Simulating Security Returns

Giovanni Barone Adesi 

Financial markets generate a continuous flow of data. Prices change every moment, prompting traders to revise their portfolios. For financial institutions, often with hundreds of traders, keeping track of their ever- changing portfolios and their future possible evolutions is a very complex task, requiring sophisticated skills in computer science, statistics and finance. The financial industry keeps investing massively in its information and risk management systems. The financial crisis of 2008 has given new scope to this endeavor, prompting new efforts to bring the inter-bank transactions to regulated clearing facilities.

It is necessary to continue developing appropriate algorithms to use most effectively the most powerful hardware to monitor trading portfolios and their risk. Moreover, it is necessary to design market facilities able to withstand the stresses that occur periodically in financial markets. That is accomplished largely through simulation methods, such as the Filtered Historical Simulation (FHS). The advantages of FHS are the possibility of simulating large portfolios of derivative, keeping track of their cross-dependencies without computing covariances or specifying their return distribution. FHS also allows risk to change coherently with returns along each simulation path. The last property can also be used to extrapolate more extreme outcomes from the historical record of returns, generating automatically stresses at any desired frequency.

The book Simulating Security Returns by G. Barone Adesi e K. Giannopoulos, after a discussion of the desirable properties of simulation methods for financial portfolios, describes the use of FHS for managing risk, highlighting the development of the underlying theory and its empirical applications to backtesting security portfolios, setting margins and capital requirements. The last chapter discusses applications of the FHS method to the pricing of options and the estimation of the pricing kernel. The advantage of the FHS approach is that it does not require assumptions about investors’ preferences or the distribution of security returns. It is therefore an ideal instrument to study both empirically.

The demanding requirements of the financial industry motivate the continuing search for fast and robust techniques to improve existing algorithms. They often can be based on the FHS framework discussed above. Two recent examples are the application of FHS to the stress testing of clearing houses, to meet the new EMIR requirements, and the computation of value at risk and expected shortfall (CVAR) from option prices. The FHS approach to EMIR exploits the inherent flexibility of FHS to improve the design of safe clearing facilities. Estimating risk measures, such as VaR and CVAR, from option prices allows to speed up risk calculations whenever reliable option prices are available, or to verify the accuracy of new implementations of risk models on assets for which options are quoted.

Giovanni Barone Adesi, author of Stimulating Security Returnsis a professor of finance theory and director at the Swiss Finance Institute, University of Lugano, Switzerland. 

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Stimulating Security Returns

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