Statistical Inference Of The Efficient Frontier Under Autocorrelated Asset Returns

We explain the variability of the mean-variance efficient frontier over time with a statistical three factor model. For an asset universe consisting of 22 stocks listed in Switzerland, the model explains more than 99% of the time variations in the efficient frontier.The three factors can be interpreted as level, slope and curvature effects. This result is similar to statistical factors that have been identified in the analysis of yield curve dynamics.We also show that the first factor which explains about 95% of the variability in the efficient frontier is highly correlated with average stock returns. This can be interpreted as evidence that the efficient frontier is mainly driven by time-variation in returns. Risk, on the other hand, measured by both asset volatilities and asset correlations, seems to be a second-order effect.

While mainstream financial theories and applications assume that asset returns are normally distributed, overwhelming empirical evidence shows otherwise. Yet many professionals don’t appreciate the highly statistical models that take this empirical evidence into consideration. Fat-Tailed and Skewed Asset Return Distributions examines this dilemma and offers readers a less technical look at how portfolio selection, risk management, and option pricing modeling should and can be undertaken when the assumption of a non-normal distribution for asset returns is violated. Topics covered in this comprehensive book include an extensive discussion of probability distributions, estimating probability distributions, portfolio selection, alternative risk measures, and much more. Fat-Tailed and Skewed Asset Return Distributions provides a bridge between the highly technical theory of statistical distributional analysis, stochastic processes, and econometrics of financial returns and real-world risk management and investments.

The composition of portfolios is one of the most fundamental and important methods in financial engineering, used to control the risk of investments. This book provides a comprehensive overview of statistical inference for portfolios and their various applications. A variety of asset processes are introduced, including non-Gaussian stationary processes, nonlinear processes, non-stationary processes, and the book provides a framework for statistical inference using local asymptotic normality (LAN). The approach is generalized for portfolio estimation, so that many important problems can be covered. This book can primarily be used as a reference by researchers from statistics, mathematics, finance, econometrics, and genomics. It can also be used as a textbook by senior undergraduate and graduate students in these fields.

Discover a masterful exploration of the fallacies and challenges of asset allocation In Asset Allocation: From Theory to Practice and Beyond—the newly and substantially revised Second Edition of A Practitioner’s Guide to Asset Allocation—accomplished finance professionals William Kinlaw, Mark P. Kritzman, and David Turkington deliver a robust and insightful exploration of the core tenets of asset allocation. Drawing on their experience working with hundreds of the world’s largest and most sophisticated investors, the authors review foundational concepts, debunk fallacies, and address cutting-edge themes like factor investing and scenario analysis. The new edition also includes references to related topics at the end of each chapter and a summary of key takeaways to help readers rapidly locate material of interest. The book also incorporates discussions of: The characteristics that define an asset class, including stability, investability, and similarity The fundamentals of asset allocation, including definitions of expected return, portfolio risk, and diversification Advanced topics like factor investing, asymmetric diversification, fat tails, long-term investing, and enhanced scenario analysis as well as tools to address challenges such as liquidity, rebalancing, constraints, and within-horizon risk. Perfect for client-facing practitioners as well as scholars who seek to understand practical techniques, Asset Allocation: From Theory to Practice and Beyond is a must-read resource from an author team of distinguished finance experts and a forward by Nobel prize winner Harry Markowitz.

A comprehensive reference work for teaching at graduate level and research in empirical finance. The chapters cover a wide range of statistical and probabilistic methods applied to a variety of financial methods and are written by internationally renowned experts.

Abstract: In this dissertation, I focus on econometric issues arising in the fields of Financial Economics. In the first chapter, I study return predictability in international equity markets focusing on the effects of the bias and spurious regression problems for statistical inference. The slope coefficient estimator in predictive regressions for stock returns is biased in the presence of a lagged stochastic regressor. Spurious regression may also occur if the underlying expected return is highly persistent. I consider the effect of these biases in the presence of data mining for the predictive variables. I find that the two biases can reinforce or offset each other, depending on the parameters of the model. I present a new bias expression valid with an unobserved true expected returns and re-evaluate return predictability in international equity markets adjusting for data mining associated with both effects. The second chapter studies tests for structural changes in the trend function of a univariate time series that are robust to whether the noise component is stationary (I (0)) or contains an autoregressive unit root (I (1)). The tests of interest are the robust procedures recently proposed by Perron and Yabu (2009) and Harvey, Leybourne and Taylor (2009), both of which attain the same limit distribution under I (0) and I (1) errors. We compare their finite sample size and power under different data-generating processes for the noise components. We apply the tests to a large historical panel of real exchange rates with respect to the U.S. dollar for 19 countries and document simultaneous shifts in level and trend for many series. The third chapter studies the sampling interval effect in estimating capital asset pricing models. In past empirical studies, the beta coefficient estimates are documented to be sensitive to the sampling interval used for returns. We provide a theoretical framework to explain this sampling interval effect. We show that it can be attributable to the existence of transitory components in stock prices, and provide empirical evidence supporting its presence.

The interaction between mathematicians, statisticians and econometricians working in actuarial sciences and finance is producing numerous meaningful scientific results. This volume introduces new ideas, in the form of four-page papers, presented at the international conference Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), held at Universidad Carlos III de Madrid (Spain), 4th-6th April 2018. The book covers a wide variety of subjects in actuarial science and financial fields, all discussed in the context of the cooperation between the three quantitative approaches. The topics include: actuarial models; analysis of high frequency financial data; behavioural finance; carbon and green finance; credit risk methods and models; dynamic optimization in finance; financial econometrics; forecasting of dynamical actuarial and financial phenomena; fund performance evaluation; insurance portfolio risk analysis; interest rate models; longevity risk; machine learning and soft-computing in finance; management in insurance business; models and methods for financial time series analysis, models for financial derivatives; multivariate techniques for financial markets analysis; optimization in insurance; pricing; probability in actuarial sciences, insurance and finance; real world finance; risk management; solvency analysis; sovereign risk; static and dynamic portfolio selection and management; trading systems. This book is a valuable resource for academics, PhD students, practitioners, professionals and researchers, and is also of interest to other readers with quantitative background knowledge.

This text aims to provide practical models and methods for the quantitative analysis of financial asset prices, construction of various portfolios, and computer-assisted trading systems. In particular, it should be helpful reading for Quants (quantitatively-inclined analysts) in financial industries, financial engineers in investment banks; securities companies, derivative-trading companies, and software houses who are developing portfolio trading systems; graduate students and specialists in the areas of finance, business, hardbound economics, statistics, financial engineering; investors who are interested in Japanese financial markets. Throughout the book the emphasis is placed on the originality and usefulness of models and methods for the construction of portfolios and investment decision making, and examples are provided to demonstrate, analysis, models for Japanese financial markets.

This paper demonstrates that modest amounts of security autocorrelation can cause the composition of an efficient frontier constructed on the basis of one holding period length assumption to differ substantially from the composition of an efficient frontier constructed on the basis of another holding period length assumption. This will be true even if the efficient frontiers are constructed using the same data base over the same total time span. This finding is derived mathematically and demonstrated empirically. The paper discusses the serious consequences of this finding.