Cette thèse a pour objectif d’évaluer et de comparer la performance des stratégies d’assurance de portefeuille pour tenter de définir quelles stratégies doivent être privilégiées par les investisseurs. Nous comparons de nombreuses stratégies d’assurance (OBPI, CPPI, put synthétique et Stop-loss) entre elles mais également avec quelques autres stratégies de référence. Nous utilisons différents critères de comparaison qui comprennent: 1. Les distributions de pay-off, le niveau de protection, la dominance stochastique et le coût d’assurance dans différentes conditions de marché identifiées par des modèles à changements de régime markovien. 2. Les mesures de la performance ajustée au risque qui peuvent refléter les préférences des investisseurs vis-à-vis du risque et de la rentabilité. 3. Les préférences des investisseurs en intégrant la théorie cumulative des perspectives (TCP). Nos résultats semblent mettre en évidence une dominance des stratégies CPPI dans la majorité des cas et pour la majorité des critères de comparaison. / This thesis is set out with the objective of evaluating and comparing the performance of portfolio insurance strategies. We try to figure out when and why one portfolio insurance strategy should be preferred by investors in practice. To meet this objective, main portfolio insurance strategies (OBPI, CPPI, Synthetic put and Stop-loss) are compared relatively to each other and to some benchmark strategies. Portfolio insurance strategies are applied within different implementation scenarios and compared according to various criteria that include:1. The payoff functions, stochastic dominance, the level of protection and the cost of insurance under bull and bear market conditions. 2. Various risk adjusted performance measures that reflect different investors’ preferences toward risk and return. 3. The preferences of investors who act according to cumulative prospect theory (CPT). Our results reveal a dominant role of CPPI strategy at the majority of cases and according to the majority of comparison criteria.

由於固定比例投資組合保險策略(CPPI)能依據投資者本身的風險偏好來選定參數，並透過簡單的公式動態調整風險性資產及保留性資產的部位，以達到投資組合保險的目的，因此成為常用的投資組合保險策略之一。然而在固定乘數的選定中，僅考慮投資人的效用函數而並未考慮市場變化情況，因此投資組合的報酬往往未必為最適化之結果。 　　本研究以台灣股市為例，旨在討論透過技術分析指標來動態調整乘數，對固定比例投資組合保險策略之影響。實證方面以1992~2003年間台股指數作為研究標的，配合固定時點調整法及相對強弱指標來調整投資組合中風險性資產及保留性資產之投資比例。並討論動態調整與固定乘數在要保誤差、平均報酬、報酬變異、偏態、交易成本及不同市況下績效表現。

本文利用模擬最適化（Simulation Optimization）的技術，來找出適合投資人之最佳資產配置。模擬最適化係為一種將決策變數輸入而使其反應變數得到最佳化結果之技術，在本篇中，決策變數為各種投資標的之資產配置，而反應變數則為投資結果之預期報酬與標準差，模擬最適化可視為一種在可行範圍內尋求最佳解之過程。本篇中模擬最適化之方法係採演化策略法，最適化問題則為具放空限制之多期架構。我們亦進一步與各種傳統的投資保險策略比較，包括買入持有策略（Buy-and-Hold）、固定比例策略（Constant Mix）、固定比例投資保險策略（Constant Proportion Portfolio Insurance）及時間不變性投資組合保險策略（Time-Invariant Portfolio Protection），以驗證模擬最適化的有效性，並以多種評估指標來衡量各種策略績效之優劣。 由實證結果發現，利用模擬最適化求解出每月的最適資產配置，雖然造成每期因資金配置比例變動而提高波動性，另一方面卻能大幅的增加報酬率。整體而言，模擬最適化技術的確能夠有效提升投資績效，使得最終財富增加，並且得到較大的夏普指數及每單位風險下較高的報酬。 / This paper applied simulation optimization technique to search for the optimal asset allocation. Simulation optimization is the process of determining the values of the decision variables that optimize the values of the stochastic response variable generated from a simulation model. The decision variables in our case are the allocations of many kinds of assets. The response variable is a function of the expected wealth and the associated risk. The simulation optimization problem can be characterized as a stochastic search over a feasible exploration region. The method we applied is the evolution strategies and the optimization problem is formulated as a multi-period one with short-sale constraints. In order to verify the effectiveness of simulation optimization, we compared the resulting asset allocation with allocations obtaining using traditional portfolio insurance strategies including Buy-and-Hold, Constant Mix, Constant Proportion Portfolio Insurance, and Time-Invariant Portfolio Protection. We also used many indexes to evaluate performance of all kinds of strategies in this paper. Our empirical results indicated that using simulation optimization to search for the best asset allocation resulted in large volatilities, however, it significantly enhanced rate of return. As a whole, applying simulation optimization indeed gets the better performance, increases the final wealth, makes Sharpe Index large, and obtains the higher return under per unit risk.

