1,721,051 research outputs found
Correction to: Stochastic optimization: theory and applications. Preface: special issue in memory of Marida Bertocchi
No full textThis erratum is published due to proofing error as author corrections were overlooked
Data-driven optimization in management
No full textThe area of data-driven optimization continues to attract interest not only in finance but in several other subject areas, such as energy, transportation, supply chain management, and logistics.
In this issue we include three original articles, all on the subject of finance, and one paper that presents a tutorial on the wait-and-judge approach. The content of the three finance articles is clearly influenced by the persistent condition of financial instability and high volatility experienced by global markets in the aftermath of the 2008 crisis
Optimization Methods in Finance
No full textThe article includes an independent, scholar review of a volume on Optimization in finance by Cambridge University Press
A stochastic programming model for dynamic portfolio management with financial derivatives
Full text linkStochastic optimization models have been extensively applied to financial portfolios and have proven their effectiveness in asset and asset-liability management. Occasionally, however, they have been applied to dynamic portfolio problems including not only assets traded in secondary markets but also derivative contracts such as options or futures with their dedicated payoff functions. Such extension allows the construction of asymmetric payoffs for hedging or speculative purposes but also leads to several mathematical issues. Derivatives-based nonlinear portfolios in a discrete multistage stochastic programming (MSP) framework can be potentially very beneficial to shape dynamically a portfolio return distribution and attain superior performance. In this article we present a portfolio model with equity options, which extends significantly previous efforts in this area, and analyse the potential of such extension from a modeling and methodological viewpoints. We consider an asset universe and model portfolio set-up including equity, bonds, money market, a volatility-based exchange-traded-fund (ETF) and over-the-counter (OTC) option contracts on the equity. Relying on this market structure we formulate and analyse, to the best of our knowledge, for the first time, a comprehensive set of optimal option strategies in a discrete framework, including canonical protective puts, covered calls and straddles, as well as more advanced combined strategies based on equity options and the volatility index. The problem formulation relies on a data-driven scenario generation method for asset returns and option prices consistent with arbitrage-free conditions and incomplete market assumptions. The joint inclusion of option contracts and the VIX as asset class in a dynamic portfolio problem extends previous efforts in the domain of volatility-driven optimal policies. By introducing an optimal trade-off problem based on expected wealth and Conditional Value-at-Risk (CVaR), we formulate the problem as a stochastic linear program and present an extended set of numerical results across different market phases, to discuss the interplay among asset classes and options, relevant to financial engineers and fund managers. We find that options’ portfolios and trading in options strengthen an effective tail risk control, and help shaping portfolios returns’ distributions, consistently with an investor's risk attitude. Furthermore the introduction of a volatility index in the asset universe, jointly with equity options, leads to superior risk-adjusted returns, both in- and out-of-sample, as shown in the final case-study
The predictive ability of the bond-stock earnings yield differential model
No full textIn this article, the authors survey the bond-stock prediction model for five worldwide equity markets. This model is useful for predicting the time-varying equity risk premium (ERP) and for strategic asset allocation of bond-stock equity mixes. The focus is on the model's economic and financial implications and its application to the study of stock market strategies and corrections. The model has two versions. The first model, formulated more than 35 years ago by Ziemba and Schwartz, is the difference between the most liquid long bond, usually the 10- or 30-year bond, and the trailing equity yield. The idea is that asset allocation between stocks and bonds is related to their relative yields.When the bond yield is too high, a shift out of stocks into bonds can cause an equity market correction. This model predicted the 1987,2000, and 2002 corrections in the United States and the 1990 correction in Japan. The second model and equivalent version, the Fed model, uses the ratio, or equivalently the logs, of the two yields, and has its origins in reports and statements from the Federal Reserve System under Alan Greenspan dating from about 1996. The ERP can thus be negative or positive and is, therefore, partially predictable. Despite its predictive ability, the bond-stock model has been criticized as being theoretically unsound because it compares a nominal quantity (the long-bond yield) with a real quantity (the earnings yield on stocks). Theoretical models of fairly priced equity indices can be derived and compared to actual index values to ascertain danger levels
Pricing and Hedging Pension Fund Liability via Portfolio Replication
Full text linkThis thesis is composed by three works concerning multistage stochastic programming (MSP) applications for implementing optimal decisions for defined benefit pension funds into an asset and liability management framework. The first research focuses on the development of a method for generating asset returns scenario tree processes which serve as the input specification for the risky factors in the optimization MSP problem for general financial applications. In particular the proposed method produces a scenario tree for asset returns which does not contain arbitrage opportunities and which fits the first four moments of the reference probability distribution describing the uncertain nature of the risky factors. The second work deals with the problem of evaluating the liability of a defined benefit pension fund on a market based approach. The proposed methodology has been developed on a risk measure replication approach in a discrete time setting and solved with a numerical optimization approach via MSP. The approach needs the design of a statistical model for all the risky factors driving the pension fund asset and liability dynamic. The statistical model is then used to generate a discrete space and time representation of the risky factors dynamic by means of a scenario tree. The actual price of the pension fund liability will be then defined as the minimum initial capital in order to construct a self-financing trading strategy which replicates the future pensions net expenditure with a certain degree. The degree in which the replication is performed is evaluated on the basis of a risk measure. Finally we propose a methodology to price a longevity swap contract from the point of view of the pension fund manager as the third contribution. We have defined the swap price (the fixed rate) as the maximum fixed rate that allows the pension fund to enter the contract without worsening the liability present value obtained with the risk measure replication approach developed in the second work of this thesis
Applying stochastic programming to insurance portfolios stress-testing
Full text linkThe introduction of the Solvency II regulatory framework in 2011 and unprecendented property and casualty (P/C) claims experienced in recent years by large insurance firms have motivated the adoption of risk-based capital allocation policies in the insurance sector. In this article, we present the key features of a dynamic stochastic program leading to an optimal asset-liability management and capital allocation strategy by a large P/C insurance company and describe how from such formulation a specific, industry-relevant, stress-testing analysis can be derived. Throughout the article the investment manager of the insurance portfolio is regarded as the relevant decision-maker: he faces exogenous constraints determined by the core insurance division and is subject to the capital allocation policy decided by the management, consistently with the company's risk exposure. A novel approach to stress-testing analysis by the insurance management, based on a recursive solution of a large-scale dynamic stochastic program, is presented
The cost of delay as risk measure in target-based multi-period portfolio selection models
No full textAccepted by: Aris SyntetosIncreasingly, in recent years, the fund management industry has evolved towards so-called goal-based investing paradigms, under which investors are assumed to base their portfolio strategies on pre-specified targets to be attained in the future. A similar decision model is common in the wealth management and the life insurance industries where targets may be associated with long-term investment horizons and retirement planning problems. Based on this evidence, we propose in this article a novel risk measure explicitly focusing on the financial cost that may be associated with a delay in reaching those targets. We show that the definition of this risk measure is both rather natural and effective to capture investors' risk preferences. A dynamic portfolio selection model is developed to assess the effectiveness of the risk measure from financial and risk control perspectives. The introduced risk measure has good properties and it is related to the Value-at-Risk with a given confidence level. Under sufficiently general statistical assumptions, we derive a closed form solution to a mean-risk formulation of the portfolio problem in which the cost of delay is taken as risk measure. Finally, a set of numerical tests validate the proposed portfolio selection model and show a set of comparative results with respect to a classical dynamic mean-variance model
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