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The authors analyze the equilibrium effects of non-tradable assets on optimal policy portfolios. They study how the existence of non-tradable assets impacts optimal asset allocation decisions of investors who own such assets and of investors who do not have access to non-tradable assets.

In this theoretical analysis, the authors analyze a model with tradable and non-tradable asset classes whose cash flows are jointly normally distributed. There are two types of investors, with and without access to non-tradable assets. All investors have constant absolute risk aversion preferences. The authors derive closed form solutions for optimal investor demand and equilibrium asset prices. They calibrated the model using US data for listed equity, bonds and private equity. Further, the authors illustrate the sensitivities of quantities and prices with respect to the main parameters.

The study finds that the existence of non-tradable assets has a large impact on optimal asset allocation. Investors with (without) access to non-tradable assets tilt their portfolios of tradable assets away from (toward) assets to which non-tradable assets exhibit positive betas.

The model provides important insights not only for investors holding non-tradable assets such as private equity but also for investors who do not have access to non-tradable assets. Investors who ignore the effect of non-tradable assets when reverse-engineering risk premia from asset covariances and market capitalizations might severely underestimate the equity risk premium.

The authors provide the first comprehensive analysis of the equilibrium effects of non-tradability of some assets on optimal policy portfolios. Thus, this paper goes beyond analyzing the effects of market imperfections on individual portfolio choices.

The purpose of this paper is to test the so-called “Sell in May” effect in globally listed private equity markets based on monthly data covering the period 2004–2017.

Ordinary least squares regressions, generalized autoregressive conditional heteroscedasticity regressions and robust regressions are used to investigate the existence of the “Sell in May” effect in globally listed private equity markets. Additionally, the authors conduct robustness checks by dividing the sample period into two subperiods: pre-financial and post-financial crisis periods.

The authors find limited statistically significant evidence for the “Sell in May” effect. In particular, the authors observed a statistically significant “Sell in May” effect when taking time-varying volatility into account. These findings indicate that the “Sell in May” effect is driven by time-varying volatility. By contrast, economic significance as measured by visual return inspection and the magnitude of the estimated “Sell in May” coefficients in combination with their positive signs was found to be considerable.

The findings are important for all kinds of investors and asset managers who are considering investing in listed private equity.

The authors present a novel study that examines the “Sell in May” effect for globally listed private equity markets by using LPX indices, offering valuable insight into this growing asset class.

The purpose of this paper is, to study macroeconomic risk factors driving the expected stock returns of listed private equity (LPE). The authors use LPE indices divided into different styles and regions from January 2004 to December 2016 and a set of country stock indices to estimate the macroeconomic risk profiles and corresponding risk premiums. Using a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock market βs that are greater than 1. A one-factor asset pricing model using world stock market returns as the only possible risk factor is rejected on the basis of generalized method of moments (GMM) orthogonality conditions. In contrast, using the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of economic policy uncertainty as additional risk factors, this multifactor model is able to price a cross-section of expected LPE returns. The risk-return profile of LPE differs from country equity indices. Consequently, LPE should be treated as a separate asset class.

Following Ferson and Harvey (1994), the authors use an unconditional asset pricing model to capture the structure of returns across LPE. The authors use 11 LPE indices divided into different styles and regions from January 2004 to December 2016, and a set of country stock indices as spanning assets to estimate the macroeconomic risk profiles and corresponding risk premiums.

Using a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock market ßs that are greater than 1. The authors estimate a one-factor asset pricing model using world stock market returns as the only possible risk factor by GMM. This model is rejected on the basis of the GMM orthogonality conditions. By contrast, a multifactor model built on the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of global economic policy uncertainty as additional risk factors is able to price a cross-section of expected LPE returns.

Given data availability, the authors’ sample is strongly influenced by the financial crisis and its aftermath.

Information about the risk profile of LPE is important for asset allocation decisions. In particular, it may help to optimally react to contemporaneous changes in economy-wide risk factors.

To the best of authors’ knowledge, this is the first LPE study which investigates whether a set of macroeconomic factors is actually priced and, therefore, associated with a non-zero risk premium in the cross-section of returns.

This paper aims to analyze the characteristics of stochastic volatility processes in globally listed private equity (LPE) markets, which are represented by nine global, regional and style indices, and reveals transmissions in the conditional variances between the different markets, based on weekly data covering the period January 2011 to December 2020.

The study uses the generalized autoregressive conditional heteroscedasticity [GARCH(p, q)] model and its exponential GARCH (EGARCH) and GARCH-in-mean extensions.

The estimates of the volatility models GARCH, EGARCH and GARCH-in-mean GARCH-M for testing the stylized properties persistence, asymmetry, mean reversion and risk premium lead to very different results, depending on the respective LPE index.

The knowledge of conditional volatilities of LPE returns as well as the detection of volatility transmissions between the different LPE markets under investigation serve to support asset allocation decisions with respect to risk management or portfolio allocation. Hence, the findings are important for all kinds of investors and asset managers who consider investments in LPE.

