Liquidity Risk of Private Assets: Evidence from Real Estate Markets

Investment in thinly traded private assets involves liquidity risk. Existing literature provides limited guidance as it mainly focuses on publicly traded security assets such as stocks and bonds. This paper develops an analytical tool for quantifying liquidity risk of private assets. Using commercial real estate as a model asset and under reasonable assumptions, we find that the magnitude of liquidity risk is too large to be ignored, especially in down markets when liquidity risk is a great concern.     

Safe Assets as Commodity Money

This paper presents a model in which safe assets are systemic because they are the medium of exchange in risky assets. It connects the literature from banking and finance on safe assets to the monetary literature on alternative monetary systems involving commodity money, interest bearing money, and private money creation. Because safe assets have intrinsic value, changes in their supply lead to changes in market efficiency. Additionally, because safe assets are costly to produce, there is overproduction of safe assets relative to the social optimum. When the model is calibrated to plausible liquidity premiums the resulting inefficiencies are not large.     

Credit Availability and Asset Pricing Dynamics in Illiquid Markets: Evidence from Commercial Real Estate Markets

This article examines credit frictions and asset pricing in public and private markets with varying liquidity. We find that a tightening in credit availability is negatively related to subsequent price movements in private and public commercial real estate markets. Assets trading in illiquid segments of these markets are also susceptible to a feedback effect whereby changes in asset prices predict subsequent changes in credit availability. Controlling for investor demand, our findings suggest credit constraints play an economically significant asset pricing role in markets that are both highly levered and relatively illiquid.     

Corporate Real Estate Ownership Implications: International Performance Evidence

In this paper we explore corporate real estate ownership internationally. Based on a sample of 4,636 companies from 18 industries and 9 countries we document distinct patterns and trends in the corporate ownership level of real estate. Real estate ownership appears to be driven by industrial rather than national differences, with corporate real estate ratios ranging between 0.13 for Business Services and 0.63 for the Mining sector. Overall, real estate ownership appears to be decreasing over time, which may be due to the gaining popularity of lease alternatives. When analyzing the stock performance of the companies in our sample, we discover a significantly negative relationship between real estate ownership and a firms systematic risk. Idiosyncratic risk bears no significant relationship with real estate ownership. With respect to stock returns our results show that returns are lowest among firms with the highest real estate ownership levels in each industry. After controlling for the variation in risks, the remaining return patterns differ strongly across industries, with a significantly negative relationship between stock outperformance and real estate ownership for Communications and Business Services and a positive but not significant relationship for Transportation.     

Rational Delays: The Case of Real Estate

Real estate markets, for both commercial real estate and single family homes, typically respond to a large negative demand shock with a period during which the volume of transactions and liquidity of real estate declines. Explanations for these periods have focused on overly optimistic owners, imperfections in real estate markets and/or minimum down payment requirements. These are important characteristics of real estate markets, but they do not provide a satisfying explanation for the long-term declines in the number of transactions and liquidity of real estate that frequently follow negative demand shocks. This paper presents estimates, for a specific real estate market (Los Angeles single family dwellings), of the option-like value of an owners interest in a property. Our estimates imply that when an owner has little or negative equity, the value of waiting to sell is likely to exceed the net carrying cost. Consequently, the option value of a potential sellers interest may eliminate the possibility of an otherwise mutually advantageous transaction.     

A comparison among various dimensions of illiquidity effect: A case study of Finland

We study different dimensions of the illiquidity effect on asset returns in the Finnish market. The market illiquidity is measured as unexpected rises and falls in average monthly zero returns across all stocks. We find that for the returns on the specific class of assets, a flight to the liquidity effect is the most important systematic risk among all dimensions of the illiquidity effect. In other words, higher returns for illiquid assets in good times compensate for a pronounced drop in those returns in bad times and vice versa. Furthermore, only one illiquidity-related factor has a similar pricing capacity as Fama and French's (1993) three-factor model and Carhart's (1997) four-factor model in the context of this study.     

The Performance of UK International Unit Trusts

We evaluate the performance of UK unit trusts with international equity objectives between January 1985 and December 2000 relative to domestic benchmark strategies. We use performance measures based on Jensen (1968) , Ferson and Schadt (1996) , and the Chen and Knez (1996) law of one price (LOP). We find more favourable trust performance using the Jensen and Ferson and Schadt measures relative to the LOP measure. There is evidence of inferior performance by some international trusts using the unconditional LOP measure. The charges and investment sector of the trust also has an impact on the performance of the trusts using the LOP measure.     

