Asset pricing with liquidity risk

This paper solves explicitly a simple equilibrium model with liquidity risk. In our liquidity-adjusted capital asset pricing model, a security's required return depends on its expected liquidity as well as on the covariances of its own return and liquidity with the market return and liquidity. In addition, a persistent negative shock to a security's liquidity results in low contemporaneous returns and high predicted future returns. The model provides a unified framework for understanding the various channels through which liquidity risk may affect asset prices. Our empirical results shed light on the total and relative economic significance of these channels and provide evidence of flight to liquidity.     

Economic valuation of liquidity timing

This paper conducts a horse-race of different liquidity proxies using dynamic asset allocation strategies to evaluate the short-horizon predictive ability of liquidity on monthly stock returns. We assess the economic value of the out-of-sample power of empirical models based on different liquidity measures and find three key results: liquidity timing leads to tangible economic gains; a risk-averse investor will pay a high performance fee to switch from a dynamic portfolio strategy based on various liquidity measures to one that conditions on the Zeros measure (Lesmond et al., 1999); the Zeros measure outperforms other liquidity measures because of its robustness in extreme market conditions. These findings are stable over time and robust to controlling for existing market return predictors or considering risk-adjusted returns.     

Liquidity and asset pricing: Evidence from the Hong Kong stock market

This study investigates the role of liquidity in pricing stock returns in the Hong Kong stock market. Our results show that liquidity is an important factor for pricing returns in Hong Kong after taking well-documented asset pricing factors into consideration. The results are robust to adding portfolio residuals and higher moment factor in the factor models. The results are also robust to seasonality, and conditional-market tests. We also compare alternative factor models and find that the liquidity four-factor model (market excess return, size, book-to-market ratio, and liquidity) is the best model to explain stock returns in the Hong Kong stock market, while the momentum factor is not found to be priced.     

Volatilities and Momentum Returns in Real Estate Investment Trusts

We examine the relation of time-varying idiosyncratic risk and momentum returns in REITs using a GARCH-in-mean model and incorporate liquidity risk in the asset pricing model. This is important because illiquidity may be more severe for REITs due to the nature of their underlying assets. We find that momentum returns display asymmetric volatility, i.e., momentum returns are higher when volatility is higher. Additionally, we find evidence that REITs with lowest past returns (losers) have higher idiosyncratic risks than those with highest past returns (winners) and that investors require a lower risk premium for holding losers’ idiosyncratic risks. Therefore, although losers have higher levels of idiosyncratic risks, their low risk premia cause low returns, which contribute to momentum. Lastly, we find a positive relation between REITs’ momentum return and turnover.     

Stock and Bond Liquidity and its Effect on Prices and Financial Policies

An asset is liquid if it can be traded at the prevailing market price quickly and at low cost. We show that in addition to risk, liquidity affects asset prices and returns. Theories of asset pricing suggest that the expected return of an asset is increasing in its risk, because risk-averse investors require compensation for bearing more risk. Because investors are also averse to the costs of illiquidity and want to be compensated for bearing them, asset returns are increasing in illiquidity. Thus, asset prices should depend on two asset characteristics: risk and liquidity. This paper surveys research on the effects of liquidity on asset prices and returns, showing that liquidity is an important factor in capital asset pricing.     

Return and volatility co-movement in commodity futures markets: the effects of liquidity risk

Commodity markets are a widely researched topic in the field of finance. In this paper, we investigate the co-movement of return and volatility measures in different commodity futures markets and how these measures are affected by liquidity risk. First, we find that commodity returns display co-movement and that liquidity risk plays a key role in shaping asset return patterns. Moreover, we show that the volatilities of commodity returns co-move, and we demonstrate the role of liquidity risk in this joint pattern. We also find that the commodity markets we investigated share a common volatility factor that determines their joint volatility co-movement. Because liquidity risk affects both commodity returns and volatility shocks, it might be interpreted as the common causal factor driving both measures simultaneously. Therefore, we affirm the view that liquidity shocks are firmly related to two residual risks originating from both market return and market volatility. Finally, we also show that liquidity spillovers can significantly drive cross-sectional correlation dynamics.     

