A weakly stationary process has short memory when its autocorrelation function (ACF) ?(:) is geometrically bounded.
The stationary stochastic processes frequently referred in financial time series, such as ARCH (Engle, 1982), GARCH (Bollerslev, 1986), IGARCH (Engle and Bollerslev, 1986), and EGARCH (Nelson, 1991) all have short memory for volatilities.
A weakly stationary process have long memory if its ACF has a hyperbolic decay. The stationary stochastic processes such as LMSV (Bredit, Crato and de Lima, 1994), FIGARCH (Baillie, Bollerslev and Mikkelsen, 1996), FIEAGRCH (Nelson, 1991; Bollerslev and Mikkelsen, 1996) are long-memory models for volatilitis.
FIGARCH, Fractionally Integrated ARCH model (e.g. Andersen and Bollslev 1997; Baillie et al 1996).
Baillie et al. (1996) (hereafter denoted BBM) introduce the Fractionally Integrated GARCH (FIGARCH) model
Absolute returns and squared returns are proxies for volatility.
DING, Zhuanxin, Clive W. J. GRANGER and Robert F. ENGLE, 1993. A long memory property of stock market returns and a new model, Journal of Empirical Finance, Volume 1, Issue 1, June 1993, Pages 83-106. [Cited by 593] (111.52/year)
Abstract: "A ‘long memory’ property of stock market returns is investigated in this paper. It is found that not only there is substantially more correlation between absolute returns than returns themselves, but the power transformation of the absolute return ¦rt¦d also has quite high autocorrelation for long lags. It is possible to characterize ¦rt¦d to be ‘long memory’ and this property is strongest when d is around 1. This result appears to argue against ARCH type specifications based upon squared returns. But our Monte-Carlo study shows that both ARCH type models based on squared returns and those based on absolute return can produce this property. A new general class of models is proposed which allows the power δ of the heteroskedasticity equation to be estimated from the data."
BARNDORFF-NIELSEN, Ole E. and Neil SHEPHARD, 2001. Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics. Journal of the Royal Statistical Society: Series B (Statistical Methodology), Volume 63 Issue 2, Pages 167-241. [Cited by 282] (52.81/year)
Abstract: "Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures. This paper develops this potential, drawing on and extending powerful results from probability theory for applications in statistical analysis. Their power is illustrated by a sustained application of OU processes within the context of finance and econometrics. We construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive OU processes, and we study these models in relation to financial data and theory."
BARNDORFF-NIELSEN, Ole E. and Neil SHEPHARD, 2002. Econometric analysis of realized volatility and its use in estimating stochastic volatility models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), Volume 64, Issue 2, May 2002, Page 253-280. [Cited by 221] (49.94/year)
Abstract: "The availability of intraday data on the prices of speculative assets means that we can use quadratic variation-like measures of activity in financial markets, called realized volatility, to study the stochastic properties of returns. Here, under the assumption of a rather general stochastic volatility model, we derive the moments and the asymptotic distribution of the realized volatility error—the difference between realized volatility and the discretized integrated volatility (which we call actual volatility). These properties can be used to allow us to estimate the parameters of stochastic volatility models without recourse to the use of simulation-intensive methods."
ANDERSEN, T.G., et al., 2001. The Distribution of Realized Exchange Rate Volatility, Journal of the American Statistical Association, Volume 96, Number 453, March, Pages 42-55. [Cited by 228] (42.79/year)
Abstract: "Using high-frequency data on Deutschemark and Yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility and correlation, covering an entire decade. Our estimates, termed realized volatilities and correlations, are not only model-free, but also approximately free of measurement error under general conditions, which we discuss in detail. Hence, for practical purposes, we may treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normality-inducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and persistent dynamics in volatilities and correlations, evidence of long-memory dynamics in volatilities and correlations, and remarkably precise scaling laws under temporal aggregation."
\citeasnoun{Andersen-etal01b} found long memory in exchange rate volatility.