隨著時間的發展，基金的種類與數量成倍數增長，導致投資人在挑選基金時，亦面臨了選擇股票時的窘境：投資標的數目過多、複雜度高，身陷其中，而不知如何挑選理想的投資組合。目前由於人們對於退休金的相關規劃愈益重視，遂有基金商品針對此概念來設計。 生命週期基金基本上符合這樣的概念，生命週期基金基本上是屬於一種組合型基金，但是並不一定要以組合型基金的型態來顯現，美國80只生命週期基金中將近半數為基金的基金。生命週期基金是為了滿足某個年份左右退休投資者的退休投資目標的基金，如FidelityFreedom系列、FrankRussell Life Points系列、T.Rowe PriceRetirement系列、Vanguard LifeStrategy系列等。例如FidelityFreedom2020是針對2020年左右退休的投資者設計的，為實現投資者退休的投資目標的基金，主要投資在Fidelity旗下股票型基金、債券型基金和貨幣市場基金等各類基金。我們便想要了解此種商品的投資型態下具有何種特色。 我們首先要探討基金在不同投資策略其表現如何，而我們衡量的方式---簡單的說是以是否能夠達到投資人的要求報酬率為基準，以投資報酬率來建構出年金終值，最後以各種投資策略所得到的最終價值之差距做為成本的衡量，之後我們則根據生命週期基金的樣態，自行設計出兩種投資模式同樣來探討不足要求資本的相關概念。 再來以投資聚集效果(pooling effect)為主題，因為在基金存在著不同風險容忍程度的投資人，所以我們希望探討在不同投資策略下所建構的效率前緣對於不同風險忍受程度的投資人是否具有超額報酬。 首先我們就兩種投資標的(股票、債劵)之投資報酬率變化以下列方式作設定---利用隨機模型(Stochastic Model)：並利用蒙地卡羅模擬的方式來建構投資標的之報酬率。 我們觀察不同的起始投資比重(股票資產權重考慮由0%~100%，間隔為1%，共101組；債券資產的權重則為1-股票資產權重，也就是100%~0%)，並以投資組合保險中三種常見的投資策略：買入持有(Buy & Hold；BH)、固定比例混合法(Constant Mixture；CM)及時間不變性投資組合保護(Time-invariant Portfolio Protection；TIPP)，作為投資策略。在完成對投資標的之報酬率變化及投資策略的設定後，就可以在三種投資策略及每個投資策略有101個起始權重下，得到303組不同的投資結果，如此我們就可以得到帳戶的最終價值，就可以針對是否符合投資者要求的報酬率做相關的研究。 同樣的我們可以就個別的投資策略建立個別的效率前緣。之後我們就不同風險容忍程度的投資大眾，以Harry M.Markowitz等人所提出的optimal frontier的概念加以設定風險點(risk point) ，各種不同風險程度的投資人即代表不同的風險點，如此我們便可以就不同的投資模型來探討基金的投資聚集效果(pooling effect) 。 最後我們想探討的部分則是希望讓投資大眾知道如果其處於何種經濟體之下，應該採用何種投資方式，或者是在投資人的不同要求之下，可以知道採取何種投資策略，以求學術上的操作可以應用到實務上，並求取更佳的效果。 / With the development of time, the kind and quantity of the fund become multiples to increase, cause investors to face the awkward situation while choosing the stock when they select funds: There is too much figure of the investment object marking investment complexity more diffcult , and does not know how to select ideal investment combination. Nowdays, people put emphasize on retirement plan more and more. so some mutual funds are designed for this concept. Lifecycle fund is identical to this concept .Lifecycle fund is a kind of Fund of Funds basically, but might not appear like the Fund of Funds , 80 Fund of Funds in U.S.A. nearly half appear like Fund of Funds . Lifecycle fund is for the fund of retired investors' retired investment needs which is different from age-changed , such as Fidelity Freedom series, Frank Russell Life Points series , T.Rowe Price Retirement series , Vanguard Life Strategy series ,etc.. For example Fidelity Freedom2020 is designed for pensioner's investor to retire about 2020 year, the fund that in order to realize the goal of investors when they retired, make an investment in many objectives, such as stock fund ,bond fund and money market fund ,etc. under command of Fidelity mainly. I want to know the characteristic of lifecycle fund and based on this concept to design mutual fund. I will discuss behavior of fund in different investment strategies, and the way which we measure ---It is to set the rate of returns by meeting investor's requirement as the datum, build and pay the end value of the annuity to construct by investing in the rate of returns, for the measurement of " bankrupt " with the disparity of the final value got of various kinds of investment strategies , later I designed two kinds of investment ways according to the concept of lifecycle fund and also discuss the concept of " bankrupt ". This research will also make emphasize on pooling effect , There are a lot of investors of different risk tolerance in the fund ,so I hope to discuss investor of different risk tolerance will have abcdrmal return under different efficiency frontier which are derived by different invest model and strategies. First, two kind investment target (stock, bond) Investment rate of returns by way of the following to settle ---Utilize Stochastic Mode: Wilkie investment model, Taiwan investment model and the rate of returns of the one that make use of simulation that build and construct investment terms. In each method, we will consider 101 different initial ratio of stock value and three different investment strategies: Buy & Hold(BH)、Constant Mixture(CM) and Time-invariant Portfolio Protection(TIPP).According to theses investment combination, I can construct different efficiency frontier under different investment models and strategies. Such final value of the account that we can receive so I can do relevant research to the rate of