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on Risk Management |
| By: | Weihuan Huang |
| Abstract: | CoVaR (conditional value-at-risk) is a crucial measure for assessing financial systemic risk, which is defined as a conditional quantile of a random variable, conditioned on other random variables reaching specific quantiles. It enables the measurement of risk associated with a particular node in financial networks, taking into account the simultaneous influence of risks from multiple correlated nodes. However, estimating CoVaR presents challenges due to the unobservability of the multivariate-quantiles condition. To address the challenges, we propose a two-step nonparametric estimation approach based on Monte-Carlo simulation data. In the first step, we estimate the unobservable multivariate-quantiles using order statistics. In the second step, we employ a kernel method to estimate the conditional quantile conditional on the order statistics. We establish the consistency and asymptotic normality of the two-step estimator, along with a bandwidth selection method. The results demonstrate that, under a mild restriction on the bandwidth, the estimation error arising from the first step can be ignored. Consequently, the asymptotic results depend solely on the estimation error of the second step, as if the multivariate-quantiles in the condition were observable. Numerical experiments demonstrate the favorable performance of the two-step estimator. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.18658&r=rmg |
| By: | Kang Gao; Stephen Weston; Perukrishnen Vytelingum; Namid R. Stillman; Wayne Luk; Ce Guo |
| Abstract: | We propose the Chiarella-Heston model, a new agent-based model for improving the effectiveness of deep hedging strategies. This model includes momentum traders, fundamental traders, and volatility traders. The volatility traders participate in the market by innovatively following a Heston-style volatility signal. The proposed model generalises both the extended Chiarella model and the Heston stochastic volatility model, and is calibrated to reproduce as many empirical stylized facts as possible. According to the stylised facts distance metric, the proposed model is able to reproduce more realistic financial time series than three baseline models: the extended Chiarella model, the Heston model, and the Geometric Brownian Motion. The proposed model is further validated by the Generalized Subtracted L-divergence metric. With the proposed Chiarella-Heston model, we generate a training dataset to train a deep hedging agent for optimal hedging strategies under various transaction cost levels. The deep hedging agent employs the Deep Deterministic Policy Gradient algorithm and is trained to maximize profits and minimize risks. Our testing results reveal that the deep hedging agent, trained with data generated by our proposed model, outperforms the baseline in most transaction cost levels. Furthermore, the testing process, which is conducted using empirical data, demonstrates the effective performance of the trained deep hedging agent in a realistic trading environment. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.18755&r=rmg |
| By: | Peter Albrecht; Evžen Kočenda; Evžen Kocenda |
| Abstract: | We provide a comprehensive assessment of volatility connectedness between the currencies of Central European (CE) countries using high-frequency data from 2009 to 2022. We assess asymmetries in connectedness (not investigated for CE currencies before) and document domination of the negative volatility, especially during periods of economic distress. We further bring the first statistical evidence based on a formal bootstrap-after-bootstrap procedure of Greenwood-Nimmo et al. (2023) that increases in connectedness are linked with systematic events, and identify the impact of specific domestic and global shocks. We find that for eight out of eight endogenously selected global events, there was an increase in connectedness within a maximum of one business month from the event's occurrence. Finally, we show that the connectedness is linked with its potential drivers: uncertainty, liquidity, and economic activity whose impacts differ substantially. Our results are robust with respect to a volatility measure and provide direct policy implications for portfolio composition and hedging. |
| Keywords: | volatility connectedness, Central European currencies, asymmetries in volatility connectedness, bootstrap-after-bootstrap procedure, portfolio composition and hedging |
| JEL: | C58 F31 F65 G01 G15 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10728&r=rmg |
| By: | Aya Nasreddine; Yasmine Essafi Zouari |