Are extreme returns priced in the stock market? European evidence

This paper revisits some recently found evidence in the literature on the cross-section of stock returns for a carefully constructed dataset of euro area stocks. First, we confirm recent results for US data and find evidence of a negative cross-sectional relation between extreme positive returns and average returns after controlling for characteristics such as momentum, book-to-market, size, liquidity and short term return reversal. We argue that this is the case because these stocks have lottery-like characteristics, which is attractive to certain investors. Also, these stocks tend to be very volatile so that arbitrageurs are discouraged from correcting potential mispricing. As a consequence, these stocks are often overpriced and hence face lower expected returns. Second, when we control for extreme returns, the recently found negative relationship between idiosyncratic risk and future returns is less robust. In our models, after adding maximum returns, the relationship is insignificant and sometimes even positive. We also find that idiosyncratic skewness and coskewness play an important role for asset pricing, as predicted by several theoretical models.     

The Conditional Beta and the Cross-Section of Expected Returns

We examine the cross-sectional relation between conditional betas and expected stock returns for a sample period of July 1963 to December 2004. Our portfolio-level analyses and the firm-level cross-sectional regressions indicate a positive, significant relation between conditional betas and the cross-section of expected returns. The average return difference between high- and low-beta portfolios ranges between 0.89% and 1.01% per month, depending on the time-varying specification of conditional beta. After controlling for size, book-to-market, liquidity, and momentum, the positive relation between market beta and expected returns remains economically and statistically significant.     

The Risk‐Return Relation in International Stock Markets

We investigate the risk‐return relation in international stock markets using realized variance constructed from MSCI (Morgan Stanley Capital International) daily stock price indices. In contrast with the capital asset pricing model, realized variance by itself provides negligible information about future excess stock market returns; however, we uncover a positive and significant risk‐return tradeoff in many countries after controlling for the (U.S.) consumption‐wealth ratio. U.S. realized variance is also significantly related to future international stock market returns; more importantly, it always subsumes the information content of its local counterparts. Our results indicate that stock market variance is an important determinant of the equity premium.     

The world price of liquidity risk

This paper empirically tests the liquidity-adjusted capital asset pricing model of Acharya and Pedersen (2005) on a global level. Consistent with the model, I find evidence that liquidity risks are priced independently of market risk in international financial markets. That is, a security’s required rate of return depends on the covariance of its own liquidity with aggregate local market liquidity, as well as the covariance of its own liquidity with local and global market returns. I also show that the US market is an important driving force of global liquidity risk. Furthermore, I find that the pricing of liquidity risk varies across countries according to geographic, economic, and political environments. The findings show that the systematic dimension of liquidity provides implications for international portfolio diversification.     

There is a risk-return trade-off after all

This paper studies the intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns, the mixed data sampling (or MIDAS) approach. Using MIDAS, we find a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the intertemporal capital asset pricing model based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional variance.     

Liquidity risk and expected corporate bond returns

This paper studies the pricing of liquidity risk in the cross section of corporate bonds for the period from January 1994 to March 2009. The average return on bonds with high sensitivities to aggregate liquidity exceeds that for bonds with low sensitivities by about 4% annually. The positive relation between expected corporate bond returns and liquidity beta is robust to the effects of default and term betas, liquidity level, and other bond characteristics, as well as to different model specifications, test methodologies, and a variety of liquidity measures. The results suggest that liquidity risk is an important determinant of expected corporate bond returns.     

Market Illiquidity and Conditional Equity Premium

We examine the time‐series relation between aggregate bid‐ask spreads and conditional equity premium. We document that average marketwide relative effective bid‐ask spreads forecast aggregate market returns only when controlling for average idiosyncratic variance. This control allows us to document the otherwise elusive relation between illiquidity and returns. The reason is that idiosyncratic variance correlates positively with spreads but has a negative effect on conditional equity premium, causing an omitted variable bias. Our results are robust to standard return predictors, alternative illiquidity measures, and out‐of‐sample tests. These findings are important because they provide strong support for the literature's conjecture that marketwide liquidity is an important asset pricing risk factor.     

Return spread and liquidity: Evidence from Hong Kong ADRs

Fifteen Chinese H-shares listed on the Stock Exchange of Hong Kong are cross listed as ADRs on the NYSE. We empirically determine the role of security specific liquidity associated with those ADRs and their underlying H-shares on return spreads, differences between the returns on ADRs and their corresponding H-shares after controlling for ADRs and H-shares excess market returns and their respective price inverses denoting conditional betas. We use three proxies for liquidity, trading volume, turnover, and illiquidity (Amihud, 2002) and find that only trading volume and turnover are consistent determinants of return spread for the majority of Chinese ADRs with primary listing in Hong Kong Stock Exchange (SEHK). We use a switching regression model and find that the model parameter estimates are not stationary and change, often drastically between pre and post 2000 and 2003. Further tests using Bai Perron indicate return spreads data as non-stationary with multiple regime changes during the sample period. Further the causes of non-stationarity seem to be largely security specific and not driven by broad market swings in either market.     

