Liquidity-adjusted Capital Asset Pricing Model Research Articles

Liquidity Risk, Return Performance, and Tracking Error: Synthetic vs. Physical ETFs

As the global ETF market continues to grow, the significance of understanding replication strategies has become increasingly apparent. This study examines the compensation of synthetic exchange-traded funds (ETFs) due to their greater risk exposure than physical ETFs, using ETFs listed on developed European markets from 2009 to 2020. This is the first study to investigate liquidity risk related to the replication strategy of ETFs. We investigate the difference in systematic liquidity risk between synthetic and physical ETFs using a liquidity-adjusted capital asset pricing model (LCAPM). Our findings suggest that synthetic ETFs have higher liquidity risks than physical ETFs and offer additional returns within the low-liquidity group. We also analyze the tracking performance of ETFs based on their type and find that the synthetic replication method reduces tracking errors within the high-liquidity group. Specifically, the enhanced tracking performance of synthetic ETFs becomes more prominent during periods of liquidity shocks than in regular periods. These findings shed light on key considerations for resolving puzzles in tracking errors.

Are conditional illiquidity risks priced in China? A cross-sectional test

In this study, we theoretically derive conditional illiquidity risks from the conditional liquidity-adjusted capital asset pricing model (CLCAPM) that we propose by incorporating funding illiquidity into the LCAPM, and we examine whether they are priced empirically in China's A-share market. We provide new evidence of the positive premiums of conditional illiquidity risks even after controlling for mispricing signals and sentiment. The finding suggests that conditional illiquidity risk could be an alternative channel to explain the cross-section of stock returns. Moreover, investors could obtain higher premiums as compensation for their tolerance of more highly conditional illiquidity risks during high market volatility (low market returns) periods.

Role of Liquidity Risk in Asset Pricing: Evidence from Sri Lanka

Securities liquidity varies over time, which leads to equity return volatility. It implies that the liquidity in the capital markets is a significant source of risk. Therefore, liquidity risk in securities is difficult to diversify and contributes to the systemic market risk. This study aims to analyze the relationship between securities returns and liquidity risk while taking into account the time-varying characteristics of illiquidity on the Colombo Stock Exchange from 2015-2019 and taking into account the effect of liquidity level, using the Generalized Method of Movements (GMM) framework model to assess the persistence of illiquidity securities contributions of the updated version of the Amihud illiquidity (Amihud, 1986) proxy to represent across time market illiquidity and to research the time-series relationship between liquidity and returns. The pricing of liquidity risk and its implications for expected returns are empirically tested using the conditional liquidity adjusted capital asset pricing model (LCAPM), where stock returns are cross-sectionally dependent on market risk and three additional betas (β1, β2 , β3 ) that capture different aspects of illiquidity and its risk. The findings reveal some support for the conditional capital asset pricing model (CAPM), but results are not robust to alternative specifications and estimation techniques. The total effect of liquidity risk is 0.11%, and illiquidity is 2.5% per year. Illiquidity premium depends on the expected transaction cost at the end of the holding period for investors' 2.5%. This makes the overall illiquidity premium of 2.61%. These estimates and the overall importance of liquidity level and liquidity risk depend on the model implied restrictions of a constant market risk premium and a fixed transaction cost. However, LCAPM has constructed conditionally; it can relax these model-implied constraints and estimate different liquidity risk premiums while also allowing transaction costs to be a free parameter. The overall liquidity risk characterized by liquidity betas with a single market risk premium is relatively small and barely significant in the restricted model. Using this unrestricted model, find that the overall illiquidity premium corresponds to 2.61%. The empirical results shed light on these channels' toal and relative economic significance and provide evidence of flight to liquidity.
 
 Keywords: Capital Asset Pricing Model, Liquidity Risk, Liquidity beta, Generalize Method of Movement, Sri Lanka

PENGUKURAN KINERJA PORTOFOLIO OPTIMAL SAHAM LQ45 MENGGUNAKAN METODE CAPITAL ASSET PRICING MODEL (CAPM) DAN LIQUIDITY ADJUSTED CAPITAL ASSET PRICING MODEL (LCAPM)