The price of skill: Performance evaluation by households

Skilled investors make money off uninformed investors. By acting as intermediaries, they provide a hedge to the uninformed investors themselves. I present a model in which households have imperfect information about expected returns. Non-traded income shocks lead them to rebalance, sometimes at the wrong time. Active funds hedge this risk by trading on superior information. In equilibrium, they pay off when non-traded income disappoints, earning a premium that makes them appear to underperform index funds after fees. Empirical results using aggregate fund flows support the model. A corresponding asset pricing test can account for the apparent underperformance of active funds.     

Stationarity and Cointegration in Systems with Real Estate and Financial Assets

This study examines the properties of wealth indices for investments in several asset classes (real estate, stocks, bonds, and Treasury bills), for several types of real estate (office, retail, research and development office, and warehouse), and by region (East, Midwest, South, and West). The series representing the value of investments in real estate and financial assets are not stationary; therefore, ordinary statistical procedures cannot be applied. Since many of the properties that are included in the real estate series have outside appraisals on an annual basis, especially in the fourth quarter, the real estate series may show seasonal influences. Hence, the appropriate test for cointegration is the Johansen's test, which is formulated in such a way as to allow for deterministic seasonality by the inclusion of seasonal dummy variables. The finding of cointegration implies that there is a long-run relationship between the series in the cointegrated system. When the CPI (or a proxy for inflation) is included in the three systems, the number of common factors increase to two, implying that inflation plays an important role in creating a linkage between these time series. These findings also have implications for developing portfolios comprising financial assets and real estate. The findings also have implications for developing a model to forecast real estate prices.     

Real option analysis of a technology portfolio

This article contributes to methodology of real options analysis of investments in capital intensive process industries, where relatively homogenous outputs are produced using commonly known production technologies. In addition to capacity expansion, the method can be used for analysis of mergers and acquisitions. Valuation of the real option is based on bid price; i.e., the maximum price the firm is willing to pay. To find such a price, stochastic optimization with an expected utility criterion is used to determine investments in product specific technologies as well as in publicly traded financial instruments (the competing investments). To operationalize the valuation principle, we develop a double binary tree employing Kalman filter for scenario generation. For exponential utility, valuation is carried out by dynamic programming. We extend known methods to allow interdependence of the mill cash flow and return on competing financial investments. For forest industries, we provide an illustration, where the underlying price process in our Kalman filter application is a vector error correction model.     

A Pricing Framework for Real Estate Derivatives

New methods are developed here for pricing the main real estate derivatives — futures and forward contracts, total return swaps, and options. Accounting for the incompleteness of this market, a suitable modelling framework is outlined that can produce exact formulae, assuming that the market price of risk is known. This framework can accommodate econometric properties of real estate indices such as predictability due to autocorrelations. The term structure of the market price of risk is calibrated from futures market prices on the Investment Property Databank index. The evolution of the market price of risk associated with all five futures curves during 2009 is discussed.     

Valuation in US Commercial Real Estate

We consider a log‐linearized version of a discounted rents model to price commercial real estate as an alternative to traditional hedonic models. First, we verify a key implication of the model, namely, that cap rates forecast commercial real estate returns. We do this using two different methodologies: time series regressions of 21 US metropolitan areas and mixed data sampling (MIDAS) regressions with aggregate REIT returns. Both approaches confirm that the cap rate is related to fluctuations in future returns. We also investigate the provenance of the predictability. Based on the model, we decompose fluctuations in the cap rate into three parts: (i) local state variables (demographic and local economic variables); (ii) growth in rents; and (iii) an orthogonal part. About 30% of the fluctuation in the cap rate is explained by the local state variables and the growth in rents. We use the cap rate decomposition into our predictive regression and find a positive relation between fluctuations in economic conditions and future returns. However, a larger and significant part of the cap rate predictability is due to the orthogonal part, which is unrelated to fundamentals. This implies that economic conditions, which are also used in hedonic pricing of real estate, cannot fully account for future movements in returns. We conclude that commercial real estate prices are better modelled as financial assets and that the discounted rent model might be more suitable than traditional hedonic models, at least at an aggregate level.     

