Author: Edwin O. FischerPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Portfolio Optimization: a Combined Regime-switching and Black-Litterman Model
Author: Edwin O. FischerAsset Allocation Using Regime Switching Methods
Author: Sarthak GargPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
The aim of this thesis is to develop a Markov Regime Switching framework that can be used in asset allocation in conjunction with Modern Portfolio Theory. Modern Portfolio Theory has long been a popular tool among big financial institutions. However, one of its major limitations is assumption of stationary market volatility. In this paper, we develop a single period Mean Variance Optimization model that minimizes the variance of a portfolio subject to a specified expected return by combining Modern Portfolio Theory with a Markov Regime Switching framework. Then, we extend the above developed framework to be used in conjunction with a robust optimization framework as proposed by Goldfarb Iyengar in which regards we were partially successful. The portfolios constructed by the Markov Regime-Switching framework were tested out of sample to outperform those suggested by a Simple MVO One Factor model and the Robust MVO One Factor Model.
Portfolio Management Using Black-Litterman
Author: Henning PadbergPublisher: GRIN Verlag
ISBN: 3638897834
Category : Business & Economics
Languages : en
Pages : 24
Book Description
Seminar paper from the year 2007 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1,3, University of Münster (Finance Center Münster), course: Betriebliche Finanzierung (Finance Seminar), language: English, abstract: The Black-Litterman optimization model is based on the idea of efficient markets and the capital asset pricing model (CAPM). The BL model enhances standard mean-variance optimization by implementing market views into the optimization process (probability theory). Investors obtain sophisticated and reasonable asset allocations. Portfolio management usually comprises asset allocation decisions with the goal of creating diversified portfolios. Managers can consult quantitative models to support their decision-making process. Fischer Black and Robert Litterman (1992) developed the Black-Litterman (BL) optimization model. It is based on the idea of efficient markets, the capital asset pricing model of Sharpe (1964) and Lintner (1965), as well as the established mean-variance optimization (MVO) developed by Markowitz (1952), and conditional probability theory dating back to Bayes (1763). Starting point of the BL model is the assumption that equilibrium markets and market cap. weights provide the investor with Implied Returns. The BL model uses a mixed estimation technique to incorporate investors’ Views into return forecasts. It is possible to implement relative and absolute opinions regarding expected returns of assets with different levels of confidence. These Views enable an adjustment of equilibrium Implied Returns, which forms a new expectation of BL Revised Implied Returns. As a result of optimization with BL input data, the investor gets new optimal portfolio weights. The motivation of Black and Litterman (1992) to develop a new portfolio optimization tool was a lack of acceptance of the Markowitz algorithm within professional asset managers. There aim was to shape a model which can overcome the weaknesses of MVO and which combines a quantitative and qualitative approach. Consequently, the BL model tackles the weakest point of MVO, its sensitivity to the return forecasts and allows taking active Views. This paper is structured in the following sections: First, it shows the basic principles on which the BL model is founded. Then, it illustrates the model by means of its assumptions, the general approach, and the math involved. Finally, it evaluates the model in a critical review, provides an overview of applicable extensions, and addresses the issues of practicability and behavioral finance.
Optimal Portfolio Selection in a Regime Switching Model
Author: Xudong ZengA Regime-Switching Factor Model for Mean-Variance Optimization
Author: Giorgio CostaPublisher:
ISBN:
Category :
Languages : en
Pages : 33
Book Description
We formulate a novel Markov regime-switching factor model to describe the cyclical nature of asset returns in modern financial markets. Maintaining a factor model structure allows us to easily derive the asset expected returns and their corresponding covariance matrix. By design, these two parameters are calibrated to better describe the properties of the different market regimes. In turn, these regime-dependent parameters serve as the inputs during mean-variance optimization, thereby constructing portfolios adapted to the current market environment. Through this formulation, the proposed model allows for the construction of large, realistic portfolios at no additional computational cost during optimization. Moreover, the viability of this model can be significantly improved by periodically re-balancing the portfolio, ensuring proper alignment between the estimated parameters and the transient market regimes. An out-of-sample computational experiment over a long investment horizon shows that the proposed regime-dependent portfolios are better aligned with the market environment, yielding a higher ex post rate of return and lower volatility than competing portfolios.
Multi-Period Trading Via Convex Optimization
Author: Stephen BoydPublisher:
ISBN: 9781680833287
Category : Mathematics
Languages : en
Pages : 92
Book Description
This monograph collects in one place the basic definitions, a careful description of the model, and discussion of how convex optimization can be used in multi-period trading, all in a common notation and framework.
Portfolio Optimization in the Financial Market with Regime Switching Under Constraints, Transaction Costs and Different Rates for Borrowing and Lending
Author: Vladimir DombrovskiiPublisher:
ISBN:
Category :
Languages : en
Pages : 8
Book Description
In this work, we consider the optimal portfolio selection problem under hard constraints on trading amounts, transaction costs and different rates for borrowing and lending when the dynamics of the risky asset returns are governed by a discrete-time approximation of the Markov-modulated geometric Brownian motion. The states of Markov chain are interpreted as the states of an economy. The problem is stated as a dynamic tracking problem of a reference portfolio with desired return. Our approach is tested on a set of a real data from Russian Stock Exchange MICEX.
Strategic Asset Allocation and Markov Regime Switch with GARCH Model
Author: Ph.D. Simi (Wei)Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
During the financial crisis of 2008, the S&P 500 Implied Volatility Index (VIX), known as the “fear gauge”, jumped to 80% of the highest level it has ever reached. Portfolio managers faced tremendous pressures in these environments of such high levels market volatility. Because it is well known that asset allocation dominates portfolio performances, this paper focuses on asset allocation strategies. It develops a strategic asset allocation solution for portfolio management under all conditions and at all levels of market volatility. The approach is to derive a dynamic optimal portfolio that is based on the well-known asset allocation Black-Litterman [1991, 1992] framework. In addition, this paper proposes a methodology that considers the features of volatility regime-switching over time. This new strategic framework allows portfolio managers to derive a systematically optimal portfolio in a timely, accurate fashion.
Optimal Portfolio Choice Under Regime Switching, Skew and Kurtosis Preferences
Author: Allan TimmermannPublisher:
ISBN:
Category :
Languages : en
Pages : 34
Book Description
This paper proposes a new tractable approach to solving multi-period asset allocation problems. We assume that investor preferences are defined over moments of the terminal wealth distribution such as its skew and kurtosis. Time-variations in investment opportunities are driven by a regime switching process that can capture bull and bear states. We develop analytical methods that only require solving a small set of difference equations and thus are very convenient to use. These methods are applied to a simple portfolio selection problem involving choosing between a stock index and a risk-free asset in the presence of bull and bear states in the return distribution. If the market is in a bear state, investors increase allocations to stocks the longer their time horizon. Conversely, in bull markets it is optimal for investors to decrease allocations to stocks the longer their investment horizon.
Author: Zijian Yang