Inter-temporal variation in the illiquidity premium

We find evidence of a systematic link between monetary conditions and inter-temporal variation in the price of liquidity. Specifically, following an expansive monetary policy shift, funding conditions improve and market-wide liquidity increases, which is especially beneficial for illiquid securities. The improved liquidity and funding conditions reduce the returns required for holding illiquid securities. Consequently, illiquid stocks experience relatively large price increases when monetary conditions become expansive, and thus, the measured return spread between illiquid and liquid stocks expands substantially. Overall, our evidence supports the claim that the price of asset liquidity is dependent on monetary conditions.     

Liquidity costs,idiosyncratic volatility and expected stock returns

This paper considers liquidity as an explanation for the positive association between expected idiosyncratic volatility (IV) and expected stock returns. Liquidity costs may affect the stock returns, through bid-ask bounce and other microstructure-induced noise, which will affect the estimation of IV. We use a novel method (developed by Weaver, 1991) to eliminate microstructure influences from stock closing price-based returns and then estimate IV. We show that there is a premium for IV in value-weighted portfolios, but this premium is less strong after correcting returns for microstructure bias. We further show that this premium is driven by liquidity in the prior month after correcting returns for microstructure noise. The pricing results from equally-weighted portfolios indicate that IV does not predict returns either before or after controlling for liquidity costs. These findings are robust after controlling for common risk factors as well as analysing double-sorted portfolios based on IV and liquidity.     

Feedback trading and intermittent market turbulence

This paper studies the potential for complex asset return dynamics in a high-frequency, non-fundamental feedback trading model. Price adjustment is driven by the time-varying price impact of net orderflow. In tranquil times feedback trading has no impact on the price level. Given feedback trading intensities, as asset liquidity declines the market progressively becomes stressed and turbulent. Returns and absolute returns persistence are found to display power-law features, and episodes of turbulence are intermittent.     

Dynamic asset–liability management in a Markov market with stochastic cash flows

This paper provides a general model to investigate an asset–liability management (ALM) problem in a Markov regime-switching market in a multi-period mean–variance (M–V) framework. Emphasis is placed on the stochastic cash flows in both wealth and liability dynamic processes, and the optimal investment and liquidity management strategies in achieving the M–V bi-objective of terminal surplus are evaluated. In this model, not only the asset returns and liability returns, but also the cash flows depend on the stochastic market states, which are assumed to follow a discrete-time Markov chain. Adopting the dynamic programming approach, the matrix theory and the Lagrange dual principle, we obtain closed-form expressions for the efficient investment strategy. Our proposed model is examined through empirical studies of a defined contribution pension fund. In-sample results show that, given the same risk level, an ALM investor (a) starting in a bear market can expect a higher return compared to beginning in a bull market and (b) has a lower expected return when there are major cash flow problems. The effects of the investment horizon and state-switching probability on the efficient frontier are also discussed. Out-of-sample analyses show the dynamic optimal liquidity management process. An ALM investor using our model can achieve his or her surplus objective in advance and with a minimum variance close to zero.     

考虑流动性的三阶矩资本资产定价的理论模型与实证研究

本文把流动性风险、偏态风险引进传统CAPM模型中,推导出基于流动性的三阶矩资本资产定价的理论模型。本文的模型表明,证券(组合)的收益依赖于它的期望流动性成本、其流动性成本和市场流动性成本的协方差以及其收益和市场收益的协方差与协偏态。本文采用我国A股市场的股票收益数据对模型进行了实证检验.检验结果表明,我国A股市场的证券(组合)的风险溢价在大盘升降区间体现了不同的特征,无论是在全样本区间还是两个子样本区间,基于流动性的三阶矩资本资产定价模型都能更好的拟合资产收益,说明了流动性和偏态因素在我国A股市场的资产定价中有重要影响。     

A Characterization of the Daily and Intraday Behavior of Returns on Options

The daily and intraday behavior of returns on Chicago Board Options Exchange options is examined. Option returns contain systematic patterns even after adjusting for patterns in the means and variances of the underlying assets. This is consistent with the hypothesis that informed trading in options can make the order flow in the options market informative about the value of the underlying asset, making options nonredundant. The intraday patterns in adjusted option return variances are further consistent with a model of strategic trading by informed and discretionary liquidity traders.