Determinants of financial distress and bankruptcy in highly levered transactions

Prior literature on highly levered transactions (levered buyouts or levered recapitalizations) has emphasized either changes in governance or the structuring of their financing in helping these firms avoid financial distress or bankruptcy. Observing a sample of HLTs over time, we observe that debt composition is a more critical influence than proposed changes in governance for the likelihood of an HLT avoiding financial distress or bankruptcy. Such evidence is consistent with the [Chemmanur, T. & Fulghieri, P. (1994). Reputation, renegotiation, and the choice between bank loans and publicly traded debt. Review of Financial Studies 7, 475–506] model and suggests that the critical factor is the ability to informally renegotiate debt terms with a few lenders.     

Selecting a discrete portfolio

We study the problem of selecting an optimal portfolio out of a finite set of available assets. Assets are characterized by their expected returns and the covariance matrix, and investors are assumed to have a mean–variance utility, that is, their utility function is linear in the mean and variance of the portfolio they hold.When assets are negatively correlated, or even when a slightly more general condition is satisfied, we provide an algorithm for selecting an optimal portfolio. We illustrate the usefulness of this algorithm by some comparative statics result. When assets can be positively correlated, we deliver a negative result regarding the existence of useful algorithms for selecting an optimal portfolio.     

多期最优资产组合选择模型分析研究

在对多期最优资产组合选择模型研究的基础上 ,着重分析了模型的求解 ,为证券投资者进行多期投资提供了科学的依据和方法。     

Structural analysis of portfolio risk using beta impulse response functions

We estimate the data generating process of daily excess returns of 20 major German stocks in a CAPM framework with time varying betas. Our sample spans a 23 year period from 1974 to 1996. An asymmetric dependence of volatility on lagged innovations is taken into account. We introduce beta impulse response functions to shed light on the structural implications of systematic risk associated with competing volatility models. The dependence of beta on news is characterized with respect to different sources (asset specific vs. market general news). The empirical results suggest that negative news emerging from the market involve a stronger impact on beta relative to positive news. Concerning firm specific news the opposite relation is found for the majority of the analysed data sets.     

Seasonality in firm-size portfolio returns: A nonparametric analysis

In this study the author uses stochastic dominance, a nonparametric method of portfolio performance analysis, to test for seasonality in firm-size portfolio return behavior. Stochastic dominance confirms the January effect, found in previous parametric studies, only for the smallest firm-size portfolio. It statistically eliminates the size effect for the larger firm-size portfolios in January and for all firm-size portfolios in the other months of the year. It is demonstrated that a market proxy problem and normality assumption violation may bias the parametric results. Nonparametric analysis, therefore, suggests that markets may be more efficient than parametric methods imply when model violations exist.     

VaR约束下的投资组合决策模型分析

VaR方法是目前国际上风险管理的主流方法之一。文章将VaR应用到Markwitz均值-方差模型中,构建和动态调整了投资者的最优投资组合,并分析了该组合模型的特性,提出了应用模型中应注意的问题。     

Bayesian portfolio selection using a multifactor model

This article develops a new portfolio selection method using Bayesian theory. The proposed method accounts for the uncertainties in estimation parameters and the model specification itself, both of which are ignored by the standard mean-variance method. The critical issue in constructing an appropriate predictive distribution for asset returns is evaluating the goodness of individual factors and models. This problem is investigated from a statistical point of view; we propose using the Bayesian predictive information criterion. Two Bayesian methods and the standard mean-variance method are compared through Monte Carlo simulations and in a real financial data set. The Bayesian methods perform very well compared to the standard mean-variance method.     

大型房地产企业组合投资的模型分析

文章利用现代组合投资理论建立了针对房地产企业的组合投资决策模型,力求为房地产企业的科学投资决策指出一条出路。     

Hedging inflation with individual US stocks: A long-run portfolio analysis

This paper examines whether individual stocks can act as inflation hedgers. We focus on longer investment horizons and construct in- and out-of-sample portfolios based on the long-run relationship (cointegration) of stock prices with respect to consumer prices. Empirical evidence suggests that investors are better off by holding a portfolio of stocks with higher long-run betas as part of asset selection and allocation strategy. Stocks that outperform inflation tend to be drawn from the Energy and Industrial sectors. Finally, we observe that the companies average inflation hedging ability declined steadily over the past ten years, while the number of firms that hedge inflation has decreased considerably after the recent downturn of the US economy.     

Minimum-cost portfolio insurance

Minimum-cost portfolio insurance is an investment strategy that enables an investor to avoid losses while still capturing gains of a payoff of a portfolio at minimum cost. If derivative markets are complete, then holding a put option in conjunction with the reference portfolio provides minimum-cost insurance at arbitrary arbitrage-free security prices. We derive a characterization of incomplete derivative markets in which the minimum-cost portfolio insurance is independent of arbitrage-free security prices. Our characterization relies on the theory of lattice-subspaces. We establish that a necessary and sufficient condition for price-independent minimum-cost portfolio insurance is that the asset span is a lattice-subspace of the space of contingent claims. If the asset span is a lattice-subspace, then the minimum-cost portfolio insurance can be easily calculated as a portfolio that replicates the targeted payoff in a subset of states which is the same for every reference portfolio.     