BAILLIE, Richard T., 1996. Long memory processes and fractional integration in econometrics. Journal of Econometrics, Volume 73, Issue 1 , July 1996, Pages 5-59. [Cited by 401] (38.83/year)
Abstract: "This paper provides a survey and review of the major econometric work on long memory processes, fractional integration, and their applications in economics and finance. Some of the definitions of long memory are reviewed, together with previous work in other disciplines. Section 3 describes the population characteristics of various long memory processes in the mean, including ARFIMA. Section 4 is concerned with estimation and examines semiparametric procedures in both the frequency and time domain, and also the properties of various regression based and maximum likelihood techniques. Long memory volatility processes are discussed in Section 5, while Section 6 discusses applications in economics and finance. The paper also has a concluding section."
TAYLOR, S.J., 1986. Modeling Financial Time Series, Chichester: Wiley. [Cited by 7] (0.34/year)
Taylor (1986) was the first to notice the apparent stylized fact that the absolute values of stock returns tended to have very slowly decaying autocorrelations.
HOSKING, J.R.M., 1981. Fractional differencing, Biometrika, Vol. 68, No. 1. (Apr., 1981), pp. 165-176. [Cited by 875] (34.53/year)
Abstract: "The family of autoregressive integrated moving-average processes, widely used in time series analysis, is generalized by permitting the degree of differencing to take fractional values. The fractional differencing operator is defined as an infinite binomial series expansion in powers of the backward-shift operator. Fractionally differences processes exhibit long-term persistence and antipersistence; the dependence between observations a long time span apart decays much more slowly with time span than is the case with the more commonly studied time series models. Long-term persistent processes have applications in economics and hydrology; compared to existing models of long-term persistence, the family of models introduced here offers much greater flexibility in the simultaneous modelling of the short-term and long-term behaviour of a time series."
ALIZADEH, Sassan, Michael W. BRANDT and Francis X. DIEBOLD, 2002. Range-Based Estimation of Stochastic Volatility ModelsThe Journal of Finance, Vol. 57, No. 3. (Jun., 2002), pp. 1047-1091. [Cited by 149] (34.33/year)
Abstract: "We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range-based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range-based Gaussian quasi-maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine the dynamics of daily exchange rate volatility and find the evidence points strongly toward two-factor models with one highly persistent factor and one quickly mean-reverting factor."
@article{PoonGranger03,
author = {Ser-Huang Poon and Clive W. J. Granger},
title = {Forecasting Volatility in Financial Markets: A Review},
journal = {Journal of Economic Literature},
year = {2003},
volume = {41},
number = {2},
pages = {478--539},
month = {June},
note = {},
key = {},
abstract = {n/a}
}
GRANGER, Clive W.J., Scott SPEAR and Zhuanxin DING, 2000. Stylized facts on the temporal and distributional properties of absolute returns: An update. Proceedings of the Hong Kong International Workshop on Statistics and Finance: An Interface, edited by Wai-Sum Chan, Wai Keung Li and Howell Tong, pages 97-120. [Cited by 13] (2.05/year)
Abstract: "The possibility of specific long-memory temporal properties exponential marginal distributionals of absolute returns are considered for daily data for a number of markets and similar results are found in each case. Possible explanations are considered but no complete explanation is found. A fractionally integrated model is considered, found to require an unusual distribution for its inputs, has a poor forecasting performance, and its properties may be explained by a regime-switching process."
Conclusions: "The stylized facts presented are that absolute returns of daily prices have long-memory, and have, approximately, an exponential distribution after outlier reduction. These facts are found for stock price indices, for individual shares, and for the residuals of CAPM model as well as commodity prices and an interest rate. A simple parsimonious model using a fractional difference I(d) model finds that the d coefficient is not constant through time and may vary similarly in New York and London. It is a challenge to stock market observers and theorists to explain these “fact”, in some way that the efficient market theory explained the random walk hypothesis, which might once have been considered a stylized fact. An implication of the results is that many of the statistical tests being used with data are inappropriate because of the long-memory property."
