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. We offer an unified approach based on the Cholesky decomposition of the covariance matrix which makes these problems exactly solvable in an efficient way. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

This thesis examines the relationship between the distribution of structural breaks within a data sample and the estimated parameter of long memory. We use Monte Carlo simulations to generate data from processes with specific values of parameters. Subsequently we join the data with various shifts to mean and examine how the estimates of the parameters vary from their true values. We have discovered that the overestimate of the long memory parameter is higher when the breaks are clustered together. It further increases when the signs of the shifts are positively correlated within the clusters while negative correlation reduces the bias. Our findings enable the improvement of robustness of estimators against the presence structural breaks. Powered by TCPDF (www.tcpdf.org)

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Keliami uždaviniai: GARCH modelių klasės taikymas ilgo periodo finansiniams duomenims: modelių parametrų paieška, jų vertinimas, testavimas ir taikymas. Ilga atmintis sąlyginiame variantiškume yra viena iš empirinių savybių, kurią turi daugelis finansinių laiko eilučių. Viena modelių klasė, kuri atvaizduoja šį elgesį yra vadinama Dalinai Integruotu GARCH (Baillie, Bollerslev ir Mikkelsen 1996). Dalinės integracijos idėją pateikė ir ją pritaikė GARCH struktūrai Granger (1980) ir Hosking (1981). Šiame darbe bus surastos analitinės FIGARCH proceso antros eilės logaritminės tikėtinumo funkcijos išvestinės. Ilgo diapazono priklausomybė bus apskaičiuota parametriniu dalinai integruotu GARCH modeliu. Finansinių laiko eilučių duomenys bus įvertinti GARCH (CGARCH(1), CGARCH(2)) ir FIGARCH(1,d,1)) modeliais maksimalaus tikėtinumo metodu. Taip pat bus sukurtas NASDAQ- NYSE santykinio stiprumo indikatorius bei patikrintos jo panaudojimo sąlygos. Iiustracija yra pateikta 5 akcijų indeksais, 2 valiutų santykiais, aukso bei NNSS duomenims. / The paper deals with the problems of applying GARCH model/framework to a long term financial data, the search of the models, their evaluation, testing/validation and application. Long memory in conditional variance is one of the empirical features exhibited by many financial time series. One class of models that was suggested to capture this behavior is the so-called Fractionally Integrated GARCH (Baillie, Bollerslev and Mikkelsen 1996) in which the ideas of fractional integration originally introduced by Granger (1980) and Hosking (1981) for processes of the mean are applied to GARCH framework. In this paper we derive analytic expressions for the second-order derivatives of the log-likelihood function of FIGARCH processes. Long-range dependence is assessed through the parametric fractionally integrated GARCH model. Financial time series data will be estimated Component GARCH (CGARCH(1), CGARCH(2)) and FIGARCH models maximum likelihood method. Also we built NASDAQ- NYSE relative strength indicator and tested its usage conditions. An illustration is provided on 2 exchange rate, 5 stock index, gold and NNSS data.

Šiame darbe atliekami elektros energijos kainų analizė ir modeliavimas. Elektros kainų kitimui ir tokioms jų charakteringoms savybėms, kaip sezoniškumas, vidurkio reversija, darbo dienų, savaitgalio ir švenčių efektas, kintamumo klasterizacija, aprašyti taikomi SARIMA-TGARCH ir SARFIMA-TGARCH modeliai. Tyrimui naudojami kasvalandiniai Prancūzijos elektros energijos biržos kainų stebėjimai. Darbą sudaro dvi dalys – bendroji (teorinė) ir tiriamoji dalys. Pirmoje dalyje apžvelgiama literatūra bei aptariami teoriniai modelių aspektai: aprašomi ilgos atminties modeliai. Antroje dalyje pristatomi modelių empiriniai rezultatai: SARIMA-TGARCH ir SARFIMA-TGARCH modelių taikymas ir adekvatumo tikrinimas. / In this paper an econometric modelling and forecasting of electricity spot prices is presented. The aim of this work is to examine SARIMA-TGARCH and SARFIMA-TGARCH models for describing volatility of electricity spot prices and their characteristics such as season, mean reversion, volatility cauterization, and effects of workdays, weekends or holidays. The data of France electricity stock prices are used for analysis. This paper contains two parts – theoretical and empirical. In the first part the short review of literature is presented. Moreover, the theoretical aspects of long memory models are discussed. In the following part the empirical results are presented: application and adequacy examination of SARIMA-TGARCH and SARFIMA-TGARCH models.

