1,720,976 research outputs found
Is GARCH(1,1) as good a model as the Nobel prize accolades would imply?
Full text linkThis paper investigates the relevance of the stationary, conditional, parametric ARCH modeling paradigm as embodied by the GARCH(1,1) process to describing and forecasting the dynamics of returns of the Standard & Poors 500 (S&P 500) stock market index. A detailed analysis of the series of S&P 500 returns featured in Section 3.2 of the Advanced Information note on the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel reveals that during the period under discussion, there were no (statistically significant) differences between GARCH(1,1) modeling and a simple non-stationary, non-parametric regression approach to next-day volatility forecasting. A second finding is that the GARCH(1,1) model severely over-estimated the unconditional variance of returns during the period under study. For example, the annualized implied GARCH(1,1) unconditional standard deviation of the sample is 35% while the sample standard deviation estimate is a mere 19%. Over-estimation of the unconditional variance leads to poor volatility forecasts during the period under discussion with the MSE of GARCH(1,1) 1-year ahead volatility more than 4 times bigger than the MSE of a forecast based on historical volatility. We test and reject the hypothesis that a GARCH(1,1) process is the true data generating process of the longer sample of returns of the S&P 500 stock market index between March 4, 1957 and October 9, 2003. We investigate then the alternative use of the GARCH(1,1) process as a local, stationary approximation of the data and find that the GARCH(1,1) model fails during significantly long periods to provide a good local description to the time series of returns on the S&P 500 and Dow Jones Industrial Average indexes. Since the estimated coefficients of the GARCH model change significantly through time, it is not clear how the GARCH(1,1) model can be used for volatility forecasting over longer horizons. A comparison between the GARCH(1,1) volatility forecasts and a simple approach based on historical volatility questions the relevance of the GARCH(1,1) dynamics for longer horizon volatility forecasting for both the S&P 500 and Dow Jones Industrial Average indexes.stock returns, volatility, Garch(1,1), non-stationarities, unconditional time-varying volatility, IGARCH effect, longer-horizon forecasts
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Long range dependence effects and ARCH modelling
Full text linkOur study supports the hypothesis of global non-stationarity of the return time series. We bring forth both theoretical and empirical evidence that the long range dependence (LRD) type behavior of the sample ACF and the periodogram of absolute return series and the IGARCH effect documented in the econometrics literature could be due to the impact of non-stationarity on statistical instruments and estimation procedures. In particular, contrary to the common-hold belief that the LRD characteristic and the IGARCH phenomena carry meaningful information about the price generating process, these so-called stylized facts could be just artifacts due to structural changes in the data. The effect that the switch to a different regime has on the sample ACF and the periodogram is theoretically explained and empirically documented using time series that were the object of LRD modeling efforts (S&P500, DEM/USD FX) in various publications.sample autocorrelation, change point, GARCH process, long range dependence.
Non-stationarities in stock returns
Full text linkThe paper outlines a methodology for analyzing daily stock returns that relinquishes the assumption of global stationarity. Giving up this common working hypothesis reflects our belief that fundamental features of the financial markets are continuously and significantly changing. Our approach approximates locally the non-stationary data by stationary models. The methodology is applied to the S&P 500 series of returns covering a period of over seventy years of market activity. We find most of the dynamics of this time series to be concentrated in shifts of the unconditional variance. The forecasts based on our non-stationary unconditional modeling were found to be superior to those obtained in a stationary long memory framework or to those based on a stationary Garch(1,1) data generating process.stock returns, non-stationarities, locally stationary processes, volatility, sample autocorrelation, long range dependence, Garch(1,1) data generating process.
Code for: Identifying the Relationship Between Earnings and Prices
No full textThe files contain code for R, giving possibility to apply the method presented in the article: Starica, C., & Marton, J. (2024). Identifying the relationship between earnings and prices. The Accounting Review (forthcoming).Filerna innehåller kod för R, och ger möjlighet att tillämpa metoden som presenteras i artikeln: Starica, C., & Marton, J. (2024). Identifying the relationship between earnings and prices. The Accounting Review (forthcoming)
Asymptotic Behavior of Hill's Estimator for Autoregressive Data
No full textConsider a stationary, pth order autoregression fXn g satisfying Xn = p X i=1 OE i Xn\Gammai + Zn ; n = 0; \Sigma1; \Sigma2; : : : whose innovation sequence fZn g is iid with regularly varying tail probabilities of index \Gammaff. From observations X 1 ; : : : ; Xn , one may estimate ff \Gamma1 by applying Hill's estimator to X 1 ; : : : ; Xn . Alternatively, a second procedure is to use X 1 ; : : : ; Xn to get estimates OE 1 ; : : : ; OE p of the autoregressive coefficients and then to estimate the residuals by Z t (n) = X t \Gamma p X i=1 OE i X t\Gammai ; t = p + 1; : : : ; n; and then to apply Hill's estimator to the estimated residuals. We show that from the point of asymptotic variance, the second procedure is superior. 1 Introduction. Sets of data displaying large values with high probabilities are commonly encountered in fields such as finance, hydrology, reliability and teletraffic engineering. For these fields estimating the tail probability P(X ? x) of a rando..
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