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Финансовая экономика - это область теоретико-прикладных знаний о законах функционирования финансовых потоков и отношений между всеми субъектами экономической системы... (подробнее...)

Статьи

Всего статей в данном разделе : 34

Noise [статья]
Опубликовано на портале: 03-12-2007
Fisher Black Journal of Finance. 1986.  Vol. 21. P. 529-543. 
The effects of noise on the world, and on our views of the world, are profound. Noise in the sense of a large number of small events is often a causal factor much more powerful than a small number of large events can be. Noise makes trading in financial markets possible, and thus allows us to observe prices for financial assets. Noise causes markets to be somewhat inefficient, but often prevents us from taking advantage of inefficiencies. Noise in the form of uncertainty about future tastes and technology by sector causes business cycles, and makes them highly resistant to improvement through government intervention. Noise in the form of expectations that need not follow rational rules causes inflation to be what it is, at least in the absence of a gold standard or fixed exchange rates. Noise in the form of uncertainty about what relative prices would be with other exchange rates makes us think incorrectly that changes in exchange rates or inflation rates cause changes in trade or investment flows or economic activity. Most generally, noise makes it very difficult to test either practical or academic theories about the way that financial or economic markets work. We are forced to act largely in the dark
ресурс содержит полный текст, либо отрывок из него
Опубликовано на портале: 01-11-2007
Michael D. Atchison, Kirt C. Butler, Richard R. Simonds Journal of Finance. 1987.  Vol. 42. No. 1. P. 111-118. 
The theoretical portfolio autocorrelation due solely to nonsynchronous trading is estimated from a derived model. This estimated level is found to be substantially less than that observed empirically. The theoretical and empirical relationship between portfolio size and autocorrelation also is investigated. The results of this study suggest that other price-adjustment delay factors in addition to nonsynchronous trading cause the high autocorrelations present in daily returns on stock index portfolios
ресурс содержит полный текст, либо отрывок из него
Опубликовано на портале: 03-10-2003
John C. Cox, Stephen A. Ross, Mark Rubinstein Journal of Financial Economics. 1979.  Vol. 7. No. 3. P. 229-263. 
This paper presents a simple discrete-time model for valuing options. The fundamental economic principles of option pricing by arbitrage methods are particularly clear in this setting. Its development requires only elementary mathematics, yet it contains as a special limiting case the celebrated Black-Scholes model, which has previously been derived only by much more difficult methods. The basic model readily lends itself to generalization in many ways. Moreover, by its very construction, it gives rise to a simple and efficient numerical procedure for valuing options for which premature exercise may be optimal.
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Опубликовано на портале: 03-11-2007
Dong-Hyun Ahn, Jacob Boudoukh, Matthew Richardson, Robert Whitelaw Review of Financial Studies. 2002.  Vol. 15. No. 2. P. 655-689. 
We investigate the relation between returns on stock indices and their corresponding futures contracts to evaluate potential explanations for the pervasive yet anomalous evidence of positive, short-horizon portfolio antocorrelations. Using a simple theoretical framework, we generate empirical implications for both microstructure and partial adjustment models. The major findings are (i) return autocorrelations of indices are generally positive even though futures contracts have autocorrelations close to zero, and (ii) these autocorrelation differences are maintained under conditions favorable for spot-futures arbitrage and are most prevalent during low-volume periods. These results point toward microstructure-based explanations and away from explanations based on behavioral models.
ресурс содержит полный текст, либо отрывок из него
Опубликовано на портале: 25-10-2007
Timothy S. Mech Journal of Financial Economics. 1993.  Vol. 34. No. 3. P. 307-334. 
This paper investigates whether portfolio return autocorrelation can be explained by time-varying expected returns, nontrading, stale limit orders, market maker inventory policy, or transaction costs. Evidence is consistent with the hypothesis that transaction costs cause portfolio autocorrelation by slowing price adjustment. I develop a transaction-cost model which predicts that prices adjust faster when changes in valuation are large in relation to the bid-ask spread. Cross-sectional tests support this prediction, but time-series tests do not.