Continuous-time mean-variance portfolio optimization in a jump-diffusion market

In this paper, we take up an approach of (Lindberg, in Bernoulli, 15(2):464–474, 2009) who introduced a new parameterization of the Black–Scholes model that allows for an easy solution of the continuous-time Markowitz mean-variance problem. We generalize the results of (Lindberg, in Bernoulli, 15(2):464–474, 2009) to a jump-diffusion market setting and slightly correct the proof and the assertion of the main result. Further, we demonstrate the implications of the Lindberg parameterization for the stock price drift vector in different market settings, analyse the dependence of the optimal portfolio from jump and diffusion risk and finally indicate how to use the method. We particularly also show how the optimal strategy can be obtained with the restricted use of historical data.     

Robust portfolio optimization

We address the problem of estimating risk-minimizing portfolios from a sample of historical returns, when the underlying distribution that generates returns exhibits departures from the standard Gaussian assumption. Specifically, we examine how the underlying estimation problem is influenced by marginal heavy tails, as modeled by the univariate Student-t distribution, and multivariate tail-dependence, as modeled by the copula of a multivariate Student-t distribution. We show that when such departures from normality are present, robust alternatives to the classical variance portfolio estimator have lower risk.     

Project portfolio control and portfolio management performance in different contexts

This article investigates the nature and relationship of project portfolio control techniques and portfolio management performance, and how this relationship is moderated by situational idiosyncrasies of internal and external dynamics, industries, governance types, and geographic location. A worldwide questionnaire with 242 responses was used, of which 136 high‐performing responses were filtered out for quantitative analysis of best practices. Three portfolio control factors were identified: portfolio selection, portfolio reporting, and decision‐making style. Two measures for portfolio management performance were identified: achievement of desired portfolio results and achievement of project and program purpose. The results indicate that different portfolio control mechanisms are associated with different performance measures. A contingency model was developed, including moderating effects by contextual variables.     

Multiperiod security markets with differential information: Martingales and resolution times

We model multiperiod securities markets with differential information. A price system that admits no free lunches is related to martingales when agents have rational expectations. We introduce the concept of resolution time, and show that a better informed agent and a less informed agent must agree on the resolution times of commonly marketed events if they have rational expectations and if there are no free lunches. It then follows that if all elementary events are marketed for a less informed agent then any price system that admits no free lunches to a better informed agent must eliminate any private information asymmetry between the two. We provide an example of a dynamically fully revealing price system that is arbitrage free and yields elementarily complete markets.     

On mean-variance portfolio selection

Two concepts are introduced in this paper, personal security market lines and personal betas of assets, and applied in a novel approach to optimal portfolio selection. The procedure is shown to be analogous to asset selection decision rules developed in the context of the capital asset pricing model. However, the statistical problems associated with the latter are not shared by the new procedure.     

Distributional properties of portfolio weights

In this paper, we prove several distributional properties for optimal portfolio weights. The weights are estimated by replacing the parameters with the sample counterparts. All results for finite samples are made assuming normally distributed returns. We calculate the exact covariances for the weights obtained by the expected quadratic utility. Additionally we derive the multivariate density function of the global minimum variance portfolio and the univariate density of the tangency portfolio. We obtain the conditional density for the Sharpe ratio optimal weights and show that the expectations of the Sharpe ratio optimal weights do not exist. Moreover, we determine the asymptotic distributions of the estimated weights assuming that the returns follow a multivariate stationary Gaussian process.     

Testing out-of-sample portfolio performance

This paper studies the quality of portfolio performance tests based on out-of-sample returns. By disentangling the components of the out-of-sample performance, we show that the observed differences are driven largely by the differences in estimation risk. Our Monte Carlo study reveals that the puzzling empirical findings of inferior performances of theoretically superior strategies result mainly from the low power of these tests. Thus, our results provide an explanation as to why the null hypothesis of equal performance of the simple equally-weighted portfolio compared to many theoretically-superior alternative strategies cannot be rejected in many out-of-sample horse races. Our findings turn out to be robust with respect to different designs and the implementation strategies of the tests.For the applied researcher, we provide some guidance as to how to cope with the problem of low power. In particular, we make use of a novel pretest-based portfolio strategy to show how the information regarding performance tests can be used optimally.     

Tempering effects of (dependent) background risks: A mean-variance analysis of portfolio selection

In a mean variance framework, we analyse risk taking in the presence of a (possibly) dependent background risk, exemplified in a linear portfolio selection problem. We first characterise the comparative statics of changes in the distribution and dependence structure of the background risk. For unfair, undesirable and loss-aggravating increases in background risks (both dependent and independent), we then present necessary and sufficient restrictions on preferences such that greater background uncertainty leads to reduced risk taking. With mean-variance preferences, these restrictions boil down to simple conditions on the marginal rate of substitution between risk and return. They can be easily related to familiar notions such as risk vulnerability, properness or standardness.