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Advantage of Filtering for Portfolio Optimization in Financial Markets with Partial Information

  • In a financial market we consider three types of investors trading with a finite time horizon with access to a bank account as well as multliple stocks: the fully informed investor, the partially informed investor whose only source of information are the stock prices and an investor who does not use this infor- mation. The drift is modeled either as following linear Gaussian dynamics or as being a continuous time Markov chain with finite state space. The optimization problem is to maximize expected utility of terminal wealth. The case of partial information is based on the use of filtering techniques. Conditions to ensure boundedness of the expected value of the filters are developed, in the Markov case also for positivity. For the Markov modulated drift, boundedness of the expected value of the filter relates strongly to port- folio optimization: effects are studied and quantified. The derivation of an equivalent, less dimensional market is presented next. It is a type of Mutual Fund Theorem that is shown here. Gains and losses eminating from the use of filtering are then discussed in detail for different market parameters: For infrequent trading we find that both filters need to comply with the boundedness conditions to be an advan- tage for the investor. Losses are minimal in case the filters are advantageous. At an increasing number of stocks, again boundedness conditions need to be met. Losses in this case depend strongly on the added stocks. The relation of boundedness and portfolio optimization in the Markov model leads here to increasing losses for the investor if the boundedness condition is to hold for all numbers of stocks. In the Markov case, the losses for different numbers of states are negligible in case more states are assumed then were originally present. Assuming less states leads to high losses. Again for the Markov model, a simplification of the complex optimal trading strategy for power utility in the partial information setting is shown to cause only minor losses. If the market parameters are such that shortselling and borrowing constraints are in effect, these constraints may lead to big losses depending on how much effect the constraints have. They can though also be an advantage for the investor in case the expected value of the filters does not meet the conditions for boundedness. All results are implemented and illustrated with the corresponding numerical findings.

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Verfasserangaben:Leonie Ruderer
URN (Permalink):urn:nbn:de:hbz:386-kluedo-42821
Betreuer:Jörn Saß
Sprache der Veröffentlichung:Englisch
Veröffentlichungsdatum (online):13.01.2016
Jahr der Veröffentlichung:2016
Veröffentlichende Institution:Technische Universität Kaiserslautern
Titel verleihende Institution:Technische Universität Kaiserslautern
Datum der Annahme der Abschlussarbeit:04.12.2015
Datum der Publikation (Server):15.01.2016
Fachbereiche / Organisatorische Einheiten:Fachbereich Mathematik
DDC-Sachgruppen:5 Naturwissenschaften und Mathematik / 510 Mathematik
MSC-Klassifikation (Mathematik):60-XX PROBABILITY THEORY AND STOCHASTIC PROCESSES (For additional applications, see 11Kxx, 62-XX, 90-XX, 91-XX, 92-XX, 93-XX, 94-XX)
Lizenz (Deutsch):Standard gemäß KLUEDO-Leitlinien vom 30.07.2015