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Опубликовано на портале: 02-11-2007
Charles M.C. Lee, Bhaskaran Swaminathan Journal of Finance. 2000.  Vol. 55. No. 5. P. 2017-2069. 
This study shows that past trading volume provides an important link between ‘momentum’ and ‘value’ strategies. Specifically, we find that firms with high (low) past turnover ratios exhibit many glamour (value) characteristics, earn lower (higher) future returns, and have consistently more negative (positive) earnings surprises over the next eight quarters. Past trading volume also predicts both the magnitude and persistence of price momentum. Specifically, price momentum effects reverse over the next five years, and high (low) volume winners (losers) experience faster reversals. Collectively, our findings show that past volume helps to reconcile intermediate-horizon ‘underreaction’ and long-horizon ‘overreaction’ effects
ресурс содержит полный текст, либо отрывок из него
Опубликовано на портале: 01-11-2007
Jennifer Conrad, Mustafa N. Gultekin, Gautam Kaul Journal of Business and Economic Statistics. 1997.  Vol. 15. No. 3. P. 379-386. 
In recent years, several researchers have argued that the stock market consistently overreacts to new information, which, in turn, results in price reversals. Lehmann and others showed that a contrarian can make substantial profits in the short run by simply buying losers and selling winners. We, however, demonstrate that these profits are largely generated by the bid-ask bounce in transaction prices; accounting for this "bounce" by using bid prices eliminates all profits from price reversals for NASDAQ-NMS stocks and most of the profits for NYSE/AMEX stocks. Moreover, any remaining profits (regardless of their source) disappear at trivial levels of transactions costs.
ресурс содержит полный текст, либо отрывок из него
Опубликовано на портале: 25-10-2007
David M. Cutler, James Michael Poterba, Lawrence H. Summers American Economic Review. 1990.  Vol. 80. No. 2. P. 63-68. 
This paper summarizes our earlier research documenting the characteristic speculative dynamics of many asset markets and suggests a framework for understanding them. Our model incorporates "feedback traders," traders whose demand is based on the history of past returns rather than the expectation of future fundamentals. We use this framework to describe ways in which the characteristic return patterns might be generated, and also to address the long-standing question of whether profitable speculation stabilizes asset markets.
ресурс содержит полный текст, либо отрывок из него
Опубликовано на портале: 12-12-2002
Helyette Geman, Marc Yor, Dilip B. Madan Finance and Stochastics. 2002.  Vol. 6. No. 1. P. 63-90. 
Stochastic volatility and jumps are viewed as arising from Brownian subordination given here by an independent purely discontinuous process and we inquire into the relation between the realized variance or quadratic variation of the process and the time change. The class of models considered encompasses a wide range of models employed in practical financial modeling. It is shown that in general the time change cannot be recovered from the composite process and we obtain its conditional distribution in a variety of cases. The implications of our results for working with stochastic volatility models in general is also described. We solve the recovery problem, i.e. the identification the conditional law for a variety of cases, the simplest solution being for the gamma time change when this conditional law is that of the first hitting time process of Brownian motion with drift attaining the level of the variation of the time changed process. We also introduce and solve in certain cases the problem of stochastic scaling. A stochastic scalar is a subordinator that recovers the law of a given subordinator when evaluated at an independent and time scaled copy of the given subordinator. These results are of importance in comparing price quality delivered by alternate exchanges.
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Опубликовано на портале: 03-11-2007
Iwaisako Tokuo Discussion Paper Series of Hitotsubashi University. 2007. 
Following Lo and MacKinlay's work on the U.S. market (1988, 1990), this paper investigates the autocorrelation of the market index and the cross-autocorrelations of size-sorted portfolios in the Japanese market. The structure of the cross-autocorrelations in the Japanese market is very similar to that of the U.S. in the sense that there are lead-lag relations running from larger stocks to smaller stocks, which will create positive autocorrelation in the market index. Although we have found no autocorrelation in the popular Japanese TOPIX market index, it is because TOPIX puts much more weight on larger stocks compared to the CRSP index for the U.S. market. However, such a cross-autocorrelation structure disappeared during the latter half of the 1990s, as the largest stocks in the Japanese market began to exhibit negative autocorrelation. The possibility of a serious financial crisis during this period provides an explanation for negative autocorrelation. Some empirical evidence is provided for this explanation.
