Asset Classes and Portfolio Diversification: Evidence from a Stochastic Spanning Approach

We propose a stochastic spanning approach to assess whether a traditional portfolio of stocks and bonds spans augmented portfolios including commodities, foreign exchange, and real estate. We empirically show that in all seven portfolio combinations, the augmented portfolio is not spanned by the traditional one. Our results are further confirmed by both parametric and non-parametric tests in an out-of-sample setting. Therefore, traditional investors can generally benefit in terms of higher Sharpe ratios from augmenting their portfolio with alternative asset classes. Additional analysis demonstrates that diversification benefits can be explained by the current state of the U.S. economy and stock markets

Frontiers of Asset Pricing

This book is comprised of articles published in a Special Issue of the Journal of Risk and Financial Management entitled "Frontiers in Asset Pricing" with Guest Editors Professor James W. Kolari and Professor Seppo Pynnonen. The book contains papers in various areas related to asset pricing: (1) models; (2) multifactors; (3) theory; (4) empirical tests; (5) applications; (6) other asset classes; and (7) international tests

COVARIANCE MATRIX CONSTRUCTION AND ESTIMATION: CRITICAL ANALYSES AND EMPIRICAL CASES FOR PORTFOLIO APPLICATIONS

The thesis contributes to the financial econometrics literature by improving the estimation of the covariance matrix among financial time series. To such aim, existing econometrics tools have been investigated and improved, while new ones have been introduced in the field. The main goal is to improve portfolio construction for financial hedging, asset allocation and interest rates risk management. The empirical applicability of the proposed innovations has been tested trough several case studies, involving real and simulated datasets. The thesis is organised in three main chapters, each of those dealing with a specific financial challenge where the covariance matrix plays a central role. Chapter 2 tackles on the problem of hedging portfolios composed by energy commodities. Here, the underlying multivariate volatility among spot and futures securities is modelled with multivariate GARCH models. Under this specific framework, we propose two novel approaches to construct the covariance matrix among commodities, and hence the resulting long-short hedging portfolios. On the one hand, we propose to calculate the hedge ratio of each portfolio constituent to combine them later on in a unique hedged position. On the other hand, we propose to directly hedge the spot portfolio, incorporating in such way investor\u2019s risk and return preferences. Trough a comprehensive numerical case study, we assess the sensitivity of both approaches to volatility and correlation misspecification. Moreover, we empirically show how the two approaches should be implemented to hedge a crude oil portfolio. Chapter 3 focuses on the covariance matrix estimation when the underlying data show non\u2013Normality and High\u2013Dimensionality. To this extent, we introduce a novel estimator for the covariance matrix and its inverse \u2013 the Minimum Regularised Covariance Determinant estimator (MRCD) \u2013 from chemistry and criminology into our field. The aim is twofold: first, we improve the estimation of the Global Minimum Variance Portfolio by exploiting the MRCD closed form solution for the covariance matrix inverse. Trough an extensive Monte Carlo simulation study we check the effectiveness of the proposed approach in comparison to the sample estimator. Furthermore, we take on an empirical case study featuring five real investment universes characterised by different stylised facts and dimensions. Both simulation and empirical analysis clearly demonstrate the out\u2013of\u2013sample performance improvement while using the MRCD. Second, we turn our attention on modelling the relationships among interest rates, comparing five covariance matrix estimators. Here, we extract the principal components driving the yield curve volatility to give important insights on fixed income portfolio construction and risk management. An empirical application involving the US term structure illustrates the inferiority of the sample covariance matrix to deal with interest rates. In chapter 4, we improve the shrinkage estimator for four risk-based portfolios. In particular, we focus on the target matrix, investigating six different estimators. By the mean of an extensive numerical example, we check the sensitivity of each risk-based portfolio to volatility and correlation misspecification in the target matrix. Furthermore, trough a comprehensive Monte Carlo experiment, we offer a comparative study of the target estimators, testing their ability in reproducing the true portfolio weights. Controlling for the dataset dimensionality and the shrinkage intensity, we find out that the Identity and Variance Identity target estimators are the best targets towards which to shrink, always holding good statistical properties