returns according with investor's request . Later, according to investor of different risk tolerance , set some risk point with the concept of optimal frontier published by Harry M.Markowitz, the investors of different risk degrees represent risk point, I can discuss pooling effect in fund under different investment model and strategies. Finally, the topic I want to discuss is let the investor know at which kind of economy , should adopt the investment strategies , or under investors' different requests, can know which kind of investment tactics are adopted , so that the operation on academy can be applied to the practice , and ask for better result.

Diploma thesis is target the tackle the questions insurance portfolio Vyškov town in Moravia. The aim of this work is to analyze the risk of the city, to compare existing insurance protection, with the possibilities offered by a competitor with regard to the property and responsibility of the city and to propose such an insurance portfolio, which would correspond in all ideas of the members of the city and which would minimize the risk

Les gérants de portefeuille pour compte de tiers sont souvent jugés par leur performance relative à celle d'un portefeuille benchmark. A ce titre, ils sont amenés très fréquemment à utiliser des modèles internes de "risk management" pour contrôler le risque de sous-performer le benchmark. Par ailleurs, ils sont de plus en plus nombreux à adopter une politique de rémunération incitative, en percevant une commission de sur-performance par rapport au benchmark. En effet, cette composante variable de leur rémunération leur permet d'augmenter leur revenu en cas de sur-performance sans contrepartie en cas de sous-performance. Or de telles pratiques ont fait récemment l'objet de nombreuses polémiques : la période récente de crise financière mondiale a fait apparaître certaines carences de plusieurs acteurs financiers en terme de contrôle de risque ainsi que des niveaux de prise de risque et de rémunération jugés excessifs. Cependant, l'étude des implications de ces pratiques reste un thème encore relativement peu exploré dans le cadre de la théorie classique des choix dynamiques de portefeuille en temps continu. Cette thèse analyse, dans ce cadre théorique, les implications de ces pratiques de "benchmarking" sur le comportement d'investissement de l'asset manager. La première partie étudie les propriétés de la stratégie dynamique optimale pour l'asset manager concerné par l'écart entre la rentabilité de son portefeuille et celle d'un benchmark fixe ou stochastique (sur ou sous-performance). Nous considérons plusieurs types d'asset managers, caractérisés par différentes fonctions d'utilité et qui sont soumis à différentes contraintes de risque de sous-performance. Nous montrons en particulier quel est le lien entre les problèmes d'investissement avec prise en compte de l'aversion à la sous-performance et avec contrainte explicite de "risk management". Dans la seconde partie, on s'intéresse à l'asset manager bénéficiant d'une rémunération incitative (frais de gestion variables, bonus de sur-performance ou commission sur encours additionnelle). On étudie, selon la forme de ses incitations financières et son degré d'aversion à la sous-performance, comment sa stratégie d'investissement s'écarte de celle de l'investisseur (ou celle de l'asset manager sans rémunération incitative). Nous montrons que le changement de comportement de l'asset manager peut se traduire soit par une réduction du risque pris par rapport à la stratégie sans incitation financière soit au contraire par une augmentation de celui-ci. Finalement, nous montrons en quoi la présence de contraintes de risque de sous-performance, imposées au gérant ou traduisant son aversion à la sous-performance, peut être bénéfique à l'investisseur donnant mandat de gestion financière. / It is common practice to judge third-party asset managers by looking at their financial performance relative to a benchmark portfolio. For this reason, they often choose to rely on internal risk-management models to control the downside risk of their portfolio relative to the benchmark. Moreover, an increasing number are adopting an incentive-based scheme, by charging an over-performance commission