اشتراک در نقدینگی : انتقال شوک نقدینگی به همه سرمایه گذاران و اوراق بهادار
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|13475||2003||22 صفحه PDF||سفارش دهید||10513 کلمه|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Financial Intermediation, Volume 12, Issue 3, July 2003, Pages 233–254
What are the causes and consequences of commonality in liquidity? We examine this issue using a model of liquidity trading in which liquidity shocks are decomposed into common (systematic) and idiosyncratic components. We show that common liquidity shocks do not give rise to commonality in trading volume. Indeed, trading volume is independent of systematic liquidity risk, and this risk is always priced irrespective of market liquidity. In contrast, idiosyncratic liquidity shocks create liquidity demand and volume, and investors can diversify their risk by trading. Hence, pricing of the risk of idiosyncratic liquidity shocks depends on market liquidity, with idiosyncratic liquidity risk being fully priced only in perfectly illiquid markets. While trading volume increases with the variance of idiosyncratic liquidity shocks, price volatility increases with the variance of both idiosyncratic and systematic liquidity shocks. Surprisingly, our results are largely independent of the number of different securities traded in the market. When asset returns are uncorrelated, there is no transmission of liquidity across assets even when investors experience common liquidity shocks, suggesting that such liquidity shocks may not be the source of commonality in liquidity across assets detected in the literature. However, under limited conditions, more liquid securities can act as substitutes for less liquid securities. Overall, our findings suggest that common factors in liquidity may be the outcome of covariation in investor heterogeneity (e.g., as measured by co-movements in the volatility of idiosyncratic liquidity shocks) rather than of common liquidity shocks. Moreover, we find that different liquidity proxies measure different things, which has implications for future empirical analysis.
With the proliferation of financial securities and the markets in which they trade, considerable attention has been focused on the role of liquidity in financial markets. While the traditional focus of research in this area has been on the liquidity of individual securities, recent studies have detected common factors in prices, trading volume, and transactions cost measures such as bid–ask spreads.1 These findings highlight the importance of understanding the mechanics by which liquidity demand and supply is transmitted across investors and securities. Chordia et al. (2000) note that drivers of common factors in liquidity may be related to market crashes and other market incidents, pointing to recent incidents such as the summer 1998 collapse of the global bond market and the October 1987 stock market collapse, which did not seem to be accompanied by any significant news. They also identify as an important area of future research the question of whether and to what extent common factors in liquidity affect asset prices. What are the fundamental determinants of commonality in liquidity, and how are they reflected in standard liquidity measures and asset valuations? These questions are the focus of this paper. We develop a model that follows the basic intuition provided by Karpoff (1986), who characterizes non-informational trading as the outcome of differences in personal valuation of assets by investors, due to their differential liquidity needs. In our model, liquidity shocks that cause investors to revise their personal valuations can have both systematic (i.e., common across all investors) and idiosyncratic components. This formulation permits us to examine the transmission of liquidity shocks across assets and across the investor base of individual assets. Indeed, our analysis highlights the importance of variations in liquidity demand across investors as a crucial determinant of the liquidity of assets they hold. Common factors in liquidity seem to imply that liquidity shocks apply systematically across investors, and are transmitted across investors and/or securities causing market-wide effects. We show that systematic and idiosyncratic liquidity shocks have significantly different effects on asset prices, trading volume and volatility. The demand for liquidity arises from investor heterogeneity caused by idiosyncratic liquidity shocks, and is manifested in trading volume. Contingent upon the state of liquidity in the market, trading volume increases with the intensity of idiosyncratic liquidity shocks (measured by their variance). In contrast, systematic liquidity shocks do not give rise to a demand for liquidity or affect trading volume, although they have a significant impact on price volatility. The risk of systematic liquidity shocks is always priced and is independent of the state of liquidity in the secondary market, since investors are unable to diversify this risk by trading.2 The price volatility associated with systematic liquidity shocks is also not contingent upon the state of liquidity in the market. Indeed, as in Milgrom and Stokey (1982), systematic liquidity shocks will not induce trading even if the market is liquid. In contrast, the state of liquidity in the market is very important in the case of idiosyncratic liquidity