Investment is planting some funds to get profit and the stock is one of the type of investment in fincancial that the most interested for investors. To avoid the risk of investing, investors try to diversify their invesments by using portfolio. Stock portfolio is investment which comprised of various stocks from different companies, with the expect when the price of one stock decreases, while the other increases, then the investments do not suffer losses. Models that can be used to make a portfolio, one of them is Capital Asset Pricing Model (CAPM) and Liquidity Adjusted Capital Asset Pricing Model (LCAPM). CAPM is a model that connects expected return with the risk of an asset under market equilibrium condition. LCAPM is a method of new development of the CAPM model which is influenced by liquidity risk. To analyze whether the formed portfolio have a good performance or not, so portfolio perfomance assessment will be done by using The Sharpe Index. This research uses data from closing prices, transaction volume and volume total of LQ45 Index stock on period March 2016-February 2020 and then data of JCI and interest rate of central bank of the Republic of Indonesia. Based on The Sharpe Index, optimal portfolio is LCAPM model portfolio with 3 stock composition and the proportion investment are 32,39% for LPPF, 49,86% for SRIL and 17,75% for TLKM. Keywords: LQ45 Index, Portfolio, Capital Asset Pricing Model (CAPM), Liquidity Adjusted Capital Asset Pricing Model (LCAPM), The Sharpe Index.

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.

Illiquidity contagion and pricing of commonality risk: Evidence from a dynamic conditional correlation model

This paper investigates the pricing of the commonality in liquidity risk in the U.S. stock market by using a more comprehensive measure of market illiquidity cost that can reflect the liquidity condition of broader asset markets, and by forming portfolios in a way that is consistent with the definition of the commonality risk. Estimating a conditional version of the Liquidity-Adjusted Capital Asset Pricing Model by the Dynamic Conditional Correlation approach, we find a commonality risk premium that is higher than those derived from alternative measures. The premium is time varying, with values being higher during periods of market turmoil.

Do the LCAPM Predictions Hold? Replication and Extension Evidence

The Liquidity-adjusted Capital Asset Pricing Model (LCAPM) includes two specific testable predictions: (1) the coefficient on expected liquidity cost equals average turnover and (2) the coefficient on market beta equals the coefficient on net liquidity risk beta. By contrast, generalized liquidity-adjusted asset pricing models allow liquidity characteristics and/or liquidity risk factors without such parameter restrictions. We empirically test these alternative theories. In doing so, we expand the range of evidence in multiple ways. First, we extend forward and backwards in time to cover 80 years. Second, we analyze NASDAQ-listed stocks as well as NYSE/AMEX-listed stocks. Third, we analyze four alternative liquidity measures: (1) the Corwin and Schultz proxy, (2) closing percent quoted spread, (3) the Amihud proxy, and (4) zeros. Fourth, we analyze the impact of adding Fama and French/Carhart risk factors to the model. Our main finding is that both of these specific predictions of LCAPM are robustly rejected. This outcome supports generalized liquidity-adjusted asset pricing. Finally, we ask can the original LCAPM results be replicated? We qualitatively replicate most tables, but obtain mixed results for two tables. We make publicly available our SAS code and the resulting data files for both our new empirical tests and our replication.

Pricing Liquidity Risk on the Tokyo Stock Exchange: Empirical Analysis Using Multiple Liquidity Measures

This study investigates the effectiveness of the liquidity-adjusted capital asset pricing model proposed by Acharya and Pedersen (2005). Using Japanese data, we compute multiple liquidity measures and a principal-component-based liquidity proxy to examine whether liquidity risk is priced on the Tokyo Stock Exchange. We find that the size and value effect should be considered together when studying the pricing of liquidity risk. Through our analysis, we suggest using the principal-component-based liquidity proxy or using multiple methods to estimate Japanese stock liquidity. To some extent, liquidity risk is priced on the Tokyo Stock Exchange.

PERBANDINGAN MODEL CAPITAL ASSET PRICING MODEL (CAPM) DAN LIQUIDITY ADJUSTED CAPITAL ASSET PRICING MODEL (LCAPM) DALAM PEMBENTUKAN PORTOFOLIO OPTIMAL SAHAM SYARIAH

In stock investments, every investor wants to get a high level of return and low risk. The stock price is very volatile and unpredictable, this makes investors have to find solutions in order to get a benefit from this investment. One way is to form a portfolio. A portfolio is a collection of several shares. There are several models for calculating stock portfolios such as CAPM (Capital Asset Pricing Model) and LCAPM (Liquidity Adjusted Capital Asset Pricing Model). The CAPM is a model that describes the relationship between the expected return and risk of investing in a security. The LCAPM is an extension of CAPM by taking into account the liquidity of assets. Data from Jakarta Islamic Index is used to verify the two models. In this case, the empirical results show that the performance of CAPM is better than the LCAPM.