Commodities as an Asset Class

Commodities have become increasingly important as an additional source of diversification. However, commodities are relatively unexplored as an asset class. Within the context of the financialization of commodity markets, we study three fundamental questions about commodities as an asset class: What determines commodity prices, why do some commodities offer higher returns than other commodities, and is there momentum in commodity markets? In this thesis, the net convenience yield as a latent payoff of a commodity is in particular taken into account to answer these questions

Harmful diversification: evidence from alternative investments

Alternative assets have become as important as equities and fixed income in the portfolios of major investors, and so their diversification properties are also important. However, adding five alternative assets (real estate, commodities, hedge funds, emerging markets and private equity) to equity and bond portfolios is shown to be harmful for US investors. We use 19 portfolio models, in conjunction with dummy variable regression, to demonstrate this harm over the 1997-2015 period. This finding is robust to different estimation periods, risk aversion levels, and the use of two regimes. Harmful diversification into alternatives is not primarily due to transactions costs or non-normality, but to estimation risk. This is larger for alternative assets, particularly during the credit crisis which accounts for the harmful diversification of real estate, private equity and emerging markets. Diversification into commodities, and to a lesser extent hedge funds, remains harmful even when the credit crisis is excluded

Effects of selected family characteristics on interrelated components of household asset portfolios

The effects of selected family characteristics on interrelated components of household asset portfolios over a three-year time period were investigated. Specifically, this study attempted to conceptually define mental accounts, to identify own-adjustment and cross-adjustment characteristics of these mental accounts, to explore influences of selected family characteristics on these mental accounts, and to examine substantial effects of income on family portfolio behavior. Based on the behavioral life-cycle hypothesis, consumer demand theory, household production theory, and the stock adjustment hypothesis, a family portfolio behavior model was formulated for studying family saving behavior as reflected in household asset portfolios. A tobit model was utilized to estimate own- and cross-adjustment coefficients of the portfolio components, and short-term and equilibrium effects of family characteristics. The data were from the Survey of Consumer Finances conducted in 1983 and 1986. Findings strongly support the mental account hierarchy hypothesis which was reflected in the own- and cross-adjustment coefficients estimated. In addition, family income and education of the household head showed positive influences on various mental accounts. Age of the household head, employment status, family life cycle stage, house mortgage, home value, other assets, and other debts showed effects on some mental accounts. Income had a substantial influence on family portfolio behavior. The behavior of middle-income families was more consistent with the hypothesis of a mental account hierarchy than the other income groups, which implies diverse preferences for asset characteristics and varying financial needs of families at different income levels. This study has contributed to the body of knowledge of family saving behavior and increased the understanding of adaptivity and dynamics of family saving behavior. The research findings could be utilized by family finance educators and consultants, financial service marketers, and public policy makers in working successfully with different family types, marketing various financial instruments, and designing effective savings policies. In addition, this study has provided empirical evidence to assess existing theoretical models and to inspire the building of new theories

On the (almost) stochastic dominance of cryptocurrency factor portfolios and implications for cryptocurrency asset pricing

This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: Data are available from the authors on request, with the caveat that requestors should also be subscribers to parts of the dataset that are derived from commercial providers that require subscription, such as CRSP.Cryptocurrency returns are highly nonnormal, casting doubt on the standard performance metrics. We apply almost stochastic dominance, which does not require any assumption about the return distribution or degree of risk aversion. From 29 long–short cryptocurrency factor portfolios, we find eight that dominate our four benchmarks. Their returns cannot be fully explained by the three-factor coin model of Liu et al. So we develop a new three-factor model where momentum is replaced by a mispricing factor based on size and risk-adjusted momentum, which significantly improves pricing performance