CONT, R., 2001. Empirical properties of asset returns: Stylized facts and statistical issues, Quantitative Finance. [Cited by 138] (25.90/year)
We present a set of stylized empirical facts emerging from the statistical analysis of price variations in various types of financial markets. We first discuss some general issues common to all statistical studies of financial time series. Various statistical properties of asset returns are then described: distributional properties, tail properties and extreme fluctuations, pathwise regularity, linear and nonlinear dependence of returns in time and across stocks. Our description emphasizes properties common to a wide variety of markets and instruments. We then show how these statistical properties invalidate many of the common statistical approaches used to study financial data sets and examine some of the statistical problems encountered in each case.
BREIDT, F. Jay, Nuno CRATO and Pedro de LIMA, 1998. The detection and estimation of long memory in stochastic volatility, Journal of Econometrics, Volume 83, Issues 1-2, March-April 1998, Pages 325-348. [Cited by 221] (26.50/year)
Abstract: "We propose a new time series representation of persistence in conditional variance called a long memory stochastic volatility (LMSV) model. The LMSV model is constructed by incorporating an ARFIMA process in a standard stochastic volatility scheme. Strongly consistent estimators of the parameters of the model are obtained by maximizing the spectral approximation to the Gaussian likelihood. The finite sample properties of the spectral likelihood estimator are analyzed by means of a Monte Carlo study. An empirical example with a long time series of stock prices demonstrates the superiority of the LMSV model over existing (short-memory) volatility models."
CRATO, Nuno and Pedro J. F. de LIMA, 1994. Long-range dependence in the conditional variance of stock returns. Economics Letters, Volume 45, Issue 3, 1994, Pages 281-285. [Cited by 27] (2.19/year)
Abstract: "We examine persistence in the conditional variance of U.S. stock returns indexes. Our results show evidence of long memory in high-frequency data, suggesting that models of conditional heteroskedasticity should be made flexible enough to accommodate these empirical findings."
DIEBOLD, F.X. and A. INOUE, 2000. Long Memory and Regime Switching, Journal of Econometrics, Volume 105, Issue 1, November 2001, Pages 131-159. [Cited by 155] (24.45/year)
The theoretical and empirical econometric literatures on long memory and regime switching have evolved largely independently, as the phenomena appear distinct. We argue, in contrast, that they are intimately related, and we substantiate our claim in several environments, including a simple mixture model, Engle and Smith’s (Rev. Econom. Statist. 81 (1999) 553–574) stochastic permanent break model, and Hamilton’s (Econometrica 57 (1989) 357–384) Markov-switching model. In particular, we show analytically that stochastic regime switching is easily confused with long memory, even asymptotically, so long as only a “small” amount of regime switching occurs, in a sense that we make precise. A Monte Carlo analysis supports the relevance of the theory and produces additional insights.
BOLLERSLEV, Tim and Hans Ole MIKKELSEN, 1996. Modeling and pricing long memory in stock market volatility, Journal of Econometrics, Volume 73, Issue 1, July 1996, Pages 151-184. [Cited by 244] (23.57/year)
A new class of fractionally integrated GARCH and EGARCH models for characterizing financial market volatility is discussed. Monte Carlo simulations illustrate the reliability of quasi maximum likelihood estimation methods, standard model selection criteria, and residual-based portmanteau diagnostic tests in this context. New empirical evidence suggests that the apparent long-run dependence in U.S. stock market volatility is best described by a mean-reverting fractionally integrated process, so that a shock to the optimal forecast of the future conditional variance dissipate at a slow hyperbolic rate. The asset pricing implications of this finding is illustrated via the implementation of various option pricing formula."
ANDERSEN, Torben G. and Tim BOLLERSLEV, 1997. Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns, The Journal of Finance, Vol. 52, No. 3, Papers and Proceedings Fifty-Seventh Annual Meeting, American Finance Association, New Orleans, Louisiana January 4-6, 1997. (Jul., 1997), pp. 975-1005. [Cited by 220] (23.53/year)
Abstract: "Recent empirical evidence suggests that the interdaily volatility clustering for most speculative returns are best characterized by a slowly mean-reverting fractionally integrated process. Meanwhile, much shorter lived volatility dynamics are typically observed with high frequency intradaily returns. The present article demonstrates, that by interpreting the volatility as a mixture of numerous heterogeneous short-run information arrivals, the observed volatility process may exhibit long-run dependence. As such, the long-memory characteristics constitute an intrinsic feature of the return generating process, rather than the manifestation of occasional structural shifts. These ideas are confirmed by our analysis of a one-year time series of five-minute Deutschemark-U.S. Dollar exchange rates."
