Illiquidity Risk Premium Research Articles

PAY GETİRİLERİNDE LİKİDİTE AZLIK PRİMİ ETKİSİ: BORSA İSTANBUL UYGULAMASI

Bu çalışmanın amacı, likidite azlık priminin mevcudiyetinin Borsa İstanbul (BIST) Pay Piyasası’nda araştırılmasıdır. Bu amaç kapsamında, likidite azlık primi, Ocak 2002-Eylül 2018 dönem aralığında, sıfır getirili ölçüt ve likidite azlık ölçütü (ILLIQ) aracılığıyla, Acharya ve Pedersen (2005) Likidite Ayarlı Finansal Varlık Fiyatlandırma Modeli (LFVFM) üzerinden regresyon analizi ile test edilmiştir. Likidite azlık göstergelerine göre oluşturulan 20 adet portföyden elde edilen analiz sonuçları BIST Pay Piyasası’nda ilgili dönem aralığında likidite azlık priminin beklenen getiri oranı üzerinde anlamlı bir etkiye sahip olmadığını ortaya koymuştur. Diğer bir ifadeyle, elde edilen bulgular, yatırımcı kararları ile likidite azlık primi arasında bir ilişki olmadığı yönündedir.

A Dealer Illiquidity Risk Premium in the Money Markets

Safe and liquid assets like Treasuries are not a cash substitute. However, dealer banks may transform Treasuries into a money-like financial asset, i.e., a repurchase agreement collateralized with Treasuries. The ratio of Treasuries useable as repo collateral by dealer banks over demand for money-like financial assets by institutional investors is a statistically and economically significant determinant of spreads in both money and bond markets beyond conventional determinants. The ratio captures a risk premium for the illiquidity of the dealer system that is due to the time-varying market liquidity of repo collateral.

The impacts of asymmetric information and short sales on the illiquidity risk premium in the stock option market

Abstract The illiquidity risk premium hypothesis implies the existence of a positive relation between illiquidity in the option market and option returns. Based on numerous studies within the extant literature examining the roles of informed traders in the option markets, we explore how information asymmetry and short sales affect the illiquidity risk premium hypothesis. We find that the illiquidity risk premium is higher for both call and put options of those firms with higher information asymmetry, which is particularly driven by small firms. We also find that it is higher for put (call) options of those stocks with lower (higher) short-sale supply (demand).

Funding Liquidity CAPM: International Evidence

We test the role of funding-constrained investors across developed financial markets. We compile direct measures of the severity of funding frictions, or illiquidity, from deviations of government bond yields from a fitted yield curve. Using these illiquidity measures, we first show that higher illiquidity is associated with a flatter security market line across markets. Exploiting the cross-section, we find that cross-country variation in illiquidity is associated with cross-country variation in alpha, in line with our theoretical predictions. Finally, we estimate a significant negative illiquidity risk premium that reveals a strong willingness of investors to hedge against the deterioration of funding conditions.

Pricing, Dynamics, and Determinants of Illiquidity Risks: International Evidence

Using the liquidity-adjusted CAPM (LCAPM) model, we estimate three time-varying illiquidity risks based on the DCC-GARCH(1,1) for 49,351 common stocks of which 20,678 trade in 60 emerging markets and the remaining 28,673 in 23 developed markets. The reported evidence from the cross-sectional regression analysis shows that the conditional LCAPM illiquidity risks are more strongly priced in emerging than developed markets and are largely determined by local factors for both markets. Our estimations show that the total annualized illiquidity risk premium in emerging markets is more than twice that in developed markets, 1.91% vs. 0.73%, respectively. Moreover, studying the dynamics of the three illiquidity risks over time, we find no time trend across all countries. We further document an increase for all three illiquidity risks during the financial crisis that is not completely reversed a year after. The illiquidity risk defined as the covariance of asset illiquidity with market returns increased more during the crisis (and decreased less a year after) in developed markets than in emerging markets. Finally, we explore the ability of business cycles, liquidity funding constraints, and the monetary policy conditions to explain the time-varying illiquidity premia. The reported results show while business cycle determinants generally have the same explanatory ability in both sets of markets, the effect of monetary policy conditions are more strongly supported in developed markets, and the liquidity funding constraints more in emerging markets.