| Abstract: | In this article, we use the framework of inflation beta to test the capacity of physical residential real estate to hedge against inflation and its components, and compare it to the inflation hedge ability of various financial assets. Specifically, the housing asset is represented by the residential market in the communes of the “Grand Paris” metropolis with the different components of inflation. We start by analyzing the residential market in this area, its fundamentals, characteristics and dynamic. Then, applying the hierarchical clustering technique, we divide the Greater Paris area into five homogenous groups of communes and test its hedging ability using both correlation and regression analysis. Residential assets are confirmed to be a hedge against inflation, particularly against its unexpected component and thanks to its capital return rather than the rental return. On the other hand, the listed real estate does not provide the same hedging properties and thus cannot be considered as a substitute for this aim. |
| Keywords: | Direct housing; Grand Paris Metropolis; Hedging ability; Inflation |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_261&r=rmg |
| By: | Ian Dew-Becker; Stefano Giglio |
| Abstract: | The historical returns on equity index options are well known to be strikingly negative. That is typically explained either by investors having convex marginal utility over stock returns (e.g. crash/variance aversion) or by intermediaries demanding a premium for hedging risk. This paper examines the consistency of those explanations with returns on dynamically replicated, or synthetic, options. Theoretically, it derives conditions under which convex marginal utility leads synthetic options to also have negative excess returns. Empirically, synthetic options have CAPM alphas near zero over the period 1926--2022, in stark contrast to exchange-traded options. Over the last 15 years, returns on traded options have converged to those on synthetic options -- with the variance risk premium shrinking towards zero -- while various drivers of the cost and risk of hedging options exposures have declined, consistent with a model in which intermediaries drive option prices. |
| JEL: | G11 G12 G13 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31833&r=rmg |
| By: | Haoyang Cao; Haotian Gu; Xin Guo; Mathieu Rosenbaum |
| Abstract: | Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. In this paper, we propose a novel concept of transfer risk and and analyze its properties to evaluate transferability of transfer learning. We apply transfer learning techniques and this concept of transfer risk to stock return prediction and portfolio optimization problems. Numerical results demonstrate a strong correlation between transfer risk and overall transfer learning performance, where transfer risk provides a computationally efficient way to identify appropriate source tasks in transfer learning, including cross-continent, cross-sector, and cross-frequency transfer for portfolio optimization. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.03283&r=rmg |
| By: | Ben Höhn; Sven Bienert |
| Abstract: | We present an assessment of contemporary climate risk quantification tools utilized by the real estate industry. Given the escalating frequency and severity of extreme weather events, it is imperative for market participants to employ reliable and robust risk quantification methods to manage their portfolios. Our study evaluates the methods currently used against multiple criteria, including database quality, quantification methodology, transparency, actuality, scope, geographical suitability, and others. We employ publicly available information and the outcomes of a questionnaire sent to providers of risk quantification software. Furthermore, we calculate the physical climate risk for a pre-defined portfolio using the identified climate risk quantification methods. Our findings indicate that many of the available tools lack transparency in their methodology and that there are significant discrepancies in the physical risk quantifications. This paper contributes to the given objective in various ways. Specifically, it offers an overview of the available tools used by market participants, it defines criteria that can be employed to assess climate risk tools, with an emphasis on the real estate industry, and it identifies the weaknesses and strengths of the different approaches. |
| Keywords: | Risk quantification tools; Transparency |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_188&r=rmg |
| By: | Guan, Bo (Cardiff Business School); Mazouz, Khelifa (Cardiff Business School); Xu, Yongdeng (Cardiff Business School) |
| Abstract: | This study uses the Multiplicative Error Model (MEM) to explore asymmetric volatility spillovers between crude oil and other major asset markets. We have extended the MEM of Engle et al. (2012) to include asymmetric volatility spillovers and developed the spillover balance as well as asymmetric spillover indexes. We have then allowed these indexes to vary over time. Our results reveal that the stock market is the dominant contributor to volatility spillover, while the crude oil is mostly the volatility spillover recipient. The asymmetric spillover effects are predominantly negative in the stock and crude oil markets and positive in the bond market. We further show that the spillover indexes are dynamic and influenced by specific events, such as the global financial crisis and the COVID-19 pandemic, as well as varying economic conditions. |