DING, Zhuanxin and Clive W. J. GRANGER, 1996. Modeling volatility persistence of speculative returns: A new approach, Journal of Econometrics, Volume 73, Issue 1, July 1996, Pages 185-215. [Cited by 229] (22.17/year)
Abstract: "This paper extends the work by Ding, Granger, and Engle (1993) and further examines the long memory property for various speculative returns. The long memory property found for S&P 500 returns is also found to exist for four other different speculative returns. One significant difference is that for foreign exchange rate returns, this property is strongest when d = 1/4 instead of at d = 1 for stock returns. The theoretical autocorrelation functions for various GARCH(1, 1) models are also derived and found to be exponential decreasing, which is rather different from the sample autocorrelation function for the real data. A general class of long memory models that has no memory in returns themselves but long memory in absolute returns and their power transformations is proposed. The issue of estimation and simulation for this class of model is discussed. The Monte Carlo simulation shows that the theoretical model can mimic the stylized empirical facts strikingly well."
GUILLAUME, D.M., et al., 1997. From the bird's eye to the microscope: A survey of new stylized facts of the intra-daily foreign exchange markets. Finance and Stochastics. [Cited by 172] (18.46/year)
Abstract: "This paper presents stylized facts concerning the spot intra-daily foreign exchange markets. It first describes intra-daily data and proposes a set of definitions for the variables of interest. Empirical regularities of the foreign exchange intra-daily data are then grouped under three major topics: the distribution of price changes, the process of price formation and the heterogeneous structure of the market. The stylized facts surveyed in this paper shed new light on the market structure that appears composed of heterogeneous agents. It also poses several challenges such as the definition of price and of the time-scale, the concepts of risk and efficiency, the modeling of the markets and the learning process."
LOBATO, I.N. and N.E. SAVIN, 1998. Real and Spurious Long-Memory Properties of Stock-Market Data. Journal of Business & Economic Statistics, Vol. 16, No. 3. (Jul., 1998), pp. 261-268. [Cited by 139] (16.71/year)
Abstract: "We test for the presence of long memory in daily stock returns and their squares using a robust semiparametric procedure. Spurious results can be produced by nonstationarity and aggregation. We address these problems by analyzing subperiods of returns and using individual stocks. The test results show no evidence of long memory in the returns. By contrast, there is strong evidence in the squared returns."
GRANGER, Clive W. J. and Namwon HYUNG, 1999. Occasional Structural Breaks and Long Memory. ideas.repec.org. [Cited by 119] (16.26/year)
"This paper shows that a linear process with breaks can mimic autocorrelations and other properties of I(d) processes, where d can be a fraction. Simulation results show that S&P 500 absolute stock returns are more likely to show the "long memory" property because of the presence of breaks in the series rather than an I(d) process."
COMTE, Fabienne and Eric RENAULT, 1998. Long memory in continuous-time stochastic volatility models, Mathematical Finance, Vol. 8, No. 4 (October 1998), 291-323. [Cited by 126] (15.11/year)
Abstract: "This paper studies a classical extension of the Black and Scholes model for option pricing, often known as the Hull and White model. Our specification is that the volatility process is assumed not only to be stochastic, but also to have long-memory features and properties. We study here the implications of this continuous-time long-memory model, both for the volatility process itself as well as for the global asset price process. We also compare our model with some discrete time approximations. Then the issue of option pricing is addressed by looking at theoretical formulas and properties of the implicit volatilities as well as statistical inference tractability. Lastly, we provide a few simulation experiments to illustrate our results."
