We model optimal asset allocations for agent-investor coalitions in which expected utility for each party is regarded as risk-adjusted return and meets their individual minimum requirements; their weighted sum is maximized. The main components of the agent's business returns are included, based on fees and changes in forecast fees. The agent's need to secure future fees anticipates perceived investor response to portfolio performance. Agent motivations can dominate system behavior. This article leads step-by-step toward better communication through performance measurement and incentive contracts. Agent and investor agree on an explicit risk aversion parameter. More alignment with investor needs also avoids the conventional, but distracting, calculation of relative returns before investor portfolio return and benchmark return have each been risk adjusted. Examples also illuminate viable and problematic coalitions based on fee feasibility, mismatches in agent business return and investor return on assets, and differences between fixed expectations versus dynamic and risk-adjusted benchmarks. In sum, the framework promotes better-targeted communication from the investor to the agent and provides more opportunities for the agent to demonstrate value added.

This paper introduces a generalization of quantiles, order statistics, and concomitants that we term co-quantiles, and investigates their statistical properties. The probability density functions for the co-quantiles are obtained along with their moments under the assumption that the distribution of the underlying data are multivariate normal. In contrast to the conventional order statistics that rank and record the same attribute of a population, or concomitants that consider different attributes observed over the same time period, co-quantiles allow the ranking and recording of different attributes across different time periods. The co-quantile results naturally reduce to those for order statistics and concomitants, and generalize those on the distributions of linear combinations and the maxima of vector valued random variables obtained in Arellano-Valle and Genton (2007, 2008) and those on cross sectional momentum returns obtained in Kwon and Satchell (2018). By applying the results to momentum spillover returns, we establish theoretically that these returns are susceptible to sudden changes in the skewness and the kurtosis during periods of market uncertainty. Since momentum spillover and cross sectional momentum are structurally very similar, this provides a theoretical explanation for the momentum crashes reported in the empirical literature over such periods.

Group investment decisions confront the challenge of meeting diverse needs. Existing practice seems to be mostly ad hoc. Even where model based, it may be too complex or idealistic to be implemented. This article proposes an extension to the expected utility framework for simplified group asset allocation. The authors’ approach combines utility functions using positive linear weights, accommodating variations in risk aversion, tax treatment, allocation frequency, funding risks, and nominal versus inflation-adjusted returns. By incorporating personal portfolios within a portfolio network to be optimized simultaneously with a pooled portfolio, potential conflicts among group members are further reduced. The procedure employs multiple matrixes to represent return probabilities as they affect the ability to safeguard goals as viewed by each individual. Behavioral finance’s goal-based investing is simplified using Rubinstein utility to seek growth while reducing the probability of failure to meet group member goals. For those able to produce financial plans with positive surplus, Rubinstein utility also supports more objectively appropriate risk aversions. The outcome is a practical and rigorous method for strategic group asset allocation.

This paper considers portfolio construction issues for a 'mental accountant', who exhibits an S-shaped utility function with loss aversion and narrowly-frames their asset allocation decision. We argue that the presence of narrow framing does not circumvent the existence of a budget constraint, and explicitly incorporate this into the investor's portfolio selection problem. The assumption of narrow framing allows us to derive relatively simple expressions for the mental accountant's optimal asset allocation decision. Our findings indicate that the mental accountant operates similarly to a high-conviction investor, leading to a time-series momentum approach to investment.

Purpose - The purpose of this paper is to provide theory for some popular models and strategies used by practitioners in constructing optimal portfolios. King (2007), for example, advocated adding a diversification term to mean-variance problems to create better portfolios and provided clear empirical evidence that this is beneficial. Design/methodology/approach - The authors provide an analytical framework to help us understand different portfolio construction practices that may incorporate diversification and conviction strategies; this allows us to connect our analysis to ideas in psychophysics and behavioural finance. The critical psychological ideas are cognitive dissonance and entropy; the economics are based on expected utility theory. The empirical section uses the theory outlined and provides the basis for constructing such portfolios.Findings - The model presented allows the incorporation of different strategies within a mean-variance framework, ranging from diversification and conviction strategies to more ESG-oriented ones. The empirical analysis provides a practical application. Originality/value - To the best of the authors' knowledge, this model is the first to bridge the gap between portfolio optimisation and the psychological ideas mentioned in a coherent analytical framework.

In this paper, we investigate value creation by hedge funds using Berk and van Binsbergen's (2015) value-added. We find that, on average, a hedge fund manager extracts $0.76 million per month from the market. We provide strong evidence of persistence in value creation by hedge fund managers. Of three skill indicators—skill ratio, fee ratio, and total compensation—we find that total compensation best identifies the skilled manager out-of-sample. Investors in value-creating funds benefit from a better risk–return payoff. While hedge funds operate in a less competitive market than mutual funds, incentive fees do not indicate greater skill. The value that hedge funds can extract from the market depends on both the profitability and scalability of the investment strategy.

Heterogeneity in informational inefficiency in a cross-market virtual currency, such as Bitcoin, allows for the extraction of differential gains from a portfolio of investments over time. In this paper, we measure inefficiency in five country/region segmented Bitcoin markets based on dynamic estimation of the fractional integration order of their price series. Results reveal a timevarying and country-specific pattern of inefficiency in the five Bitcoin markets, although the degree of inefficiency in each market has declined over time. Further, we introduce a new decomposition method to disentangle components of the inefficiency degree. Results suggest that the total variation around the convergence benchmark has fallen, whilst the proportion due to the difference between convergence and efficiency has risen from approximately 77% in 2013 to almost 100% in 2020. Besides, evidence of convergence emerges until the outbreak of COVID-19, beyond which the inefficiency degree diverges measurably. We show that Bitcoin markets have become more efficient after the first-wave COVID era and then the nature of market segmentation has played a less important role, levelling the cross-market difference and thus reducing the potential for arbitrage.