| Keywords: | asymmetric volatility spillovers, global asset markets, Multiplicative Error Model (MEM), spillover balance index |
| JEL: | G10 G15 C58 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:cdf:wpaper:2023/27&r=rmg |
| By: | Stephen Lee |
| Abstract: | We compare the performance of Markowitz’s mean-variance analysis based on Pearson correlation coefficients (PCC) and Partial-correlation coefficients (PACC); using monthly return data on 11 REIT sectors and the EREIT index, over the period from January 1994 to December 2022. The results of the empirical analysis show a number of features of interest. First, we find that the off-diagonal elements of the PCC matrix are significantly positive. In contrast, the corresponding off-diagonal elements of the PACC matrix, conditioned on the EREIT index, are generally insignificantly different from zero and in a number of cases significantly negative. Second, we find that PACC-based minimum-risk portfolios (MRPs) are more diversified than PCC-based portfolios. While the PAC-based weights of the MRP change significantly from period to period, the MRP weights using PACC are very stable. The stability of the PACC portfolio weights means that the weights in one period can be used in the next period, with very little increase in portfolio risk. Therefore, the extent of rebalancing is limited in PACC portfolios, which will minimise transaction costs. In contrast, the instability of the PCC-based portfolio weights means that holding the optimum weights of the MRP in one sub-period leads to portfolio risks that are on average 30% greater than with an MRP optimum solution in the next sub-period. In other words, the MRPs estimated by the PACC method are substantially better than that by PCC-based methods. We conclude, therefore that portfolios constructed by PACC are more useful and meaningful for risk management and optimal portfolio selection than Pearson correlation-based methods. |
| Keywords: | Mean-Variance Analysis; Partial correlations; Pearson Correlations; REIT Sectors |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_156&r=rmg |
| By: | Lloyd, Simon; Fernández-Gallardo, Álvaro; Manuel, Ed |
| Abstract: | We estimate the causal effects of macroprudential policies on the entire distribution of GDP growth for advanced European economies using a narrative-identification strategy in a quantile-regression framework. While macroprudential policy has near-zero effects on the centre of the GDP-growth distribution, tighter policy brings benefits by reducing the variance of future growth, significantly boosting the left tail while simultaneously reducing the right. Assessing a range of channels through which these effects materialise, we find that macroprudential policy particularly operates through ‘credit-at-risk’: it reduces the right tail of future credit growth, dampening booms, in turn reducing the likelihood of extreme GDP-growth outturns. JEL Classification: E32, E58, G28 |
| Keywords: | growth-at-risk, macroprudential policy, narrative identification, quantile local projections |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:srk:srkwps:2023145&r=rmg |
| By: | Stark, Oded (University of Bonn) |
| Abstract: | I study attitudes towards risk taking in cases where a person relates to others positively, namely altruistically. This study is needed because it is unclear how altruism influences the inclination of an altruistic person to take risks. Will this person's risk-taking behavior differ if the utility of another person does not enter his utility function? Does being altruistic cause a person to become more reluctant to take risks because a risky undertaking turning sour will also damage his ability to make altruistic transfers? Or does altruism induce a person to resort to risky behavior because the reward for a successful outcome is amplified by the outcome facilitating a bigger transfer to the beneficiary of the altruistic act? Specifically, holding constant other variables, I ask: is an altruistic person more risk averse or less risk averse than a comparable person who is not altruistic? In response to this question, using a simple model in which preferences are represented by a logarithmic utility function, I show that an altruistic person who is an active donor (benefactor) is less risk averse than a comparable person who is not altruistic: altruism is a cause of greater willingness to take risks. The finding that the altruism trait causes greater willingness to take risks has not previously been noted in the existing literature. |
| Keywords: | altruism, altruistic transfers, relative risk aversion, intensity of altruism |
