Este estudo considera o modelo de controle ótimo estocástico sob um critério de média-variância para sistemas lineares a tempo discreto sujeitos a saltos Markovianos e ruídos multiplicativos sob dois critérios. Inicialmente, consideramos como critério de desempenho a minimização multiperíodo de uma combinação entre a média e a variância da saída do sistema sem restrições. Em seguida, consideramos o critério de minimização multiperíodo da variância da saída do sistema ao longo do tempo com restrições sobre o valor esperado mínimo. Condições necessárias e suficientes explícitas para a existência de um controle ótimo são determinadas generalizando resultados anteriores existentes na literatura. O controle ótimo é escrito como uma realimentação de estado adicionado de um termo constante. Esta solução é obtida através de um conjunto de equações generalizadas a diferenças de Riccati interconectadas com um conjunto de equações lineares recursivas. Como aplicação, apresentamos alguns exemplos numéricos práticos para um problema de seleção de portfólio multiperíodo com mudança de regime, incluindo uma estratégia de ALM (Asset and Liability Management). Neste problema, deseja-se obter a melhor alocação de portfólio de forma a otimizar seu desempenho entre risco e retorno em cada passo de tempo até o nal do horizonte de investimento e sob um dos dois critérios citados acima. / In this work we consider the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noise under two criterions. First, we consider an unconstrained multiperiod mean-variance trade-off performance criterion. In the sequence, we consider a multiperiod minimum variance criterion subject to constraints on the minimum expected output along the time. We present explicit necessary and sufficient conditions for the existence of an optimal control strategy for the problems, generalizing previous results in the literature. The optimal control law is written as a state feedback added with a deterministic sequence. This solution is derived from a set of coupled generalized Riccati difference equations interconnected with a set of coupled linear recursive equations. As an application, we present some practical numerical examples on a multiperiod portfolio selection problem with regime switching, including an Asset and Liability Management strategy. In this problem it is desired to nd the best portfolio allocation in order to optimize its risk-return performance in every time step along the investment horizon, under one of the two criterions stated above.In this work we consider the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noise under two criterions. First, we consider an unconstrained multiperiod mean-variance trade-off performance criterion. In the sequence, we consider a multiperiod minimum variance criterion subject to constraints on the minimum expected output along the time. We present explicit necessary and sufficient conditions for the existence of an optimal control strategy for the problems, generalizing previous results in the literature. The optimal control law is written as a state feedback added with a deterministic sequence. This solution is derived from a set of coupled generalized Riccati difference equations interconnected with a set of coupled linear recursive equations. As an application, we present some practical numerical examples on a multiperiod portfolio selection problem with regime switching, including an Asset and Liability Management strategy. In this problem it is desired to nd the best portfolio allocation in order to optimize its risk-return performance in every time step along the investment horizon, under one of the two criterions stated above.

Este estudo considera o modelo de controle ótimo estocástico sob um critério de média-variância para sistemas lineares a tempo discreto sujeitos a saltos Markovianos e ruídos multiplicativos sob dois critérios. Inicialmente, consideramos como critério de desempenho a minimização multiperíodo de uma combinação entre a média e a variância da saída do sistema sem restrições. Em seguida, consideramos o critério de minimização multiperíodo da variância da saída do sistema ao longo do tempo com restrições sobre o valor esperado mínimo. Condições necessárias e suficientes explícitas para a existência de um controle ótimo são determinadas generalizando resultados anteriores existentes na literatura. O controle ótimo é escrito como uma realimentação de estado adicionado de um termo constante. Esta solução é obtida através de um conjunto de equações generalizadas a diferenças de Riccati interconectadas com um conjunto de equações lineares recursivas. Como aplicação, apresentamos alguns exemplos numéricos práticos para um problema de seleção de portfólio multiperíodo com mudança de regime, incluindo uma estratégia de ALM (Asset and Liability Management). Neste problema, deseja-se obter a melhor alocação de portfólio de forma a otimizar seu desempenho entre risco e retorno em cada passo de tempo até o nal do horizonte de investimento e sob um dos dois critérios citados acima. / In this work we consider the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noise under two criterions. First, we consider an unconstrained multiperiod mean-variance trade-off performance criterion. In the sequence, we consider a multiperiod minimum variance criterion subject to constraints on the minimum expected output along the time. We present explicit necessary and sufficient conditions for the existence of an optimal control strategy for the problems, generalizing previous results in the literature. The optimal control law is written as a state feedback added with a deterministic sequence. This solution is derived from a set of coupled generalized Riccati difference equations interconnected with a set of coupled linear recursive equations. As an application, we present some practical numerical examples on a multiperiod portfolio selection problem with regime switching, including an Asset and Liability Management strategy. In this problem it is desired to nd the best portfolio allocation in order to optimize its risk-return performance in every time step along the investment horizon, under one of the two criterions stated above.In this work we consider the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noise under two criterions. First, we consider an unconstrained multiperiod mean-variance trade-off performance criterion. In the sequence, we consider a multiperiod minimum variance criterion subject to constraints on the minimum expected output along the time. We present explicit necessary and sufficient conditions for the existence of an optimal control strategy for the problems, generalizing previous results in the literature. The optimal control law is written as a state feedback added with a deterministic sequence. This solution is derived from a set of coupled generalized Riccati difference equations interconnected with a set of coupled linear recursive equations. As an application, we present some practical numerical examples on a multiperiod portfolio selection problem with regime switching, including an Asset and Liability Management strategy. In this problem it is desired to nd the best portfolio allocation in order to optimize its risk-return performance in every time step along the investment horizon, under one of the two criterions stated above.

The phenomenon of financial bubbles is known to have impacted various markets since the seventeenth century. Such bubbles are known to form when the market drastically overvalues the price of an asset, causing its market value to increase hyperbolically, only to suddenly collapse once the untenable perceived future prospects of the asset are realized. Hence, it remains crucial for investors to be able to sell off assets residing within a bubble before they burst and their value is significantly diminished. Thus, portfolio optimization methods capable of accounting for financial bubbles in stock dynamics is a field of great value and interest for market participants. Portfolio optimization with respect to the mean-field is a relatively novel approach to accounting for the bubble-phenomenon. Hence, this paper investigates a previously unattempted method of portfolio optimization, providing a mean-field solution to the mean-variance trade-off problem, as well as providing new definitions of stock dynamics capable of diverting investors from bubbles. / Finansiella bubblor är ett fenomen som har påverkat marknader sedan 1600-talet. Bubblor tenderar att skapas när marknaden kraftigt övervärderar en tillgång vilket orsakar en hyperbolisk tillväxt i marknadspriset. Detta följs av en plötslig kollaps. Därför är det viktigt för investerare att kunna minska sin exponering mot aktier som befinner sig i en bubbla, så att risken för stora plötsliga förluster reduceras. Således är portföljoptimering där aktiedynamiken tar hänsyn till bubblor av högt intresse för marknadsdeltagare. Portföljoptimering med avseende på medelfältet är ett relativt nytt tillvägagångssätt för att behandla bubbelfenomen. Av denna anledning undersöks i detta arbete en hittills oprövad lösningsmetod som möjliggör en medelfältslösning till avvägningen mellan förväntad avkastning och risk. Där-utöver presenteras även ett antal nya modeller för aktier som kan bortleda investerare från bubblor.