relative to the benchmark. Indeed, including this variable component in their global remuneration allows them to increase their revenue in case of over-performance without any penalty in the event of underperforming the benchmark. However, such practices have recently been at the heart of several polemics: the recent global financial crisis has uncovered some shortcomings in terms of internal risk control as well as excessive risk-taking and compensation levels of several financial players. Nevertheless, it appears that analyzing the impact of these practices remains a relatively new issue in continuous time-dynamic asset allocation theory. This thesis analyses in this theoretical framework the implications of these "benchmarking" practices on the asset manager's investment behavior. The first part examines the properties of the optimal dynamic strategy for the asset manager who is concerned by the difference of return between their portfolio and a fix or stochastic benchmark (over- or under-performance). Several asset manager types are considered, defined by different utility functions and different downside-risk constraints. In particular, the link between investment problems with aversion to under-performance and risk management constraints is shown. In the second part, the case of the asset manager who benefits from an incentive compensation scheme (variable asset management fees, over-performance bonuses or additional commission on asset under management), is investigated. We study how, depending on the choice of financial inventive structure and loss aversion level, the asset manager's strategy differs from that of the investor (or the strategy of the asset manager receiving no incentive remuneration). This study shows that the change in investment behavior of the asset manager can lead to both a reduction in the risk taken relative to the strategy without financial incentives or conversely an increase thereof. Finally we show that the existence of downside risk constraints, imposed on the asset manager or corresponding to their aversion for under-performance, can be beneficial to the investor mandating financial management.

La disparitions graduelle des peurs liées à l’inflation pendant l’ère de la «Grande Modération» macroéconomique est aujourd’hui chose révolue : la crise financière américaine des «Subprimes», la «Grande Récession» ainsi que la crise des dettes souveraines qui s’en est suivie ont abouti à un nouvel ordre économique caractérisé par une volatilité accrue de l’inflation, un accroissement des chocs dans les prix des matières premières et une défiance envers la qualité de la signature de certains émetteurs souverains pour n’en mentionner que trois caractéristiques. De la réduction des émissions de titres souverains indexés sur l’inflation aux taux réels négatifs jusqu’à de très longues maturités, cette nouvelle donne tend à mettre en péril aussi bien les stratégies conventionnelles de couvertures inflation que les stratégies directionnelles purement nominales . Cette thèse a pour but d’investiguer les effets de ces évènements qui ont changé la donne macro-financière et d’évaluer leurs conséquences en terme de couverture inflation aussi bien dans la gestion actif-passif des investisseurs institutionnels que sur l’épargne des particuliers. Trois stratégies alternatives de couverture sont proposées pour y faire face. / Gone are the days when inflation fears had receded under years of “Great Moderation” in macroeconomics. The US subprime financial crisis, the ensuing “Great Recession” and the sovereign debt scares that spread throughout much of the industrialized world brought about a new order characterized by higher inflation volatility, severe commodity price shocks and uncertainty over sovereign bond creditworthiness to name just a few. All of which tend to put in jeopardy both conventional inflation protected strategies and nominal unhedged ones: from reduced issues of linkers to negative long-term real rates, they call into question the viability of current strategies. This paper investigates those game changing events and their asset liability management consequences for retail and institutional investors. Three alternative ways to achieve real value protection are proposed.