| JEL: | D01 D64 D81 G41 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16573&r=rmg |
| By: | Wei-Xing Zhou; Yun-Shi Dai; Kiet Tuan Duong; Peng-Fei Dai |
| Abstract: | The ongoing Russia-Ukraine conflict between two major agricultural powers has posed significant threats and challenges to the global food system and world food security. Focusing on the impact of the conflict on the global agricultural market, we propose a new analytical framework for tail dependence, and combine the Copula-CoVaR method with the ARMA-GARCH-skewed Student-t model to examine the tail dependence structure and extreme risk spillover between agricultural futures and spots over the pre- and post-outbreak periods. Our results indicate that the tail dependence structures in the futures-spot markets of soybean, maize, wheat, and rice have all reacted to the Russia-Ukraine conflict. Furthermore, the outbreak of the conflict has intensified risks of the four agricultural markets in varying degrees, with the wheat market being affected the most. Additionally, all the agricultural futures markets exhibit significant downside and upside risk spillovers to their corresponding spot markets before and after the outbreak of the conflict, whereas the strengths of these extreme risk spillover effects demonstrate significant asymmetries at the directional (downside versus upside) and temporal (pre-outbreak versus post-outbreak) levels. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.16850&r=rmg |
| By: | Takao Asano (Okayama University); Yusuke Osaki (Waseda University) |
| Abstract: | This paper considers a portfolio problem with one safe asset and one risky asset in the presence of background risk. We assume that the background risk is a non-financial variable and it is correlated to financial risk. The aim of this paper is to investigate the effect of correlation on portfolio choices. While we find that an increase in correlation lowers (raises) the expected utility for mixed correlation averse (seeking) individuals, contrary to intuition, it does not necessarily reduce (increase) the investment in the risky asset. We determine the conditions needed to reduce (increase) the investment and find that these conditions can be related to cross risk apportionment, which is the type of preferences for the combination of good and bad. We also introduce ambiguity into the correlation and investigate its effects on the portfolio choices. |
| Keywords: | Ambiguity, Bivariate Utility Function, Linear Payoff, Mixed Correlation Aversion (Seekingness), Background Uncertainty, Portfolio Choice |
| JEL: | D81 D91 G11 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:kyo:wpaper:1098&r=rmg |
| By: | Philippe Goulet Coulombe (University of Quebec in Montreal); Mikael Frenette (University of Quebec in Montreal); Karin Klieber (Oesterreichische Nationalbank) |
| Abstract: | We reinvigorate maximum likelihoode stimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context. First, the hemispheres share a common core at the entrance of the network which accommodates for various forms of time variation in the error variance. Second, we introducea volatility emphasis constraint that breaks mean/variance indeterminacy in this class of overparametrized nonlinear models. Third, we conduct a blocked out-of-bag reality check to curb overfitting in both conditional moments.Fourth, the algorithm utilizes standard deep learning software and thus handles large datasets – both computationally and statistically. Ergo, our Hemisphere Neural Network (HNN) provides proactive volatility forecasts based on leading indicators when it can, and reactive volatility based on the magnitude of previous prediction errors when it must. We evaluate point and density forecasts with an extensive out-of-sample experiment and benchmark against a suite of models ranging from classics to more modern machine learning-based offerings. In all cases, HNN fares well by consistently providing accurate mean/variance forecasts for all targets and horizons. Studying the resulting volatility paths reveals its versatility, while probabilistic forecasting evaluation metrics showcase its enviable reliability. Finally, we also demonstrate how this machinery can be merged with other structured deep learning models by revisiting Goulet Coulombe(2022)’s Neural Phillips Curve. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:bbh:wpaper:23-04&r=rmg |
| By: | Hendrik Jenett |