Pricing of time-varying illiquidity within the Eurozone: Evidence using a Markov switching liquidity-adjusted capital asset pricing model

This paper investigates time-varying characteristics of illiquidity and the pricing of its risk using a liquidity-adjusted capital asset pricing model (L-CAPM). Collecting data from a pool of Eurozone countries between 1990 and 2018, we employ Markov switching models to assess the degree of persistence of illiquidity shocks. Contrary to prior research, which largely makes use of autoregressive (AR) processes, we provide strong evidence that illiquidity is time-varying and the persistence of shocks determines two distinct regimes characterised by high and low illiquidity. We assess pricing of illiquidity risk by developing and empirically testing a conditional L-CAPM model, where different regimes constitute priced risk factors for the cross-section of stock returns. We extend previous unconditional versions of L-CAPM models and we show that the various channels through which illiquidity affects asset returns and price of risks are time-varying. We find strong support for our conditional L-CAPM and our results are robust to alternative specifications and estimation techniques. These findings have important implications for portfolio management practices and are relevant to portfolio and risk managers and regulatory institutions.

Empirical tests of asset pricing models with individual assets: Resolving the errors-in-variables bias in risk premium estimation

To attenuate an inherent errors-in-variables bias, portfolios are widely employed to test asset pricing models; but portfolios might mask relevant risk- or return-related features of individual stocks. We propose an instrumental variables approach that allows the use of individual stocks as test assets, yet delivers consistent estimates of ex post risk premiums. This estimator also yields well-specified tests in small samples. The market risk premium under the capital asset pricing model (CAPM) and the liquidity-adjusted CAPM, premiums on risk factors under the Fama–French three- and five-factor models, and the Hou et al. (2015) four-factor model are all insignificant after controlling for asset characteristics.

Pricing of time-varying liquidity risk in Finnish stock market: new evidence

ABSTRACTUsing two recently developed illiquidity measures, we estimate a conditional version of liquidity-adjusted capital asset pricing model (LCAPM), which allows for a time-varying decomposition of the total illiquidity premium into a level component and three risk components. The total estimated annualized illiquidity premium for the Finnish equities during 1997–2015 is 1.13–1.90% depending on the illiquidity measure. Of the three systematic liquidity risk components, risk arising from hedging of wealth shocks is the most important followed by commonality in liquidity risk, whereas flight to liquidity risk is not significantly priced in the Finnish stock market. Our results show that the liquidity risk is time varying, therefore the models estimating the risk-return relationship should address the issue of conditionality.

Empirical tests on the asset pricing model with liquidity risk: An unobserved components approach

The aim of this paper is to test empirically the conditional liquidity-adjusted capital asset pricing model (L-CAPM) developed by Acharya and Pedersen (2005). Accordingly, we propose to estimate the L-CAPM using unobserved components methodology, which allows us to take into account the main stylized facts characterizing liquidity. Based on a sample of firms listed on the NASDAQ, our empirical analysis reveals several findings. Firstly, we show that liquidity is time-varying and exhibits strong seasonality. Secondly, we highlight the impact of the liquidity level premium on asset prices. Thirdly, we show that the most important liquidity risk is related to the covariance between portfolio illiquidity and market returns. Fourthly, we observe a negative relationship between portfolio returns and market illiquidity. Fifthly, we find that liquidity risk and illiquidity level are not always positively correlated.

The Time Variation of Liquidity Risk on US Stock Markets

Abstract The influence of liquidity costs and liquidity risk on asset returns has been proven by several empirical studies. This paper analyzes the conditional version of the liquidity-adjusted capital asset pricing model and shows that betas significantly vary over different economic regimes and that liquid portfolios provide diversification benefits compared with illiquid portfolios. The results support the effects of a flight-to-liquidity. The time variation of liquidity betas induces additional risk for investors, which has important implications for investment decisions and asset allocation. Die Zeitschwankung des Liquiditatsrisikos in US-Aktienmarkten Zusammenfassung Der Einfluss von Liquiditatskosten und Liquiditatsrisiko auf die Renditen verschiedener Assets wurde bereits durch einige Studien belegt. Dieses Paper untersucht das conditional liquidity-adjusted capital asset pricing model und zeigt, dass die Liquidity-Betas signifikant uber verschiedene Marktphasen hinweg schwanken und dass liquide P...

