Michael A. Hauser

Semiparametric and nonparametric testing for long memory: A Monte Carlo study

The finite sample properties of three semiparametric estimators, several versions of the modified rescaled range, MRR, and three versions of the GHURST estimator are investigated. Their power and size for testing for long memory under short-run effects, joint short and long-run effects, heteroscedasticity andt-distributions are given using Monte Carlo methods.The MRR with the Bartlett window is generally robust with the disadvantage of a relatively small power. The trimmed Whittle likelihood has high power in general and is robust except for large short-run effects.The tests are applied to changes in exchange rate series (daily data) of 6 major countries. The hypothesis of no fractional integration is rejected for none of the series.

Fractionally Integrated Models with ARCH Errors: With an Application to the Swiss One-Month Euromarket Interest Rate

We introduce ARFIMA-ARCH models, which simultaneously incorporate fractional differencing and conditional heteroskedasticity. We develop the likelihood function and we use it to construct the bias-corrected maximum (modified profile) likelihood estimator. Finite-sample properties of the estimation procedure are explored by Monte Carlo simulation. Backus and Zin (1993) have motivated the existence of fractional integration in interest rates by the persistence of the short rate and the variability of the long end of the yield curve. An empirical investigation of a daily one-month Swiss Euromarket interest rate finds a difference parameter of 0.72. This indicates non-stationary behavior. In contrast to first-order integrated models, the long-run cumulative response of shocks to the series is zero.

Modelling profit series: nonstationarity and long memory

The dynamic structure of profit rates for 156 US manufacturing companies is analysed by means of fractional integration techniques as an alternative to the commonly used ARIMA models with respect to the ‘persistence of profits’. Thereby the pseudo-spectral density aproach of Velasco and Robinson together with model selection criteria is applied. The results show–despite the short lengths of the series and tests for the integer degrees of integration (d = 0, 1)–that 35.5% of the series may be well-approximated by long-range dependent processes, and 54% are nonstationary. This is a confirmation of the strong challenge to the competitive environment hypothesis obtained by previous studies.

Modelling exchange rates: long-run dependence versus conditional heteroscedasticity

Indications for two different features not captured by low-order linear time-series models can be found in day-to-day changes of exchange rates: long memory and conditional heteroscedasticity. These characteristics have inspired the development of ARFIMA and GARCH models. By means of Monte Carlo simulation, it is demonstrated that either of the two features stands a non-negligible chance of being detected spuriously in the presence of the other. A table of explicit empirical small-sample quantiles for identification of long-memory structures in the presence of GARCH effects is included.

Estimation of the fractionally differencing parameter with the R/S method

Extensive computer experiments are performed to examine the finite sample behavior of the R/S analysis in case of long-term dependence. Our simulation study differs from former studies both in its design and in its size. In addition, we used a method for obtaining finite sequences of a long-term dependent process, which exactly reproduces the correlation structure of the underlying process. The results obtained show that the R/S analysis does not allow reliable estimation of the fractionally differencing parameter.

A wavelet Whittle estimator of generalized long-memory stochastic volatility

We consider a k-GARMA generalization of the long-memory stochastic volatility model, discuss the properties of the model and propose a wavelet-based Whittle estimator for its parameters. Its consistency is shown. Monte Carlo experiments show that the small sample properties are essentially indistinguishable from those of the Whittle estimator, but are favorable with respect to a wavelet-based approximate maximum likelihood estimator. An application is given for the Microsoft Corporation stock, modeling the intraday seasonal patterns of its realized volatility.

A Note on Generation, Estimation and Prediction of Stationary Processes

Some recently discussed stationary processes like fractionally integrated processes cannot be described by low order autoregressive or moving average (ARMA) models rendering the common algorithms for generation estimation and prediction partly very misleading [cf. Hosking(1981,1984), Sowell(1992), Ray(1993)]. We offer an unified approach based on the Cholesky decomposition of the covariance matrix which makes these problems exactly solvable in an efficient way.

Profit Persistence and Stock Returns

ABSTRACT This article attempts to assemble further empirical evidence on the relationship between the product and the financial market. Drawing back on work in industrial organization, we analyse the relationship between profit persistence and factor-adjusted stock returns looking at about 2000 listed US firms over the last 34 years. While the relationship between (current, lagged and unexpected) profits/earnings and returns has been extensively analysed before, to our knowledge this is the first study to look at the relationship between stock returns and profit persistence. We interpret profit persistence as a result of market competition and innovation of the firm. It is shown that firm-specific long-run profit persistence after correction for other additional economic fundamentals of the firm has a positive impact on four-factor adjusted returns and a negative impact on their volatility.