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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|14891||2009||22 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||23 روز بعد از پرداخت||1,552,410 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||12 روز بعد از پرداخت||3,104,820 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Financial Economics, Volume 94, Issue 2, November 2009, Pages 192–213
We propose a new empirical specification of volatility that links volatility to the information flow, measured as the order flow in the market, and to the price sensitivity to that information. The time-varying market sensitivity to information is estimated from high-frequency data, and movements in volatility can therefore be directly related to movements in order flow and market sensitivity. Empirically, the model explains a large share of the long-run variation in volatility. Importantly, the time variation in the market's sensitivity to information is at least as relevant in explaining the persistence of volatility as the rate of information arrival itself. This may be evidence of a link between changes over time in the aggregate behavior of market participants and the time-series properties of realized volatility.
The results of more than two decades of empirical volatility modeling all point towards the conclusion that the volatility of asset prices changes over time in a fairly persistent manner. However, there is still no clear agreement on why this is the case. The most common hypothesis is that the rate of information arrival affecting the price of an asset is itself persistent, perhaps because economic data relevant to the price are released in a clustered fashion or because analysis relevant to the price is produced in a clustered fashion. A less common explanation, which does not exclude the first one, is that variation over time in the sensitivity of market participants to information may also help explain the pattern of volatility persistence. This paper presents a simple, direct empirical test of the roles of both information arrival and market sensitivity in driving volatility persistence, using a unique high-frequency data set covering several years of global interdealer foreign exchange trading. Much of the previous research on factors driving volatility derives from studies of ‘mixture of distributions’ models proposed by Clark (1973), Tauchen and Pitts (1983), and Andersen (1996), among others, where volatility and trading volume are jointly directed by the process of information arrival. Thus, in these models, persistence in the information arrival process generates persistence in volatility and trading volume. In practice, however, the estimated persistence of volatility in such models has typically been found to be well below that of univariate time-series models of volatility. Liesenfeld (2001), extending the previous mixture models, allows for volume and volatility to be driven not only by a latent information arrival process but also by an additional latent process that governs the impact of information on prices. He shows that such a model, where the dynamics of volatility are associated with both the rate of information arrival and the time-varying sensitivity to information, can capture much more of the persistence in volatility. Though our paper is inspired by the result of Liesenfeld (2001), it takes a very different approach from previous work on the study of volatility persistence. In particular, we draw upon recent findings on the relationship between returns and order flow. This relationship, which has been studied extensively in the foreign exchange market in the past few years (e.g., Evans and Lyons, 2002 and Evans and Lyons, 2004), has been found to account for a substantial share of observed movements in exchange rates. The main thrust of the current paper is to show that there are large and persistent variations over time in the relationship between returns and order flow, and to study how these variations, along with variations in the order flow itself, are linked to the time-series behavior of volatility. Evans and Lyons and other authors have argued that the contemporaneous explanatory power of order flow for exchange rate movements derives in great part from the fact that information relevant to the price is transmitted to the market through order flow, a point previously demonstrated by Hasbrouck (1991) for the stock market. To the extent that order flow represents information, our empirical specification therefore allows us to decompose the factors affecting volatility into two time-varying components: the flow of information itself and the sensitivity of the market to that information. It should be stressed, however, that our results on the role of market sensitivity in driving volatility do not depend upon the interpretation of order flow as information, although we focus on that interpretation. The empirical analysis is conducted using a unique data set that represents a majority of global interdealer trading in the spot euro-dollar exchange rate at the one-minute frequency over a period of several years. Importantly, the frequency and the content of the data permit us to estimate a time-varying market sensitivity parameter at the daily frequency. We can therefore treat the market sensitivity as an observed variable, along with order flow and realized volatility. This allows us to analyze directly the interactions of the time-series properties of these three variables, without relying on latent variables. Focusing on the fact that volatility is a slow-moving and fairly persistent process that is well described by a long-memory model, we frame the empirical analysis as a test of fractional cointegration. This provides a test of how closely the long-run movements in volatility are linked to the long-run swings in order flow and market sensitivity. This empirical setup offers several advantages. By explicitly modeling the persistence in the data, it becomes possible to evaluate the degree to which the persistence in the dependent variable is captured by movements in the right-hand side variables. In particular, the long-memory framework provides a concise summary statistic of the degree of persistence and thus easily enables comparison between the persistence in the original data and in the cointegration residuals. In addition, by testing for (fractional) cointegration, we guard against the risk of spurious regression, a possibility given the persistent nature of the data. Finally, the