Monte Carlo simulations are more powerful because they explore comprehensively market outcomes. It is common to contrast historical simulations to forward looking Monte Carlo simulations. Historical simulations use actually observed sets of parameter values embedding all volatilities and correlations, over the horizon used for collecting the historical data. The historical simulations use samples from historical […]
It is important to make a distinction with day-to-day operations VaR and extreme VaR used for capital adequacy purposes. The second form follows the view that VaR is the economic capital that protects the bank from extreme market movements.
This appendix details the usual demonstration of decreasing portfolio return volatility when the number of assets gets large. The portfolio return volatility decreases to a floor set by general risk. When the asset returns are independent, the portfolio return volatility decreases to zero. When there is a common factor affecting all individual returns, the floor […]
This chapter provides an overview and a summary of credit risk models, as described in subsequent chapters, and plays a key role in the presentation of credit risk modelling. There is a wide spectrum of credit risk models, addressing different issues with different techniques, making an overview necessary. Some models serve for defining the credit […]
This block discusses risk drivers, exposure and credit risk valuation. The credit risk drivers are the factors that trigger credit risk events, default or migrations across risk classes. The exposures to credit risk use either actual or expected exposures, or modelled exposures such as those of over-the-counter derivatives, which are market-driven. Credit valuation implies calculating […]
KMV Portfolio Manager uses the asset value of firms as credit driver. CPV uses country-industry economic variables as risk drivers. Consequently, correlations between the individual credit events result from their dependence on common factors influencing asset values of firms or economic indexes driving the default probabilities.
Portfolio credit risk aggregates individual risks. Unlike market risk, where risk factors are market parameters readily observable, credit risk modelling faces unobservable credit risk drivers, making it necessary to model the underlying factors that drive the credit standing of obligors. Unlike market portfolio losses, which are observable, there is no way to back test credit […]
The main technique to obtain correlated loss distributions is to use Monte Carlo simulation. The technique necessitates the correlation structure of the credit risk drivers. When asset values are risk drivers, it becomes a simple matter to generate random asset values such that each firm has a predetermined default probability. For each run, the asset […]
The risk contribution of a facility to a reference portfolio is the risk of the facility not diversified away by the portfolio diversification. It is much lower than the standalone risk of the facility. Risk contributions serve several purposes: • Measuring the risk not diversified away by diversification.
Table 33.1 maps all techniques subsequently developed in this text to the related specifications of the various vendors portfolio models. Other commercial models include Credit Monitor from KMV and RiskCalc from Moodys for modelling default probabilities, or several models relating observed ratings to observable characteristics of firms, using various statistical techniques, not included in portfolio […]