Empirical Test of Fama French Three Factor Model and Illiquidity Premium in Indonesia

This study explores Fama French Three Factor Model and illiquidity premium in Indonesia. The objective of this research is to find evidence about the effect from market beta, size, value, and liquidity to the stock excess return in Indonesia. We use Amihud (2002) illiquidity as a proxy for illiquidity. This study uses Ordinary Least Squares (OLS) regression with monthly time series data over 10 years, from 2003 to 2013, and use dummy variable to make difference in crisis period and non-crisis period. We devided the portfolios into 12 portfolios, sorted by size-illiquidity and book-to-market (BM)-illiquidity. The results showed that market beta (excess market return) consistently positive and significant in each portfolio, when sorted by both criterion. Size factor (SMB) has explanatory power to explain illiquidity factor (ILLIQ) and vice versa. We also find that stocks with small size outperformed stocks with big size. HML coefficient increases when the book-to-market increased, whereas the SMB coefficient increases when liquidity decreases. This could be an indication that the small stock is more difficult to be traded (illiquid). The values of adjusted R2 are in the range of 0.58-0.88, it means that the value is quite big and the model can be used (applicable) on the stock market in Indonesia. Overall, we conclude that there are market risk premium, size premium, value premium and illiquidity risk premium which can explain the excess return in Indonesia. Subprime mortgage crisis statistically has no effect in all portfolios. The results supported Fama and French (1992, 1993) and the results of Lam and Tam (2011).

Stock returns with consumption and illiquidity risks

This paper derives closed-form expressions for consumption-based stochastic discount factors adjusted by market-wide illiquidity shocks, considering both contemporaneous and ultimate consumption risk. We find that market-wide illiquidity risk is important for pricing risky assets under alternative preference specifications, although it is especially relevant when we allow for ultimate consumption risk. We also find a large and highly significant illiquidity risk premium for the first quarter of the year suggesting a time-varying behavior of the market-wide illiquidity premium.

Liquidity risk premium and asset pricing in US water transportation

The water transportation of freight has been one of the most important sectors in facilitating international trade and contributing to the growth of the world economy. Bearing in mind the importance of the relation between asset returns and liquidity in water transportation, this paper examines this relation within the context of US traded international water freight transportation firms. Using a Fama–MacBeth analysis, it is shown that the illiquidity risk premium is priced in the water transportation sector beyond the Fama and French and market-wide illiquidity risk factors, indicating higher average returns for stocks with greater illiquidity measures. It is also shown that the market-wide illiquidity factor and the Fama–French SMB and HML risk factors are significant in explaining stock returns. In contrast, market risk is found not to be priced in the water transportation sector. The results are also robust to asset pricing tests over two alternative sub-periods.

The Components of the Illiquidity Premium: An Empirical Analysis of U.S. Stocks 1927-2010

This paper estimates a conditional version of the liquidity adjusted CAPM by Acharya and Pedersen (2005) using NYSE and AMEX data from 1927 to 2010 to study the illiquidity premium and its variation over time. The components of the illiquidity premium is in this model derived as the level of expected illiquidity together with three types of illiquidity risks. We measure illiquidity of individual stocks by the efficient spread proxy developed in Holden (2009) and employ illiquidity sorted portfolios as test assets. The average annual illiquidity premium is estimated to 1.55%, the respective contributions from illiquidity level being 1.15% and from the three different illiquidity risks 0.40%. Results also indicate that commonality risk is the least important component in the illiquidity risk premium, while a component related to the hedging of wealth shocks is the most important. The illiquidity premium varies substantially over time, with peaks in downturns and crises, but with no general tendency to decrease over time.

