Author: Sergio H. Lence
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This study shows how the standard portfolio model of futures trading should be modified when there is less than perfect information about the relevant parameters (estimation risk). The standard and the optimal decision rules for futures trading in the presence of estimation risk are compared and discussed. An operational model of futures trading for use under estimation risk is advanced. In the presence of relevant prior and sample information, the model can be used to optimally blend both types of information.
Author: Danielle Mousseau Davidian
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The focus of this dissertation is to investigate the impact of estimated parameters on optimal decision-making. This investigation has two streams. The first stream of this dissertation investigates the optimization bias generated by approximating expectation functions via sample average approximation (SAA). It is a well-known result that using sample average approximation to approximate stochastic programs for which there is not an analytical solution provides an optimal value function which is optimistically biased if the feasibility of approximated solutions can known with certainty. We study the impact of drawing Monte Carlo samples from a simulated distribution with estimated parameters, rather than from the true distribution, on this bias. This is particularly relevant to situations where either (1) the optimal value of the objective function is useful (as a price, for example) or (2) when the optimal value is used as a method of determining the quality of a proposed optimal solution. We consider stochastic programs with expectation constraints and find that under certain circumstances the first order bias can be approximated as the sum of two separately determined biases: the simulation bias due to SAA using true parameter values and the statistical bias of the true problem resulting purely from parameter sensitivity. In addition, we show that when the feasible region must be determined via sampling, the possibility of infeasible approximately optimal solutions potentially reverses the sign of the bias, regardless of whether parameter estimation error is present. This is contrary to the widely used assumption of an optimistic bias. The second stream of this dissertation focuses on the optimization of a collar option strategy, a strategy that is frequently used to improve performance of an investment by protecting one from downside risk at the expense of upside gains. Our analysis optimizes expected utility of single and multi-month strategies where the investor has the three assets available: the risky underlying, put contracts and call contracts with discrete strike prices. For the single month strategies, we find that the investor chooses the collar whose instantaneous replicating portfolio is equal to the optimal mixed strategy calculated using traditional Markowitz optimization without derivative contracts present. We also show that, in the presence of parameter estimation error, regimes exist where collar strategies improve investor performance over a traditional mixed strategy. These simulation results are complimented by an empirical analysis that shows that high performance regimes occur often enough to improve the investor's out of sample certainty equivalent compared to traditional mixed strategies. Existing empirical studies, as well as the empirical work performed within this dissertation, show improved performance with a multi-month strategy over a single month strategy. Our simulation results do not support this result, implying multi-month strategy improved performance cannot be due to the time-value component of derivative contract value as proposed in the existing literature. Instead, we posit multi-month strategy improved performance results from a natural ``hedge'' against either changes in the underlying risky asset's volatility or volatility estimation error.
Author: Leonard C Maclean
Publisher: World Scientific
ISBN: 981441736X
Category : Business & Economics
Languages : en
Pages : 941
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Book Description
This handbook in two parts covers key topics of the theory of financial decision making. Some of the papers discuss real applications or case studies as well. There are a number of new papers that have never been published before especially in Part II.Part I is concerned with Decision Making Under Uncertainty. This includes subsections on Arbitrage, Utility Theory, Risk Aversion and Static Portfolio Theory, and Stochastic Dominance. Part II is concerned with Dynamic Modeling that is the transition for static decision making to multiperiod decision making. The analysis starts with Risk Measures and then discusses Dynamic Portfolio Theory, Tactical Asset Allocation and Asset-Liability Management Using Utility and Goal Based Consumption-Investment Decision Models.A comprehensive set of problems both computational and review and mind expanding with many unsolved problems are in an accompanying problems book. The handbook plus the book of problems form a very strong set of materials for PhD and Masters courses both as the main or as supplementary text in finance theory, financial decision making and portfolio theory. For researchers, it is a valuable resource being an up to date treatment of topics in the classic books on these topics by Johnathan Ingersoll in 1988, and William Ziemba and Raymond Vickson in 1975 (updated 2nd edition published in 2006).
Author: Hyunjong Jin
Publisher:
ISBN:
Category :
Languages : en
Pages : 58
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Book Description
The classical mean-variance model, proposed by Harry Markowitz in 1952, has been one of the most powerful tools in the field of portfolio optimization. In this model, parameters are estimated by their sample counterparts. However, this leads to estimation risk, which the model completely ignores. In addition, the mean-variance model fails to incorporate behavioral aspects of investment decisions. To remedy the problem, the notion of ambiguity aversion has been addressed by several papers where investors acknowledge uncertainty in the estimation of mean returns. We extend the idea to the variances and correlation coefficient of the portfolio, and study their impact. The performance of the portfolio is measured in terms of its Sharpe ratio. We consider different cases where one parameter is assumed to be perfectly estimated by the sample counterpart whereas the other parameters introduce ambiguity, and vice versa, and investigate which parameter has what impact on the performance of the portfolio.