Les gérants de portefeuille pour compte de tiers sont souvent jugés par leur performance relative à celle d'un portefeuille benchmark. A ce titre, ils sont amenés très fréquemment à utiliser des modèles internes de "risk management" pour contrôler le risque de sous-performer le benchmark. Par ailleurs, ils sont de plus en plus nombreux à adopter une politique de rémunération incitative, en percevant une commission de sur-performance par rapport au benchmark. En effet, cette composante variable de leur rémunération leur permet d'augmenter leur revenu en cas de sur-performance sans contrepartie en cas de sous-performance. Or de telles pratiques ont fait récemment l'objet de nombreuses polémiques : la période récente de crise financière mondiale a fait apparaître certaines carences de plusieurs acteurs financiers en terme de contrôle de risque ainsi que des niveaux de prise de risque et de rémunération jugés excessifs. Cependant, l'étude des implications de ces pratiques reste un thème encore relativement peu exploré dans le cadre de la théorie classique des choix dynamiques de portefeuille en temps continu. Cette thèse analyse, dans ce cadre théorique, les implications de ces pratiques de "benchmarking" sur le comportement d'investissement de l'asset manager. La première partie étudie les propriétés de la stratégie dynamique optimale pour l'asset manager concerné par l'écart entre la rentabilité de son portefeuille et celle d'un benchmark fixe ou stochastique (sur ou sous-performance). Nous considérons plusieurs types d'asset managers, caractérisés par différentes fonctions d'utilité et qui sont soumis à différentes contraintes de risque de sous-performance. Nous montrons en particulier quel est le lien entre les problèmes d'investissement avec prise en compte de l'aversion à la sous-performance et avec contrainte explicite de "risk management". Dans la seconde partie, on s'intéresse à l'asset manager bénéficiant d'une rémunération incitative (frais de gestion variables, bonus de sur-performance ou commission sur encours additionnelle). On étudie, selon la forme de ses incitations financières et son degré d'aversion à la sous-performance, comment sa stratégie d'investissement s'écarte de celle de l'investisseur (ou celle de l'asset manager sans rémunération incitative). Nous montrons que le changement de comportement de l'asset manager peut se traduire soit par une réduction du risque pris par rapport à la stratégie sans incitation financière soit au contraire par une augmentation de celui-ci. Finalement, nous montrons en quoi la présence de contraintes de risque de sous-performance, imposées au gérant ou traduisant son aversion à la sous-performance, peut être bénéfique à l'investisseur donnant mandat de gestion financière. / It is common practice to judge third-party asset managers by looking at their financial performance relative to a benchmark portfolio. For this reason, they often choose to rely on internal risk-management models to control the downside risk of their portfolio relative to the benchmark. Moreover, an increasing number are adopting an incentive-based scheme, by charging an over-performance commission relative to the benchmark. Indeed, including this variable component in their global remuneration allows them to increase their revenue in case of over-performance without any penalty in the event of underperforming the benchmark. However, such practices have recently been at the heart of several polemics: the recent global financial crisis has uncovered some shortcomings in terms of internal risk control as well as excessive risk-taking and compensation levels of several financial players. Nevertheless, it appears that analyzing the impact of these practices remains a relatively new issue in continuous time-dynamic asset allocation theory. This thesis analyses in this theoretical framework the implications of these "benchmarking" practices on the asset manager's investment behavior. The first part examines the properties of the optimal dynamic strategy for the asset manager who is concerned by the difference of return between their portfolio and a fix or stochastic benchmark (over- or under-performance). Several asset manager types are considered, defined by different utility functions and different downside-risk constraints. In particular, the link between investment problems with aversion to under-performance and risk management constraints is shown. In the second part, the case of the asset manager who benefits from an incentive compensation scheme (variable asset management fees, over-performance bonuses or additional commission on asset under management), is investigated. We study how, depending on the choice of financial inventive structure and loss aversion level, the asset manager's strategy differs from that of the investor (or the strategy of the asset manager receiving no incentive remuneration). This study shows that the change in investment behavior of the asset manager can lead to both a reduction in the risk taken relative to the strategy without financial incentives or conversely an increase thereof. Finally we show that the existence of downside risk constraints, imposed on the asset manager or corresponding to their aversion for under-performance, can be beneficial to the investor mandating financial management.