HARVEY, Andrew C., 1998. Long memory in stochastic volatility. In: Forecasting Volatility in Financial Markets edited by Stephen Satchell and John Knight. [Cited by 100] (13.23/year)
Abstract: "A long memory stochastic volatility model is proposed. Its dynamic properties are derived and shown to be consistent with empical (sic) findings reported in the literature on stock returns. Ways in which the model may be estimated are discussed and it is shown how estimates of the underlying variance may be constructed and predictions made. The model is parsimonious and appears to be a viable alternative to the A-PARCH class proposed by Ding, Granger and Engle (1993) and the FIEGARCH class of Bollerslev and Mikkelsen (1996)."
Conclusions: "Ding, Granger and Engle (1993) reported two sets of stylized facts for daily stock market data. The first is that the autocorrelations of absolute values of returns tend to be maximized when raised to a power slightly less than one. However, analysis for a stochastic volatility model shows that the maximizing power depends on the strength of the volatility process as measured by its variance and so is not to be taken as an indication of the type of volatility process which is appropriate. The second stylized fact is the tendency of autocorrelations to die away slowly. This feature can be nicely captured by a long memory SV model.
Estimation of the long memory SV model is not as easy as the AR-SV model because the linear statespace form is difficult to use. Generalized method-of-moments (GMM) is possible, but is likely to have very poor properties because of the difficulty of capturing the long memory by a small number of autocovariances. One method that is relatively straightforward to apply is QML in the frequency domain and this may be a reasonable option if the sample size is large."
GIRAITIS, L., et al., 2003. Rescaled variance and related tests for long memory in volatility and levels. Journal of Econometrics, Volume 112, Issue 2, February 2003, Pages 265-294. [Cited by 43] (12.92/year)
Abstract: "This paper studies properties of tests for long memory for general fourth order stationary sequences. We propose a rescaled variance test based on V/S statistic which is shown to have a simpler asymptotic distribution and to achieve a somewhat better balance of size and power than Lo’s (Econometrica 59 (1991) 1279) modified R/S test and the KPSS test of Kwiatkowski et al. (J. Econometrics 54 (1992) 159). We investigate theoretical performance of R/S, KPSS and V/S tests under short memory hypotheses and long memory alternatives, providing a Monte Carlo study and a brief empirical example. Assumptions of the same type are used in both short and long memory cases, covering all persistent dependence scenarios. We show that the results naturally apply and the assumptions are well adjusted to linear sequences (levels) and to squares of linear ARCH sequences (volatility)."
GRANGER, Clive W. J. and Zhuanxin DING, 1996. Varieties of long memory models, Journal of Econometrics, Volume 73, Number 1, July 1996, pp. 61-77. [Cited by 122] (11.82/year)
Abstract: "Long memory is defined as a series having a slowly declining correlogram or, equivalently, an infinite spectrum at zero frequency. Fractional integrated processes have such properties but here it is pointed out that a number of other processes can also be long memory, including generalized fractionally integrated models arising from aggregation, time-changing coefficient models, and possibly nonlinear models. It seems that there are many classes of processes that deserve further study. The relevance of long memory is illustrated using absolute returns from a daily stock market index."
DACOROGNA, Michael M., et al., 1993. A Geographical Model for the Daily and Weekly Seasonal Volatility in the Foreign Exchange Market, Journal of International Money and Finance, Volume 12, Issue 4, August 1993, Pages 413-438. [Cited by 156] (11.68/year)
Abstract: "The daily and weekly seasonality of foreign exchange volatility is modelled by introducing an activity variable. This activity is explained by a simple model of the changing and sometimes overlapping market presence of geographical components (East Asia, Europe, and America).
Integrating this activity over time results in the new Image time scale, characterized by non-seasonal volatility. This scale, applied to dense datastreams of absolute price changes, suceeds in removing most of the seasonal heteroscedasticity in an autocorrelation study. Unexpectedly, the positive autocorrelation is found to decline hyperbolically rather than exponentially as a function of the lag."