| Abstract: | This study analyses the time-varying composition of real estate values by using an artificial neural network approach to identify whether and how certain indicators’ impacts on property values fluctuate over time. Therefore, cross-sectional property and macroeconomic data from the United States is applied, spanning a period from 1999 to 2021. In times of normal economic activity, property values are made up of two-thirds of physical attributes and one-third of the macroeconomic environment. During crises periods and times of high uncertainty, like the Global Financial Crisis, the share of the economies impact increases by roughly 5%, meaning that sudden economic changes have a higher impact on property values during crises periods versus normal times. However, these changes in the composition of real estate values varies even from one crisis to another, which confirms the dynamic relationship between the US macroeconomy and the housing market. Moreover, this study provides evidence that neural networks are capable of detecting non-linearities in property values especially during times of financial volatility. |
| Keywords: | Artificial Neural Network; Explainable Artificial Intelligence; Macroeconomy; Valuation |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_183&r=rmg |
| By: | Julia Braun |
| Abstract: | After the great turmoil of the latest financial crisis, the criticism of the regulatory frameworks became increasingly stronger. The rules that banks needed to comply with are presumed to be procyclical and unable to prevent and mitigate the extent of strong financial and economic cycles. As a result, Basel III introduced a set of macroprudential tools to overcome these regulatory shortfalls. One tool that strives to counteract the issue of procyclicality is the countercyclical capital buffer (CCyB). This paper introduces a heterogeneous agent-based model that investigates the implication of the new regulatory measure. We develop a housing and a financial market where economic agents trade residential property that is financed by financial institutions. To examine the macroeconomic performance of the CCyB, we evaluate the dynamics of key stability indicators of the housing and the financial market under four different market conditions: in an undisturbed market and in times of three different structural shocks. Computational experiments reveal that the CCyB is effective in stabilizing the housing and the financial market in all market settings. But the extent of the stabilizing effect varies according to market conditions. In the shock scenarios, the CCyB performs better in dampening market fluctuations and increasing banking soundness. Although the new macroprudential tool helps to mitigate economic fluctuations and to stabilize market conditions in the aftermath of a crisis, it is not able to prevent any of the crises tested. |
| Keywords: | Agent-Based Model; Basel III; Countercyclical capital buffer; Housing market stability |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_18&r=rmg |
| By: | Bednarek, Peter; Briukhova, Olga; Ongena, Steven; von Westernhagen, Natalja |
| Abstract: | What is the impact of a sudden and sizeable increase in bank capital requirements on the lending activity by directly affected banks and by non-affected non-bank financial institutions (NBFIs)? To answer this question, we apply a difference-in-differences methodology around the capital exercise by the European Banking Authority (EBA) in 2011 with German credit register data. We find that insurance companies, financial enterprises, and factoring companies - but not leasing companies - and Non-EBA banks expand their corporate lending relative to EBA banks. In particular, NBFIs use the opportunity to expand their credit activities, in riskier and more competitive borrower segments, but NBFIs do not seem to rely on increased bank funding to finance this expansion. |
| Keywords: | non-bank financial intermediation, bank capital requirements, EBA capital exercise |
| JEL: | E50 G21 G23 G28 C33 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:279547&r=rmg |
| By: | Yun-Shi Dai; Ngoc Quang Anh Huynh; Qing-Huan Zheng; Wei-Xing Zhou |
| Abstract: | This paper adopts the random matrix theory (RMT) to analyze the correlation structure of the global agricultural futures market from 2000 to 2020. It is found that the distribution of correlation coefficients is asymmetric and right skewed, and many eigenvalues of the correlation matrix deviate from the RMT prediction. The largest eigenvalue reflects a collective market effect common to all agricultural futures, the other largest deviating eigenvalues can be implemented to identify futures groups, and there are modular structures based on regional properties or agricultural commodities among the significant participants of their corresponding eigenvectors. Except for the smallest eigenvalue, other smallest deviating eigenvalues represent the agricultural futures pairs with highest correlations. This paper can be of reference and significance for using agricultural futures to manage risk and optimize asset allocation. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.16849&r=rmg |
| By: | Oytun Ha\c{c}ar{\i}z; Torsten Kleinow; Angus S. Macdonald |