Submitted by Gabriel Campos Pérgola (gabrielpergola@gmail.com) on 2013-02-04T12:56:43Z No. of bitstreams: 1 DissertationGabrielPergola2013.pdf: 521205 bytes, checksum: 85369078a82b0d5cc02f8248961e9214 (MD5) / Rejected by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br), reason: Prezado Gabriel, Não recebemos os arquivo em PDF. Att. Suzi 3799-7876 on 2013-02-05T18:53:00Z (GMT) / Submitted by Gabriel Campos Pérgola (gabrielpergola@gmail.com) on 2013-02-05T19:00:17Z No. of bitstreams: 2 DissertationGabrielPergola2013.pdf: 521205 bytes, checksum: 85369078a82b0d5cc02f8248961e9214 (MD5) DissertationGabrielPergola2013.pdf: 521205 bytes, checksum: 85369078a82b0d5cc02f8248961e9214 (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2013-02-05T19:07:12Z (GMT) No. of bitstreams: 2 DissertationGabrielPergola2013.pdf: 521205 bytes, checksum: 85369078a82b0d5cc02f8248961e9214 (MD5) DissertationGabrielPergola2013.pdf: 521205 bytes, checksum: 85369078a82b0d5cc02f8248961e9214 (MD5) / Made available in DSpace on 2013-02-05T19:09:04Z (GMT). No. of bitstreams: 2 DissertationGabrielPergola2013.pdf: 521205 bytes, checksum: 85369078a82b0d5cc02f8248961e9214 (MD5) DissertationGabrielPergola2013.pdf: 521205 bytes, checksum: 85369078a82b0d5cc02f8248961e9214 (MD5) Previous issue date: 23-01-13 / Portfolio insurance allows a manager to limit downside risk while allowing participation in upside markets. The purpose of this dissertation is to introduce a framework to portfolio insurance optimization from a hybrid frequentist-Bayesian approach. We obtain the joint distribution of regular returns from a frequentist statistical method, once the outliers have been identified and removed from the data sample. The joint distribution of extreme returns, in its turn, is modelled by a Bayesian network, whose topology reflects the events that can significantly impact the portfolio performance. Once we link the regular and extreme distributions of returns, we simulate future scenarios for the portfolio value. The insurance subportfolio is then optimized by the Differential Evolution algorithm. We show the framework in a step by step example for a long portfolio including stocks participating in the Bovespa Index (Ibovespa), using market data from 2008 to 2012. / Seguros de carteiras proporcionam aos gestores limitar o risco de downside sem renunciar a movimentos de upside. Nesta dissertação, propomos um arcabouço de otimização de seguro de carteira a partir de um modelo híbrido frequentista-Bayesiano com uso de derivativos. Obtemos a distribuição conjunta de retornos regulares através de uma abordagem estatística frequentista, uma vez removidos os outliers da amostra. A distribuição conjunta dos retornos extremos, por sua vez, é modelada através de Redes Bayesianas, cuja topologia contempla os eventos que o gestor considera crítico ao desempenho da carteira. Unindo as distribuições de retornos regulares e extremos, simulamos cenários futuros para a carteira. O seguro é, então, otimizado através do algoritmo Evolução Diferencial. Mostramos uma aplicação passo a passo para uma carteira comprada em ações do Ibovespa, utilizando dados de mercado entre 2008 e 2012.