Dynamic Portfolio Selection by Augmenting the Asset Space

Dynamic Portfolio Selection by Augmenting the Asset Space
Author: Michael W. Brandt
Publisher:
Total Pages: 32
Release: 2004
Genre: Investment analysis
ISBN:

We present a novel approach to dynamic portfolio selection that is no more difficult to implement than the static Markowitz model. The idea is to expand the asset space to include simple (mechanically) managed portfolios and compute the optimal static portfolio in this extended asset space. The intuition is that a static choice among managed portfolios is equivalent to a dynamic strategy. We consider managed portfolios of two types: "conditional" and "timing" portfolios. Conditional portfolios are constructed along the lines of Hansen and Richard (1987). For each variable that affects the distribution of returns and for each basis asset, we include a portfolio that invests in the basis asset an amount proportional to the level of the conditioning variable. Timing portfolios invest in each basis asset for a single period and therefore mimic strategies that buy and sell the asset through time. We apply our method to a problem of dynamic asset allocation across stocks, bonds, and cash using the predictive ability of four conditioning variables.

Portfolio Choice Problems

Portfolio Choice Problems
Author: Nicolas Chapados
Publisher: Springer Science & Business Media
Total Pages: 107
Release: 2011-07-12
Genre: Computers
ISBN: 1461405777

This brief offers a broad, yet concise, coverage of portfolio choice, containing both application-oriented and academic results, along with abundant pointers to the literature for further study. It cuts through many strands of the subject, presenting not only the classical results from financial economics but also approaches originating from information theory, machine learning and operations research. This compact treatment of the topic will be valuable to students entering the field, as well as practitioners looking for a broad coverage of the topic.

Portfolio Selection Using Multi-Objective Optimisation

Portfolio Selection Using Multi-Objective Optimisation
Author: Saurabh Agarwal
Publisher: Springer
Total Pages: 240
Release: 2017-08-21
Genre: Business & Economics
ISBN: 3319544160

This book explores the risk-return paradox in portfolio selection by incorporating multi-objective criteria. Empirical research is presented on the development of alternate portfolio models and their relative performance in the risk/return framework to provide solutions to multi-objective optimization. Next to outlining techniques for undertaking individual investor’s profiling and portfolio programming, it also offers a new and practical approach for multi-objective portfolio optimization. This book will be of interest to Foreign Institutional Investors (FIIs), Mutual Funds, investors, and researchers and students in the field.

Mathematical Optimization Theory and Operations Research

Mathematical Optimization Theory and Operations Research
Author: Yury Kochetov
Publisher: Springer Nature
Total Pages: 445
Release: 2020-09-13
Genre: Computers
ISBN: 303058657X

This book constitutes refereed proceedings of the 19th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2020, held in Novosibirsk, Russia, in July 2020. Due to the COVID-19 pandemic the conference was held online. The 25 full papers and 8 short papers presented in this volume were carefully reviewed and selected from a total of 102 submissions. The papers in the volume are organised according to the following topical headings: ​combinatorial optimization; mathematical programming; global optimization; game theory and mathematical economics; heuristics and metaheuristics; machine learning and data analysis.

Handbook of Financial Econometrics

Handbook of Financial Econometrics
Author: Yacine Ait-Sahalia
Publisher: Elsevier
Total Pages: 809
Release: 2009-10-19
Genre: Business & Economics
ISBN: 0080929842

This collection of original articles—8 years in the making—shines a bright light on recent advances in financial econometrics. From a survey of mathematical and statistical tools for understanding nonlinear Markov processes to an exploration of the time-series evolution of the risk-return tradeoff for stock market investment, noted scholars Yacine Aït-Sahalia and Lars Peter Hansen benchmark the current state of knowledge while contributors build a framework for its growth. Whether in the presence of statistical uncertainty or the proven advantages and limitations of value at risk models, readers will discover that they can set few constraints on the value of this long-awaited volume. - Presents a broad survey of current research—from local characterizations of the Markov process dynamics to financial market trading activity - Contributors include Nobel Laureate Robert Engle and leading econometricians - Offers a clarity of method and explanation unavailable in other financial econometrics collections

Adaptive Asset Allocation

Adaptive Asset Allocation
Author: Adam Butler
Publisher: John Wiley & Sons
Total Pages: 209
Release: 2016-02-02
Genre: Business & Economics
ISBN: 1119220378

Build an agile, responsive portfolio with a new approach to global asset allocation Adaptive Asset Allocation is a no-nonsense how-to guide for dynamic portfolio management. Written by the team behind Gestaltu.com, this book walks you through a uniquely objective and unbiased investment philosophy and provides clear guidelines for execution. From foundational concepts and timing to forecasting and portfolio optimization, this book shares insightful perspective on portfolio adaptation that can improve any investment strategy. Accessible explanations of both classical and contemporary research support the methodologies presented, bolstered by the authors' own capstone case study showing the direct impact of this approach on the individual investor. Financial advisors are competing in an increasingly commoditized environment, with the added burden of two substantial bear markets in the last 15 years. This book presents a framework that addresses the major challenges both advisors and investors face, emphasizing the importance of an agile, globally-diversified portfolio. Drill down to the most important concepts in wealth management Optimize portfolio performance with careful timing of savings and withdrawals Forecast returns 80% more accurately than assuming long-term averages Adopt an investment framework for stability, growth, and maximum income An optimized portfolio must be structured in a way that allows quick response to changes in asset class risks and relationships, and the flexibility to continually adapt to market changes. To execute such an ambitious strategy, it is essential to have a strong grasp of foundational wealth management concepts, a reliable system of forecasting, and a clear understanding of the merits of individual investment methods. Adaptive Asset Allocation provides critical background information alongside a streamlined framework for improving portfolio performance.

Bayesian Inference of State Space Models

Bayesian Inference of State Space Models
Author: Kostas Triantafyllopoulos
Publisher: Springer Nature
Total Pages: 503
Release: 2021-11-12
Genre: Mathematics
ISBN: 303076124X

Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models. The celebrated Kalman filter, with its numerous extensions, takes centre stage in the book. Univariate and multivariate models, linear Gaussian, non-linear and non-Gaussian models are discussed with applications to signal processing, environmetrics, economics and systems engineering. Over the past years there has been a growing literature on Bayesian inference of state space models, focusing on multivariate models as well as on non-linear and non-Gaussian models. The availability of time series data in many fields of science and industry on the one hand, and the development of low-cost computational capabilities on the other, have resulted in a wealth of statistical methods aimed at parameter estimation and forecasting. This book brings together many of these methods, presenting an accessible and comprehensive introduction to state space models. A number of data sets from different disciplines are used to illustrate the methods and show how they are applied in practice. The R package BTSA, created for the book, includes many of the algorithms and examples presented. The book is essentially self-contained and includes a chapter summarising the prerequisites in undergraduate linear algebra, probability and statistics. An up-to-date and complete account of state space methods, illustrated by real-life data sets and R code, this textbook will appeal to a wide range of students and scientists, notably in the disciplines of statistics, systems engineering, signal processing, data science, finance and econometrics. With numerous exercises in each chapter, and prerequisite knowledge conveniently recalled, it is suitable for upper undergraduate and graduate courses.