Commercial Real Estate Return Performance: A Cross-Country Analysis

This paper investigates the return performance of publicly traded real estate companies. The analysis spans the 1984–1999 time period and includes return data on over 600 companies in 28 countries. The return data reveal a substantial amount of variation in mean real estate returns and standard deviations across countries. Moreover, standard Treynor ratios, which scale country excess returns by the estimated beta on the world wealth portfolio, also reveal substantial variation across countries in excess real estate returns per unit of systematic risk. However, when we estimate Jensens alphas using both single and multifactor specifications, we detect little evidence of abnormal, risk-adjusted returns at the country level. We do, however, find evidence of a strong world-wide factor in international real estate returns. Furthermore, even after controlling for the effects of world-wide systematic risk, an orthogonalized country-specific factor is highly significant. This suggests that real estate securities may provide international diversification opportunities.     

Hedge fund portfolio construction: A comparison of static and dynamic approaches

This article studies the impact of modeling time-varying covariances/correlations of hedge fund returns in terms of hedge fund portfolio construction and risk measurement. We use a variety of static and dynamic covariance/correlation prediction models and compare the optimized portfolios’ out-of-sample performance. We find that dynamic covariance/correlation models construct portfolios with lower risk and higher out-of-sample risk-adjusted realized return. The tail-risk of the constructed portfolios is also lower. Using a mean-conditional-value-at-risk framework we show that dynamic covariance/correlation models are also successful in constructing portfolios with minimum tail-risk.     

Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach

Extending previous work on asset-based style factor models, this paper proposes a model that allows for the presence of structural breaks in hedge fund return series. We consider a Bayesian approach to detecting structural breaks occurring at unknown times and identifying relevant risk factors to explain the monthly return variation. Exact and efficient Bayesian inference for the unknown number and positions of the breaks is performed by using filtering recursions similar to those of the forward–backward algorithm. Existing methods of testing for structural breaks are also used for comparison. We investigate the presence of structural breaks in several hedge fund indices; our results are consistent with market events and episodes that caused substantial volatility in hedge fund returns during the last decade.     

Payout yield,risk, and mispricing: A Bayesian analysis

We develop a simple parametric model in which hypotheses about predictability, mispricing, and the risk-return tradeoff can be evaluated simultaneously, while allowing for time variation in both risk and expected return. Most of the return predictability based on aggregate payout yield is unrelated to market risk. We consider a range of Bayesian prior beliefs about the risk-return tradeoff and the extent to which predictability is driven by mispricing. The impact of these beliefs on an investor's certainty-equivalent return when choosing between a market index and riskless T-bills is economically significant, in both ex ante and out-of-sample analyses.     

试论房价波动对商业银行资产的影响

基于次贷危机的启示,本文构建了在不同的金融发展程度下房地产价格波动对商业银行资产影响的理论模型,并在此基础上对中国的现状进行分析,指出当前对中国的影响主要集中于商业银行基于负债业务发放的贷款领域。进一步地,利用向量自回归模型(VAR)及冲击响应函数对中国房地产价格波动对商业银行贷款的影响进行了实证检验。研究表明:金融创新改变了房地产价格波动对商业银行资产的影响模式;中国房价波动对商业银行资产的影响主要集中在源于银行负债发放的贷款领域,但总体影响有限;中国应加快发展银行资产证券化业务,谨慎发展信用衍生产品。     

A New Method to Measure the Performance of Leveraged Exchange‐Traded Funds

We examine the effects of daily return compounding, financing costs, and management factors on the performance of leveraged exchange‐traded funds (LETFs) over various holding periods. We propose a new method to measure LETFs’ tracking errors that allows us to disentangle these effects. Our results show that the compounding effect generally has more influence on tracking errors than other factors, especially for long holding periods and in a “sideways” market. The explicit costs (i.e., the expense ratios) and other factors (e.g., financing costs) can materially affect the performance of LETFs, especially for those with high leverage ratios and bear funds.     

A unique “T + 1 trading rule” in China: Theory and evidence

Unique to the world, China adopts a “T + 1 trading rule”, which prevents investors from selling stocks bought on the same day. We develop a dynamic price manipulation model to study the effects of the “T + 1 trading rule”. Compared to the “T + 0 trading rule”, which allows investors to buy and sell the same stocks during the same day, we show that the “T + 1 trading rule” reduces the total trading volume and price volatility, and improves the trend chasers’ welfare when trend-chasing is strong. An empirical test using data on China’s B-share stock market supports the model’s theoretical predictions.