Author: Erricos John Kontoghiorghes
Publisher: Springer Science & Business Media
ISBN: 1475736134
Category : Business & Economics
Languages : en
Pages : 626
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Book Description
Computing has become essential for the modeling, analysis, and optimization of systems. This book is devoted to algorithms, computational analysis, and decision models. The chapters are organized in two parts: optimization models of decisions and models of pricing and equilibria.
Author: Glenn Koller
Publisher: CRC Press
ISBN: 1420035053
Category : Business & Economics
Languages : en
Pages : 351
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Book Description
Building upon the technical and organizational groundwork presented in the first edition, Risk Assessment and Decision Making in Business and Industry: A Practical Guide, Second Edition addresses the many aspects of risk/uncertainty (R/U) process implementation. This comprehensive volume covers four broad aspects of R/U: general concepts, i
Author: John R. Schuyler
Publisher:
ISBN:
Category : Business & Economics
Languages : en
Pages : 294
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Book Description
Some of Schuyler's tried-and-true tips include: - The single-point estimate is almost always wrong, so that it is always better to express judgments as ranges. A probability distribution completely expresses someone's judgment about the likelihood of values within the range.- We often need a single-value cost or other assessment, and the expected value (mean) of the distribution is the only unbiased predictor. Expected value is the probability-weighted average, and this statistical idea is the cornerstone of decision analysis.- Some decisions are easy, perhaps aided by quick decision tree calculations on the back of an envelope. Decision dilemmas typically involve risky outcomes, many factors, and the best alternatives having comparable value. We only need analysis sufficient to confidently identify the best alternative. As soon as you know what to do, stop the analysis!- Be alert to ways to beneficially change project risks. We can often eliminate, avoid, transfer, or mitigate threats in some way. Get to know the people who make their living helping managers sidestep risk. They include insurance agents, partners, turnkey contractors, accountants, trainers, and safety personnel.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309180538
Category : Science
Languages : en
Pages : 124
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Book Description
Uncertainty is a fundamental characteristic of weather, seasonal climate, and hydrological prediction, and no forecast is complete without a description of its uncertainty. Effective communication of uncertainty helps people better understand the likelihood of a particular event and improves their ability to make decisions based on the forecast. Nonetheless, for decades, users of these forecasts have been conditioned to receive incomplete information about uncertainty. They have become used to single-valued (deterministic) forecasts (e.g., "the high temperature will be 70 degrees Farenheit 9 days from now") and applied their own experience in determining how much confidence to place in the forecast. Most forecast products from the public and private sectors, including those from the National Oceanographic and Atmospheric Administration's National Weather Service, continue this deterministic legacy. Fortunately, the National Weather Service and others in the prediction community have recognized the need to view uncertainty as a fundamental part of forecasts. By partnering with other segments of the community to understand user needs, generate relevant and rich informational products, and utilize effective communication vehicles, the National Weather Service can take a leading role in the transition to widespread, effective incorporation of uncertainty information into predictions. "Completing the Forecast" makes recommendations to the National Weather Service and the broader prediction community on how to make this transition.
Author: Chris Chapman
Publisher: John Wiley & Sons
ISBN:
Category : Business & Economics
Languages : en
Pages : 520
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Book Description
This title confidently puts forward a practical, new approach to decision making in an uncertain business world. Many variables are accounted for and the authors are innovative in integrating previous types of decision-making approaches with a more fluid, and therefore realistic model that can be applied across a wide range of contexts and decisions. A new title on a important topic that not only stands well on its own, but also complements Chapman and Ward's previous title Project Risk Management. This book is practical and rigorous yet written in an engaging way. It is perfect for courses, or to be used by practitioners.
Author: Sandra Milev
Publisher:
ISBN:
Category : University of Ottawa theses
Languages : en
Pages :
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Book Description
Background: Uncertainty in parameters is present in many risk assessment and decision making problems and leads to uncertainty in model predictions. Therefore an analysis of the degree of uncertainty around the model inputs is often needed. Importance analysis involves use of quantitative methods aiming at identifying the contribution of uncertain input model parameters to output uncertainty. Expected value of partial perfect information (EVPPI) measure is a current gold- standard technique for measuring parameters importance in health economics models. The current standard approach of estimating EVPPI through performing double Monte Carlo simulation (MCS) can be associated with a long run time. Objective: To investigate different importance analysis techniques with an aim to find alternative technique with shorter run time that will identify parameters with greatest contribution to uncertainty in model output. Methods: A health economics model was updated and served as a tool to implement various importance analysis techniques. Twelve alternative techniques were applied: rank correlation analysis, contribution to variance analysis, mutual information analysis, dominance analysis, regression analysis, analysis of elasticity, ANCOVA, maximum separation distances analysis, sequential bifurcation, double MCS EVPPI,EVPPI-quadrature and EVPPI- single method. Results: Among all these techniques, the dominance measure resulted with the closest correlated calibrated scores when compared with EVPPI calibrated scores. Performing a dominance analysis as a screening method to identify subgroup of parameters as candidates for being most important parameters and subsequently only performing EVPPI analysis on the selected parameters will reduce the overall run time.