| Abstract: | We revisit surplus on general life insurance contracts, represented by Markov models. We classify technical bases in terms of boundary conditions in Thiele's equation(s), allowing more general regulations than Scandinavian-style `first-order/second-order' regimes, and replacing the traditional retrospective policy value. We propose a `canonical' model with three technical bases (premium, valuation, accumulation) and show how each pair of bases defines premium loadings and surplus. Along with a `true' or `real-world' experience basis, this expands fundamental results of Ramlau-Hansen (1988a). We conclude with two applications: lapse-supported business; and the retrospectively-oriented regime proposed by M{\o}ller & Steffensen (2007). |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.16927&r=rmg |
| By: | Wilhelm Breuer; Jonas Englert |
| Abstract: | Institutional investors, such as insurance companies and funds, can increase the risk-adjusted return of their real estate portfolio by directly or indirectly granting real estate loans. This is the central finding of our study. Real estate loans, also known as real estate private debt (REPD), which are not issued through bonds but are granted directly by banks and institutional investors, are an ideal addition to a real estate portfolio, according to the study. Contrary to its relevance, this topic has received little attention in the literature so far, so that there are currently no comparable studies. Within the scope of the work, the diversification potentials that can be achieved in this way were examined. For the US market, the Giliberto Levy Commercial Mortgage Performance Index (GLCMPI) provides publicly accessible data for an analysis of the performance of REPD. The "NCREIF Property Index" (NPI) was used as a benchmark for the property portfolio to be diversified. Portfolio theory formed the theoretical foundation of the study. The indices were evaluated over the largest possible observation period from 1978 – 2021. |
| Keywords: | Diversification; Investment Management; Real Estate Private Debt; Sharp Ratio |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_234&r=rmg |
| By: | Brandon Goldstein; Julapa Jagtiani; Catharine Lemieux |
| Abstract: | A growing portion of consumer credit has recently been devoted to unsecured personal installment loans. Fintech firms have been active players in this market, with an increasing market share, while the market share of banks has declined. Studies of fintech lending have shown that their digital access and ability to leverage alternative data have increased accessibility in underserved areas, enabled consumers with thin credit files to obtain credit, and provided a lower cost alternative to long-term credit card financing. This paper exams three questions: (1) Do proprietary loan rating systems accurately predict the likelihood of default? (2) Can a proprietary loan rating system, leveraging alternative data, that was developed in a favorable economic period continue to perform well under adverse economic conditions (such as the COVID-19 pandemic)? (3) Have fintechs been “cream skimming, ” i.e., underpricing the cost of credit to top-tier customers? This study uses data from LendingClub, one of the largest fintech lenders in the personal loan market. We find that LendingClub’s loan rating system is superior to traditional measures of credit risk when predicting the likelihood of default and that the loan rating system continued to perform well during the pandemic period. Finally, we find no evidence of cream skimming. |
| Keywords: | Fintech; peer-to-peer (P2P); alternative data; financial inclusion; credit access; COVID-19; fintech loan default; cream skimming; fintech loan rate |
| JEL: | G21 G28 G18 L21 |
| Date: | 2023–11–10 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedpwp:97272&r=rmg |
| By: | Nandkumar Nayar; McKay Price; Ke Shen |
| Abstract: | Recent research has shown that macroeconomic uncertainty is a signifcant factor that is contemporaneously incorporated into asset returns. Therefore, it should not have a role in predicting future returns. At the same time, separate research has demonstrated that illiquidity is related to future returns. We examine the interplay between these two dynamics in a commercial real estate setting, where (il)liquidity is a defining characteristic of the asset class. Empirical tests confirm the absence of return predictability for liquid assets (publicly traded property portfolios). However, we find significant return predictability predicated on ex ante macroeconomic uncertainty when we examine assets that are not as liquid (directly held property portfolios). Our findings are robust to several refinements, including adjustments for delays in the transaction closing process to establish transaction prices in the directly held market, controls for leverage inherent in publicly traded real estate asset returns, and pro-cyclical liquidity variation in private real estate markets. |
| Keywords: | commercial real estate; Liquidity; Macroeconomic uncertainty; Price return predictability |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_23&r=rmg |