This paper proposes markovian models in portfolio theory and risk management. At first, we describe discrete time optimal allocation models. Then, we examine the investor’s optimal choices either when the returns are uniquely determined by their mean and variance or when they are modeled by a Markov chain. We subject these models to back-testing on out-of-sample data, in order to assess their forecasting ability. Finally, we propose some models to compute VaR and CVaR when the returns are modeled by a Markov chain.

Portfolio Selection and Risk Management with Markov Chains / Leccadito, Arturo; Ortobelli, S; Russo, Emilio. - In: INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY. - ISSN 1738-7906. - 7:6(2007), pp. 115-123.

Portfolio Selection and Risk Management with Markov Chains

LECCADITO, ARTURO;RUSSO, EMILIO
2007

Abstract

This paper proposes markovian models in portfolio theory and risk management. At first, we describe discrete time optimal allocation models. Then, we examine the investor’s optimal choices either when the returns are uniquely determined by their mean and variance or when they are modeled by a Markov chain. We subject these models to back-testing on out-of-sample data, in order to assess their forecasting ability. Finally, we propose some models to compute VaR and CVaR when the returns are modeled by a Markov chain.
Markov chain, Portfolio theory, VaR and CVaR models.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/156018
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