ресурс содержит полный текст, либо отрывок из него
Опубликовано на портале: 25-10-2007
Andrew W. Lo, A. Craig MacKinlay Review of Financial Studies. 1988.  Vol. 1. No. 1. P. 41-66. 
In this article we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived from data sampled at different frequencies. The random walk model is strongly rejected for the entire sampleperiod (1962-1985) and for all subperiods for a variety of aggregate returns indexes and size-sorted porfolios. Although the rejections are due largely to the behavior of small stocks, they cannot be attributed completely to the effects of infrequent trading or timevarying volatilities. Moreover, the rejection of the random walk for weekly returns does not support a mean-reverting model of assetprices.
ресурс содержит полный текст, либо отрывок из него
Опубликовано на портале: 25-10-2007
Diane DeQing Li, Kennet Yung Review of Accounting and Finance. 2006.  Vol. 5. No. 1. P. 45-58. 
Though stock portfolio return autocorrelation is well documented in the literature, its cause is still not clearly understood. Presently, evidence of private information induced stock return autocorrelation is still very limited. The difficulty in obtaining foreign country information by small investors makes the private information of institutional investors in the ADR (American Depository Receipt) market more significant and influential. As such, the ADR market provides a favorable environment for testing the effect of private information on return autocorrelation. The purpose of this paper is to address this issue.
ресурс содержит полный текст, либо отрывок из него
Опубликовано на портале: 25-10-2007
Kenneth R. French, Richard Roll Journal of Financial Economics. 1986.  Vol. 17. No. 1. P. 5-26. 
Asset prices are much more volatile during exchange trading hours than during non-trading hours. This paper considers three explanations for this phenomenon: (1) volatility is caused by public information which is more likely to arrive during normal business hours; (2) volatility is caused by private information which affects prices when informed investors trade; and (3) volatility is caused by pricing errors that occur during trading. Although a significant fraction of the daily variance is caused by mispricing, the behavior of returns around exchange holidays suggests that private information is the principle factor behind high trading-time variances.
ресурс содержит полный текст, либо отрывок из него
Опубликовано на портале: 25-10-2007
Jacob Boudoukh, Matthew Richardson, Robert Whitelaw NBER Working Papers. 2005.  w11841.
The prevailing view in finance is that the evidence for long-horizon stock return predictability is significantly stronger than that for short horizons. We show that for persistent regressors, a characteristic of most of the predictive variables used in the literature, the estimators are almost perfectly correlated across horizons under the null hypothesis of no predictability. For example, for the persistence levels of dividend yields, the analytical correlation is 99% between the 1- and 2-year horizon estimators and 94% between the 1- and 5-year horizons, due to the combined effects of overlapping returns and the persistence of the predictive variable. Common sampling error across equations leads to ordinary least squares coefficient estimates and R2s that are roughly proportional to the horizon under the null hypothesis. This is the precise pattern found in the data. The asymptotic theory is corroborated, and the analysis extended by extensive simulation evidence. We perform joint tests across horizons for a variety of explanatory variables, and provide an alternative view of the existing evidence.
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Опубликовано на портале: 26-10-2007
Yakov Amihud, Haim Mendelson Journal of Finance. 1987.  Vol. 42. No. 3. P. 533-553. 
This paper examines the effects of the mechanism by which securities are traded on their price behavior. We compare the behavior of open-to-open and close-to-close returns on NYSE stocks, given the differences in execution methods applied in the opening and closing transactions. Opening returns are found to exhibit greater dispersion, greater deviations from normality and a more negative and significant autocorrelation pattern than closing returns. We study the effects of the bid-ask spread and the price-adjustment process on the estimated return variances and covariances and discuss the associated biases. We conclude that the trading mechanism has a significant effect on stock price behavior.
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