GRANGER, Clive W.J. and Namwon HYUNG, 2004. Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns. Journal of Empirical Finance, Volume 11, Issue 3, June 2004, Pages 399-421. [Cited by 27] (11.54/year)
Abstract: "This paper shows that occasional breaks generate slowly decaying autocorrelations and other properties of I(d) processes, where d can be a fraction. Some theory and simulation results show that it is not easy to distinguish between the long memory property from the occasional-break process and the one from the I(d) process. We compare two time series models, an occasional-break model and an I(d) model to analyze S&P 500 absolute stock returns. An occasional-break model performs marginally better than an I(d) model in terms of in-sample fitting. In general, we found that an occasional-break model provides less competitive forecasts, but not significantly. However, the empirical results suggest a possibility such that, at least, part of the long memory may be caused by the presence of neglected breaks in the series. We show that the forecasts by an occasional break model incorporate incremental information regrading future volatility beyond that found in I(d) model. The findings enable improvements of volatility prediction by combining I(d) model and occasional-break model."
ANDREOU, E. and E. GHYSELS, 2002. Detecting multiple breaks in financial market volatility dynamics. Journal of Applied Econometrics, Volume 17, Issue 5, Date: September/October 2002, Pages: 579-600. [Cited by 49] (11.29/year)
Abstract: "The paper evaluates the performance of several recently proposed tests for structural breaks in the conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using high-frequency data. In addition to testing for the presence of breaks, the statistics identify the number and location of multiple breaks. We study the size and power of the new tests for detecting breaks in the conditional variance under various realistic univariate heteroscedastic models, change-point hypotheses and sampling schemes. The paper concludes with an empirical analysis using data from the stock and FX markets for which we find multiple breaks associated with the Asian and Russian financial crises. These events resulted in changes in the dynamics of volatility of asset returns in the samples prior and post the breaks."
KIRMAN, Alan and Gilles TEYSSIÈRE, 2002. Microeconomic models for long memory in the volatility of financial time series, Studies in Nonlinear Dynamics and Econometrics, Volume 5, Issue 4, Article 3. [Cited by 49] (11.35/year)
Abstract: "We show that a class of microeconomic behavioral models with interacting agents, derived from Kirman (1991) and Kirman (1993), can replicate the empirical long-memory properties of the two first-conditional moments of financial time series. The essence of these models is that the forecasts and thus the desired trades of the individuals in the markets are influenced, directly or indirectly, by those of the other participants. These “field effects” generate “herding” behavior that affects the structure of the asset price dynamics. The series of returns generated by these models display the same empirical properties as financial returns: returns are I (0), the series of absolute and squared returns display strong dependence, and the series of absolute returns do not display a trend. Furthermore, this class of models is able to replicate the common long-memory properties in the volatility and covolatility of financial time series revealed by Teyssière (1997, 1998a). These properties are investigated by using various model-independent tests and estimators, that is, semiparametric and nonparametric, introduced by Lo (1991), Kwiatkowski et al. (1992), Robinson (1995), Lobato and Robinson (1998), and Giraitis et al. (2000, forthcoming). The relative performance of these tests and estimators for long memory in a nonstandard data-generating process is then assessed."
GALLANT, A. Ronald, Chien-Te HSU and George TAUCHEN, 1999. Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance, The Review of Economics and Statistics, Vol. 81, No. 4. (Nov., 1999), pp. 617-631. [Cited by 77] (10.49/year)
Abstract: "A common model for security price dynamics is the continuous-time stochastic volatility model. For this model, Hull and White (1987) show that the price of a derivative claim is the conditional expectation of the Black-Scholes price with the forward integrated variance replacing the Black-Scholes variance. Implementing the Hull and White characterization requires both estimates of the price dynamics and the conditional distribution of the forward integrated variance given observed variables. Using daily data on close-to-close price movement and the daily range, we find that standard models do not fit the data very well and that a more general three-factor model does better, as it mimics the long-memory feature of financial volatility. We develop techniques for estimating the conditional distribution of the forward integrated variance given observed variables."