Hedge Funds Performance Ratios Adjusted to Market Liquidity Risk

Market liquidity is complex to measure empirically. This explains why there is no consensus about performance ratios adjusted to its risk. We summarize market liquidity by two major characteristics: a costly one because of the loss of illiquidity premium; and a profitable one when investors can withdraw when they want.Then, in this paper, three new performance indicators are proposed to integrate, to a certain extent, market liquidity risk, especially for hedge funds investment: Liquidity-loss ratio will capture the cost characteristic whereas Liquidity-Sharpe ratio and Liquidity-profit ratio the profitable one. These new ratios try to be simple enough and also precise to help investors to choose between hedge funds strategies according to their liquidity profile: do they want to capture illiquidity risk premium? Do they want to be free to withdraw?

Forward-Looking Market Risk Premium

A method for computing forward-looking market risk premium is developed in this paper. We first derive a theoretical expression that links forward-looking risk premium to investors' risk aversion and forward-looking volatility, skewness, and kurtosis of cumulative return. In addition, investors' risk aversion is theoretically linked to volatility spread, defined as the gap between the risk-neutral volatility deduced from option data and the physical return volatility exhibited by return data. The volatility spread formula serves as the basis for using the generalized method of moments to estimate investors' risk aversion. We adopt the generalized autoregressive conditional heteroskedasticity model for the physical return process and estimate the model using the S&P 500 daily index returns and then deduce the forward-looking variance, skewness, and kurtosis of the corresponding cumulative return. The forward-looking risk premiums are estimated monthly over the sample period of 2001–2010, and all are found to be positive. Furthermore, two asset pricing tests are conducted. First, change in forward-looking risk premiums is negatively related to the S&P 500 holding period return, reflecting that an increase in discount rate reduces current stock prices. Second, market illiquidity positively affects forward-looking risk premium, indicating that forward-looking risk premium contains an illiquidity risk premium component. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1758 This paper was accepted by Wei Jiang, finance.

Forward-Looking Market Risk Premium

A method for computing forward-looking market risk premium is developed in this paper. We first derive a theoretical expression that links forward-looking risk premium to investors' risk aversion and cumulative return's forward-looking volatility, skewness and kurtosis. In addition, investor's risk aversion is theoretically linked to volatility spread defined as the gap between the risk-neutral volatility deduced from option data and the physical return volatility exhibited by return data. The volatility spread formula serves as the basis for using the GMM method to estimate investor's risk aversion. We adopt the GARCH model for the physical return process, and estimate the model using the S&P500 daily index returns and then deduce the corresponding cumulative return's forward-looking variance, skewness and kurtosis. The forward-looking risk premiums are estimated monthly over the sample period of 2001-2010 and found to be all positive. The forward-looking risk premium was higher during volatile market periods (such as September 2001 and October 2008) and lower when the market was calm. Furthermore, two asset pricing tests are conducted. First, change in forward-looking risk premiums is negatively related to the S&P500 holding period return, reflecting that an increase in discount rate reduces current stock price. Second, market illiquidity positively affects forward-looking risk premium, indicating that forward-looking risk premium contains an illiquidity risk premium component.

Expected Returns and Markov-Switching Illiquidity

Recent theoretical models imply that liquidity is fragile: financial markets are liquid in some equilibria and illiquid in others. This paper employs an intuitively appealing Markov-switching regime model to investigate the episodic nature of stock market illiquidity and the intertemporal relation between illiquidity risk and expected stock returns. We introduce a two-state Markov-switching regime model for stock market illiquidity, returns and volatility. We find evidence of a significant illiquidity risk premium; the expected stock return is positively related to the conditional probability of an illiquid regime. By combining the Markov-switching model with a log-linear model for stock returns, we derive a tractable expression for the illiquidity feedback effect. Modeling the illiquidity feedback effect is critical to understanding the relation between realized stock returns, expected returns and Markov-switching illiquidity. We develop a flexible Bayesian Markov chain Monte Carlo (MCMC) approach for estimating and comparing models.