estimation methods employed are robust to potential endogeneity in the regressors, which is clearly a concern in a standard ordinary least squares (OLS) analysis of these time series. Overall, the empirical results are very strong. We show that a very large share of the persistence in volatility can be explained by variations in the market's sensitivity to order flow and variations in the order flow itself. Thus, if one interprets order flow as information, the results show that volatility persistence can be linked to both the persistence of the market's sensitivity to new information and the persistence of the information flow itself. Furthermore, the empirical analysis clearly shows that the variations in market sensitivity play at least as large a role in driving volatility persistence as the variations in the flow of information. This finding may point to an important link between changes over time in the aggregate behavior of market participants and the times-series properties of volatility. From an econometric point of view, we observe that the measured persistence in the information flow is not large enough to capture the persistence in volatility, whereas the persistence of the sensitivity parameter is very similar to that of volatility. In fact, our results show that the information flow appears more closely linked to the shorter-, or medium-term, behavior of volatility, whereas the market's sensitivity to information is more closely related to the longer-run behavior of volatility.1 The paper proceeds as follows. Section 2 introduces the high-frequency exchange rate data used in our analysis. Section 3 first derives our empirical specification, addressing its motivation, its empirical validity, and the constructed variables that we use in our estimations. It then presents some preliminary OLS analysis of the contemporaneous relationships between our constructed variables. Section 4 outlines the fractional integration and cointegration methodology used in the remainder of the paper. Section 5 presents our main estimation results, and Section 6 presents some additional extensions and robustness results. Section 7 studies the interaction between trading volume and our measure of market sensitivity, ruling out a major role for volume in explaining our results. Section 8 concludes.
نتیجه گیری انگلیسی
We have shown that movements in the market's sensitivity to information, jointly with movements in the rate of information arrival, can, to a very large degree, explain the long-run dynamics of realized exchange rate volatility. Importantly, our analysis reveals that variations in market sensitivity play at least as large a role in driving volatility persistence as do variations in the flow of information reaching the market through the trading process. This may be evidence of a link between changes over time in the aggregate behavior of market participants and the time-series properties of realized volatility. The analysis in this paper builds upon ideas contained in the ‘mixture of distributions’ literature, in particular in Liesenfeld (2001). However, our volatility model is based on relationships described in the market microstructure literature, and its estimation relies on the availability of a long time-series of price and trading data at very high frequency. As such, focusing on the micro and high-frequency aspects of the market has helped us to better explain movements in price volatility at frequencies more typical of the macro level. The fact that time-varying market sensitivity plays an important role in driving volatility fits well within the framework of several theoretical and empirical models of asset pricing and trading. However, these models provide differing explanations as to what may actually cause changes in market sensitivity over time. Some models focus more on the content of the information reaching the market, some on changes in the traders’ attitude towards risk, and some on the impact of changes in the composition or practices of market participants. On the information side, in Kyle's (1985) trading model, for instance, the response of the price to order flow depends on the amount of private information that informed traders have, relative to the expected amount of noise trading in the market. Within Kyle's framework, one may expect that, for example, times of heightened uncertainty about economic conditions, such as inflexion points in economic activity or in monetary policy, could be associated with a relative information advantage for the informed traders, thus resulting in persistent periods of higher market sensitivity. In the models of Evans and Lyons (2002) and Killeen, Lyons, and Moore (2006), which are aimed directly at understanding the impact of order flow in the foreign exchange market, the market sensitivity is assumed to be an increasing function of the absolute risk aversion parameter. This suggests the more drastic explanation that variations over time in market sensitivity may reflect changes in deeper, more fundamental parameters among market participants, including changes in risk aversion. In the same vein, McQueen and Vorkink (2004) offer a model where shocks to investors’ wealth temporarily affect both their attitude towards risk and the amount of attention they pay to news, resulting in variations in their sensitivity to information. Focusing more on the operational practices of market participants, Garleanu and Pedersen (2007), for instance, argue that standard risk management practices, such as value at risk, can impact market liquidity in a persistent way. In their model, heightened concerns by market participants, due to either fundamental or behavioral factors, lead to more stringent risk management practices and to a decrease in market liquidity. A feedback effect, with the resulting higher volatility leading to further tightening in risk management, could then cause persistent periods of reduced liquidity and of increased volatility. Such a mechanism is also potentially consistent with our observed swings in market sensitivity. Our present work provides direct evidence of the role of market sensitivity in driving variations over time in realized volatility in the foreign exchange market. Extending our work to other markets and studying whether the pattern of variation in market sensitivity is specific to certain assets or markets, or if there is a common component, should provide additional evidence on the factors driving the variation in market sensitivity and volatility.