Measure-valued differentiation (MVD) is a relatively new method for computing Monte Carlo sensitivities, relying on a decomposition of the derivative of transition densities of the underlying process into a linear combination of probability measures. In computing the sensitivities, additional paths are generated for each constituent distribution and the payoffs from these paths are combined to produce sample estimates. The method generally produces sensitivity estimates with lower variance than the finite difference and likelihood ratio methods, and can be applied to discontinuous payoffs in contrast to the pathwise differentiation method. However, these benefits come at the expense of an additional computational burden. In this paper, we propose an alternative approach, called the absolute measure-valued differentiation (AMVD) method, which expresses the derivative of the transition density at each simulation step as a single density rather than a linear combination. It is computationally more efficient than the MVD method and can result in sensitivity estimates with lower variance. Analytic and numerical examples are provided to compare the variance in the sensitivity estimates of the AMVD method against alternative methods.

In this paper, we quantify the economic gain from accounting for departures from normality for the mean-variance (MV) investor. We provide two models that account for the key empirical regularities of financial returns: stochastic volatility, asymmetric returns, heavy tails and tail dependence. We show that accounting for departures from normality leads to significant gains in expected utility commensurate with or exceeding typical active management fees. The majority of the uplift in expected utility derives from accounting for stochastic volatility.

Although there are many well-established models for valuing corporate debt and equity, option pricing literature rarely takes these models as their starting point. This happens in part because such models value equity as an option on the firm's assets, and options on equity then become compound options that cannot generally be priced analytically. In this article, the authors present a consistent and unified framework for valuing equity and options on equity within the 1994 Leland model. The authors show that it is possible to value not only European call and put options but also exotic options such as barriers and lookbacks in closed form. Moreover, the authors show that the model produces an implied volatility skew that is typically observed in the equity options market.

Errors in variables in linear regression continue to be a significant empirical issue in financial econometrics. We propose using the characteristic function (CF) to obtain estimates for linear models with errors in the variables. By assuming that the explanatory variable follows a flexible double gamma distribution, we obtain closed-form expressions for the analytic CF of the data generating process. We show that our method performs well relative to existing techniques that address error-in-variables (EIVs) through simulations. We further extend our CF technique to a multivariate setting where it continues to produce accurate estimates. We illustrate the performance of our procedure by estimating the capital asset pricing model and a two-factor model.

This paper reviews our previous published work and stresses its relevance for practitioners. We argue that modelling investment choice via utility functions is a very useful exercise but the standard approach for doing this, namely mean variance analysis, has a number of weaknesses. We offer an alternative, the generalized logarithmic utility function advocated by Rubinstein. This specification is much better suited to wealth management and to retail investment and issues of complexity and computation can be easily resolved. We discuss applications that reveal better results than the standard approach.JEL Classification: G00, G10, G11.

Recent macro-finance contributions explain a great deal of unconditional asset pricing by introducing persistent consumption risks and rare disasters. Only the volatility puzzles remain unresolved among the longer-established issues in this literature. Motivated by empirical finance contributions and conventional wisdom, we abstract from a consumption-centric analysis and let the asset-pricing kernel depend on habit formation and consumer confidence as a demand shifter correlated with consumption growth. The resulting model compares favorably with the literature explaining the risk-free rate volatility. Our findings justify using supplementary information to price assets while warning against neglecting a thorough analysis of consumption growth dynamics. We rationalize including confidence indicators in the definition of the demand shifter by drawing parallels to existing approaches such as wealth in the utility function and salience theory.

In this paper, we analyze the relative performances of pairs trading and cross-sectional momentum (CSM) strategies by comparing their expected returns. It is shown that the Sharpe ratio and the autocorrelation in the spread between the asset returns are the key factors in determining the relative performances of the two strategies, and an analytic expression for the condition under which one strategy outperforms the other is obtained in terms of these factors. It is also shown that the pairs trading strategy outperforms the CSM strategy in the majority of practically relevant situations.

Assuming the time series of random returns to be jointly elliptical, we derive a relationship between its conditional variance and the probability density function of the conditioning set. In the case that such a relationship is linear in a quadratic form for of the conditioning variables, we show that the probability density function of the conditioning variables is multivariate t. This result is then applied to models of conditionally random volatility and used to derive exact results for the GARCH(p,q) class of processes previously thought to be intractable.

While momentum benefits from persistent trends of the market, such strategies are unable to distinguish between upside and downside risk and suffer consequently. We propose a Partial Moment Momentum (PMM) trading strategy that is sensitive to the sign of risk and show risk-adjusted outperformance compared to plain momentum and volatility-adjusted momentum strategies. The outperformance is robust across multiple time periods and in particular during market downturns. Further analysis based on conventional linear factor models shows negligible exposure to factor risk for our PMM portfolio. Finally, the performance of our proposed strategy appears to be enhanced when time series momentum is present and allows for improved risk management by distinguishing between upside and downside risks.

Existing approaches have considered characteristics of Environmental, Social and Corporate Governance (ESG) focused investments from a return-oriented perspective without paying due consideration to investors' utility and how ESG features impact utility. We contribute to this literature by providing a model that captures the implications for investment if ESG is valued by the investor as well as wealth. We first present the necessary theory and discuss the rather challenging problem of calibration of the various risk and preference parameters. Using Thomson Reuters ESG data from 2002 to 2018, we provide further empirical evidence that investors who value ESG factors have improved utility which does not come at the cost of return performance.