditional on exceeding the threshold at the preset confidence level. It is easy to derive under the normal assumptions. Delta-Gamma VaR relies on shortcuts to capture VaR (1%) – 2.33×121=291 non-linearities. When moving along this path, we soon encounter complexities […]
The principle of the simulation method is to test multiple possible outcomes against a portfolio to obtain its risk. A set of future values of market parameters characterizes each scenario. These future values result from time path simulations of their returns if necessary. Each set of parameter values serves for revaluing the portfolio. The revaluation […]