In this paper, we provide a detailed characterization of the return volatility in US Treasury bond futures contracts using a sample of 5-min returns from 1994 to 1997. We find that public information in the form of regularly scheduled macroeconomic announcements is an important source of volatility at the intraday level. Among the various announcements, we identify the Humphrey–Hawkins testimony, the employment report, the producer price indexPPI., the employment cost, retail sales, and the NAPM survey as having the greatest impact. Our analysis also uncovers striking long-memory volatility dependencies in the fixed income market, a finding with important implications for the pricing of long-term options and other related instruments.
BOLLERSLEV, T. and J.H. WRIGHT, 2000. Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data. Journal of Econometrics. [Cited by 30] (5.40/year)
Recent empirical studies have argued that the temporal dependencies in "nancial market volatility are best characterized by long memory, or fractionally integrated, time series models. Meanwhile, little is known about the properties of the semiparametric inference procedures underlying much of this empirical evidence. The simulations reported in the present paper demonstrate that, in contrast to log-periodogram regression estimates for the degree of fractional integration in the mean (where the span of the data is crucially important), the quality of the inference concerning long-memory dependencies in the conditional variance is intimately related to the sampling frequency of the data. Some new estimators that succinctly aggregate the information in higher frequency returns are also proposed. The theoretical "ndings are illustrated through the analysis of a ten-year time series consisting of more than half-a-million intradaily observations on the Japanese Yen}U.S. Dollar exchange rate.
LUX, Thomas, 1996. Long-term stochastic dependence in financial prices: evidence from the German stock market, Applied Economics Letters, Volume 3, Number 11, 1 November 1996, pp. 701-706. [Cited by 18] (1.74/year)
Abstract: "A number of authors have argued that financial prices may exhibit hidden long-term dependence. We consider this claim analysing German stock market data. Applying three different concepts for the identification of long memory effects, virtually no evidence of such behaviour is found for stock market returns. Another recent assertion says that long term memory may not be pertinent to stock returns but rather to the conditional volatility of financial market prices. As it turns out, this claim is very much supported by our investigation of German stock market data. Furthermore, the long memory property is more pronounced in absolute values of returns than in the squares of returns (both used as proxies for volatility). The methods employed are: the time-honoured procedure of estimating the Hurst exponent for the scaling behaviour of the range of cumulative departures from the mean of a time series, the modified range analysis."
CAVALCANTE, J. and A. ASSAF, 2002. Long Range Dependence in the Returns and Volatility of the Brazilian Stock Market Manuscript. Rio de Janeiro. [Cited by 1] (0.28/year)
CAVALCANTE, Jorge and Ata ASSAF, 2002. Long Range Dependence in the Returns and Volatility of the Brazilian Stock Market Manuscript. Rio de Janeiro. [Cited by 1] (0.23/year)
Abstract: "This study provides empirical evidence of the long-range dependence in the returns and volatility of Brazilian Stock Market (BSM). We test for long memory in the daily returns and volatility series. The measures of long-term persistence employed are the modified rescaled range (R/S) statistic proposed by Lo (1991), the rescaled variance V/S statistic proposed by Giraitis et al. (2003), and the semiparametric estimator of Robinson (1995). Further analysis is conducted via FIGARCH model of Baillie et al. (1996). Significant long memory is conclusively demonstrated in the volatility measures, while there is a little evidence of long memory in the returns themselves. This evidence disputes the hypothesis of market efficiency and therefore implies fractal structure in the emerging stock market of Brazil. We conclude, that stock market dynamics in the biggest emerging market, even with its different institutions and information flows than the developed market, present similar return-generating process to the preponderance of studies employing other data. Our results should be useful to regulators, practitioners and derivative market participants, whose success depends on the ability to forecast stock price movements."
BRIEDT, J., N. CRATO and P. DELIMA, 1998. On the Detection and Estimation of Long-Memory in Stochastic Volatility, forthcoming. Journal of Econometrics. [Cited by 1] (0.13/year)
GRANGER, C., 1980. Long Memory Relationships and the Aggregation of Aggregate Dynamic Models. Journal of Econometrics. [Cited by 1] (0.04/year)