The authors present a novel study that examines the conditional variance for globally LPE markets by using LPX indices, offering valuable insight into this growing asset class.

The paper aims to investigate the performance determinants of European non-listed private equity real estate funds between 2001 and 2014.

Using a sample of 363 funds collected from the Inrev database, the analysis evaluated the impact of fees and other intrinsic characteristics of these funds, such as leverage, size and duration, on the funds’ performance, intending to enhance the understanding underlying their relationship.

The findings show a negative relationship between the return of the funds and redemption fee, performance fee and management fee. Conversely, marketing fees have a positive effect on performance. When analyzing the investment style, the results reveal inhomogeneous behaviors of leverage on funds’ performance. This variable has a positive impact on the return in core funds, while there is a negative relationship in value-added investments. Finally, the emphasis on the global financial crisis shows that the effects of the independent variables on the performance do not significantly change in different economic cycles.

The practical implication of the research is to understand whether an investor can direct its resources in a fund, leveraging on certain intrinsic characteristics that can be observed a priori.

Even if there is a considerable body of literature on determinants of performance in European non-listed real estate funds, little research has analyzed the role of fees in driving their results. Besides, this paper takes advantage of observations from different investment styles to emphasize the impact of higher or lower risk profiles and from the full economic cycle to understand the effects of the crisis period.

The study examines Asia Pacific (APAC) non-listed non-core real estate funds' capital calls (investor equity drawdowns) sequence for varying vehicle strategies.

Analysis starts with a cursory data interpretation that extracts a typical investors' equity drawdowns schedule. Thousands of simulations are then computed for each vehicle strategy for each year to further interpretation.

Data and methodological limitations notwithstanding, overall estimates suggest that funds exhibit a contrasting capital calls sequence. As a group, APAC non-core non-listed real estate funds call circa 76.3% of investors' committed capital during the first four years of the fund life. Single sector, single country and value added vehicles have a greater capital calls velocity compared to their multi sector, multi country and opportunity peers. However, the two fund groups exhibit a notable standard deviation heterogeneity of drawdowns.

Investors should therefore budget accordingly when choosing either of vehicle strategies to invest in.

The study adds additional evidence on the topic of capital calls velocity. Results should assist LPs with their non-listed APAC real estate funds investment programme further.

The purpose of this paper is to examine in which ways hedge funds contribute to financialization.

Two already identified conduits through which financialization operates are applied to hedge funds.

The paper finds that hedge funds drive the phenomenon of financialization in two major ways, i.e. the financialization of corporations, and the financialization of markets. Hence, hedge funds can be conceived as agents of change for financialization.

There are indications that hedge funds possess disciplinary power. Future research should address this pivotal point, even though such power will be difficult to prove empirically.

Hedge funds have been found to potentially increase market volatility. In times of crisis, stricter regulation of these investors that take excessive risks seems prudent.

Through linking “hedge funds” with “financialization” this paper closes a research gap. In addition, the so far rather structural debate about financialization benefits from the actor-centered approach of this paper.

This study aims to investigate the day-of-the-week (DoW) effect in globally listed private equity (LPE) markets using daily data covering the period 2004–2021.

To investigate the existence of the DoW effect in globally LPE markets, ordinary least squares regression, generalised autoregressive conditional heteroscedasticity (GARCH) regression and robust regressions are used. In addition, robustness audits are conducted by subdividing the sampling period into two sub-periods: pre-financial and post-financial crisis.

Limited statistically significant evidence is found for the DoW effect. By taking time-varying volatility into account, a statistically significant DoW effect can be observed, indicating that the DoW effect is driven by time-varying volatility. Economic significance is captured through visual inspection of average daily returns, which illustrate that Monday returns are lower than the other weekdays.

The results have important implications on whether to adopt a DoW strategy for investors in LPE. The findings show that higher returns on selected days of the week for certain indices are possible.

To the best of the author’s knowledge, this paper provides the first study to examine the DoW effect for globally LPE markets by using LPX indices and contributes valuable insights on this growing asset class.

This paper aims to examine the hedge, diversifier and safe haven properties of the global listed infrastructure sector and subsector indices against two traditional asset classes, stocks and bonds, and four alternative asset classes, including commodities, real estate, private equity and hedge funds during extreme negative stock market movements.

Using dynamic conditional correlation and quantile regression, the authors analyze a data set of 12 indices comprising listed infrastructure and traditional asset classes from 2010 to 2019.

Overall, the findings indicate that listed infrastructure acts as an effective diversifier but not as a strong safe haven or hedge when considered in a multiasset context. With minor exceptions, listed infrastructure cannot be concluded as a safe haven against other asset classes under investigation.

The present study has implications for institutional investors looking to incorporate infrastructure in their multiasset portfolios for increased portfolio diversification benefits.

Despite the increased influence of infrastructure as an asset class, to the best of the authors’ knowledge, this is the first study to investigate the hedge, safe haven and diversifying properties of infrastructure in a multi-asset context.