A Corporate Social Responsibility of Engineering the Liquidity-Adjusted Capital Asset Pricing Modelling Sub-Sahara Africa: Evidence from Ghana

This paper estimates a conditional version of liquidity–adjusted capital asset pricing model in an emerging market in line with the corporate social responsibility (CSR) of the Ghana Stocks Exchange. We find out that for several years, Ghana stock market has been excluded from the global financial watch and from empirical verification model for lack of transparency in the performance of Exchange. Our evaluation concludes that illiquidity risk can be measured in the local market and exhibit a strong trend of mix reactions from liquidity premia.While the effect of the recent financial crisis do not show much difference between the different market conditions, the effect is more stronger in the down market than the up market. Finally, we explore the size effect on the market and conclude that the net beta as well as the systematic liquidity risk is pronounced in the smaller market though insignificant.

Testing the LCAPM vs. Generalized Liquidity-Adjusted Asset Pricing: New Evidence and New Perspectives

The Liquidity-adjusted Capital Asset Pricing Model (LCAPM) includes two specific testable predictions: (1) the coefficient on expected liquidity cost equals average turnover and (2) the coefficient on market beta equals the coefficient on net liquidity risk beta. By contrast, generalized liquidity-adjusted asset pricing models allow liquidity characteristics and/or liquidity risk factors without such parameter restrictions. We empirically test these alternative theories. In doing so, we expand the range of evidence in multiple ways. First, we extend forward and backwards in time to cover 80 years. Second, we analyze NASDAQ-listed stocks as well as NYSE/AMEX-listed stocks. Third, we analyze four alternative liquidity measures: (1) the Corwin and Schultz proxy, (2) closing percent quoted spread, (3) the Amihud proxy, and (4) zeros. Fourth, we analyze the impact of adding Fama and French/Carhart risk factors to the model. Our main finding is that both of these specific predictions of LCAPM are robustly rejected. This outcome supports generalized liquidity-adjusted asset pricing. Finally, we ask can the original LCAPM results be replicated? We qualitatively replicate most tables, but obtain mixed results for two tables. We make publicly available our SAS code and the resulting data files for both our new empirical tests and our replication.

Empirical tests on the liquidity-adjusted capital asset pricing model

This study examines the effects of systematic liquidity risk on stock returns in the Australian market. We find that liquidity risk, in the form of (i) the co-movement between individual stock liquidity and market liquidity, (ii) the co-movement between stock returns and market liquidity, and (iii) the co-movement between stock liquidity and market returns, is priced individually and jointly in Australian equities. The results are robust to the use of alternative liquidity proxies and after controlling for other factors known to affect stock returns. The analysis across different market conditions shows that the net liquidity risk is approximately eight times higher in bearish markets than in bullish markets. Our overall results support the importance of liquidity risk in the generation of stock returns, particularly during market downturns.

Empirical Test on the Liquidity-Adjusted Capital Asset Pricing Model

In this study, we examine the effects of systematic liquidity risk on stock returns in the Australian market. We find that liquidity risk, in the form of (i) the co-movement between individual stock liquidity and market liquidity, (ii) the co-movement between stock returns and market liquidity, and (iii) the co-movement between stock liquidity and market returns, is priced individually and jointly in Australian equities. The results are robust to the use of alternative liquidity proxies and after controlling for other factors that are known to affect stock returns. The analysis across different market conditions shows that the net liquidity risk is approximately eight times higher in bearish markets than in bullish markets. Our overall results support the importance of liquidity risk in the generation of stock returns, particularly during market downturns.

Pricing of Liquidity Risks: Evidence from Multiple Liquidity Measures

We investigate the pricing implication of liquidity risks in the liquidity-adjusted capital asset pricing model of Acharya and Pedersen (2005), using multiple liquidity measures and their principal component. While we find that the empirical results are sensitive to the liquidity measure used in the test, we find strong evidence of pricing of liquidity risks when we estimate liquidity risks based on the first principal component across eight measures of liquidity, both in the cross-sectional and factor-model regressions. Our finding implies that the systematic component measured by each liquidity proxy is correlated across measures and the shocks to the systematic and common component of liquidity are an undiversifiable source of risk.

Pricing of liquidity risks: Evidence from multiple liquidity measures

We investigate the pricing implication of liquidity risks in the liquidity-adjusted capital asset pricing model of Acharya and Pedersen (2005), using multiple liquidity measures and their principal component. While we find that the empirical results are sensitive to the liquidity measure used in the test, we find strong evidence of pricing of liquidity risks when we estimate liquidity risks based on the first principal component across eight measures of liquidity, both in the cross-sectional and factor-model regressions. Our finding implies that the systematic component measured by each liquidity proxy is correlated across measures and the shocks to the systematic and common component of liquidity are an undiversifiable source of risk.