shocks. Since investors are differentially impacted by the shocks, they can be transmitted across the investor base by trading, to the benefit of all investors. Hence, investors will seek to exploit the benefits of trading if the market is liquid, and the state of liquidity in the market will determine the extent to which the risk of idiosyncratic liquidity shocks is incorporated in the price. These results suggest the importance of carefully differentiating between systematic and idiosyncratic liquidity drivers when using standard liquidity measures as proxies for liquidity. They also raise questions about the sources of commonality in liquidity detected in the literature. It is especially interesting to observe that systematic liquidity shocks do not cause co-movement in volume. Idiosyncratic liquidity shocks are the principal determinant of volume, which expands as the intensity of these shocks increases. Commonality in the context of recent findings in the literature of covariation in volume suggests the existence of covariation in investor heterogeneity, as measured, for example, by co-movements in the volatility of idiosyncratic liquidity shocks experienced by investors. The tax cycle is one potential source of such covariation although as conjectured by Chordia et al. (2000), behavioral factors may also be at work. Huberman and Halka (2001) conjecture that commonality emerges due to noise traders, which is consistent with our model if the volatility of idiosyncratic liquidity shocks is considered as a proxy for the level of noise in the market. We also provide new insights into the pricing of illiquidity. Amihud and Mendelson (1986) empirically demonstrate that asset returns are increasing in the cost of transacting (bid–ask spread) and hypothesize that in equilibrium, assets with higher bid–ask spreads will be held by investors with longer investment time horizons. Brennan and Subrahmanyam (1996) also find a significant relationship between required rates of return and measures of illiquidity, after adjusting for the Fama and French risk factors and the stock price level. However, Eleswarapu and Reinganum (1993) find a significant liquidity premium only in January. As noted by Brennan and Subrahmanyam (1996), these differences may be due in part to the noisiness of transactions cost measures. However, as our analysis suggests, different liquidity variables measure different things, which may also be a confounding factor in empirical analysis. Moreover, whereas the traditional focus has been on factors related to the supply of liquidity, we show that liquidity is the outcome of both demand and supply factors, with the demand side having a much more significant and varied impact than previously thought to be the case in the literature. When investors have differences in liquidity demand due to differences in their exposure to liquidity shocks, we show that investors with lower exposure to liquidity shocks will supply liquidity to investors with higher exposure, and benefit from a higher risk-adjusted return by doing so. Thus, in addition to receiving higher returns by holding less liquid assets (as in Amihud and Mendelson, 1986), low-exposure investors will also receive a higher risk-adjusted return than high-exposure investors from the assets that they hold in common. Surprisingly, our results are largely independent of the number of different securities traded in the market. With multiple securities, systematic liquidity shocks continue to be fully priced, since they are, by definition, perfectly correlated across investors, making them impossible to diversify by trading. This would be the case even if these shocks were not common across assets. In contrast, idiosyncratic liquidity shocks are priced only if they cannot be mutualized by trading. Even if idiosyncratic liquidity shocks were common across assets while being idiosyncratic across investors, there will be no transmission across assets as long as all assets can be freely traded. The only case in which one asset can be a “liquidity substitute” for another asset is if liquidity shocks on one asset can be better mitigated by trading another asset, which would arise if there were significant liquidity differences between the assets, all else equal. In such cases, the market price of liquid substitutes can be used to benchmark the value of illiquid securities. Indeed, in the extreme case when perfectly liquid but otherwise identical substitutes exist for illiquid securities, the price discount due to illiquidity should be zero in the absence of short-sale constraints. The magnitude of the discounts observed empirically suggests that the unavailability of liquid substitutes and/or short sale restrictions may be significant impediments to hedging the liquidity risk of illiquid securities in this way. The rest of the paper is organized as follows. In the next section, we develop the benchmark model of our paper. In Section 3, we examine the transmission of liquidity across investors, and study the differential effects of systematic and idiosyncratic liquidity shocks on asset prices, trading volume and price volatility. In Section 4, we extend the analysis to the case of multiple securities to examine liquidity transmission across securities, and study cases in which liquid securities can act as substitutes for their illiquid counterparts. Section 5 concludes.