We analyze by simulation the properties of two time domain and two frequency domain estimators for low order autoregressive fractionally integrated moving average Gaussian models, ARFIMA (p,d,q). The estimators considered are the exact maximum likelihood for demeaned data, EML, the associated modified profile likelihood, MPL, and the Whittle estimator with, WLT, and without tapered data, WL. Length of the series is 100. The estimators are compared in terms of pile-up effect, mean square error, bias, and empirical confidence level. The tapered version of the Whittle likelihood turns out to be a reliable estimator for ARMA and ARFIMA models. Its small losses in performance in case of ``well-behaved" models are compensated sufficiently in more ``difficult" models. The modified profile likelihood is an alternative to the WLT but is computationally more demanding. It is either equivalent to the EML or more favorable than the EML. For fractionally integrated models, particularly, it dominates clearly the EML. The WL has serious deficiencies for large ranges of parameters, and so cannot be recommended in general. The EML, on the other hand, should only be used with care for fractionally integrated models due to its potential large negative bias of the fractional integration parameter. In general, one should proceed with caution for ARMA(1,1) models with almost canceling roots, and, in particular, in case of the EML and the MPL for inference in the vicinity of a moving average root of +1. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

This dissertation contains five essays in the field of time series econometrics. The main issue discussed is the lack of coherence between small sample and asymptotic inference. Frequently, in modern econometrics distributional results are strictly only valid for a hypothetical infinite sample. Studies show that the attained actual level of a test may be considerable different from the nominal significance level, and as a concequence, too many true null hypotheses will falsely be rejected. This leads, in the extension, to applied users that too often reject evidence in the data for theoretical predictions. In large, the thesis discusses how computer intensive methods may be used to adjust the test distribution, such that the actual significance level will coincide with the desired nominal level. The first two essays focus on how to improve testing for persistence in data, through a bootstrap procedure within a univariate framework. The remaining three essays are studies of multivariate time series models. The third essay considers the identification problem of the basic stationary vector autoregressive model, which is also the basic-line econometric specification for maximum likelihood cointegration analysis. In the fourth essay the multivariate framework is expanded to allow for components of different integrating order and in this setting the paper discusses how fractional cointegration affects the inference in maximum likelihood cointegration analysis. The fifth essay consider once again the bootstrap testing approach, now in a multivariate application, to correct inference on long-run relations in maximum likelihood cointegration analysis. / Diss. Stockholm : Handelshögsk.

The first chapter considers the asymptotic validity of bootstrap methods in a linear trend model with a change in slope at an unknown time. Perron and Zhu (2005) analyzed the consistency, rate of convergence, and limiting distributions of the parameter estimates in this model. I provide theoretical results for the asymptotic validity of bootstrap methods related to forming confidence intervals for the break date. I consider two bootstrap schemes, the residual (for white noise errors) and the sieve bootstrap (for correlated errors). Simulation experiments confirm that confidence intervals obtained using bootstrap methods perform well in terms of exact coverage rate. The second chapter extends Perron and Zhu's (2005) analysis to cover more general fractionally integrated errors with memory parameter d in the interval (-0.5,1.5). My theoretical results uncover some interesting features. For example, with a concurrent level shift allowed, the rate of convergence of the estimate of the break date is the same for all values of d in the interval (-0.5,0.5), a feature linked to the contamination induced by allowing a level shift. In all other cases, the rate of convergence is decreasing as d increases. I also provide results about the spurious break issue. The third chapter considers constructing confidence intervals for the break date in linear regressions. I compare the performance of various procedures in terms of the exact coverage rates and lengths: Bai's (1997) based on the asymptotic distribution with shrinking shifts, Elliott and Müller's (EM) (2007) based on inverting a test locally invariant to the magnitude of the change, Eo and Morley's (2013) based on inverting a likelihood ratio test, and various bootstrap procedures. In terms of coverage rates, EM's approach is the best but with a high cost in terms of length. With serially correlated errors and a change in intercept or in the coefficient of a regressor with a high signal-to-noise ratio, or when a lagged dependent variable is present, the length approaches the whole sample as the magnitude of the change increases. This drawback is not present for the other methods. Theoretical results are provided to explain the drawbacks of EM's method.

The paper deals with a three variable analogues of certain fractional integral operators introduced by M. Saigo. Resides giving three variable analogues of earlier known fractional integral operators of one variable as a special cases of newly defined operators, the paper establishes certain results in the form of theorems including integration by parts.

Os modelos ARFIMA de memória longa mostraram-se nesse trabalho mais versáteis à análise da persistência em séries temporais em comparação aos modelos ARIMA. As funções impulso-resposta dos modelos de integração fracionária indicam que essa classe de modelos capta mais adequadamente as informações contidas nas baixas freqüências das séries e, portanto, estes modelos são mais capacitados para avaliar como os choques econômicos são acomodados no médio e longo prazo. Os estudos simulatórios mostraram que os testes de raiz unitária aplicados a processos com memória longa possuem baixo poder, e que os estimadores por máxima verossimilhança e os baseados no espectro de ondaletas são eficientes para estimar o parâmetro de integração fracionária. Os estudos empíricos encontraram componentes altamente persistentes nas séries brasileiras do produto, desemprego e consumo. A análise de co-integração fracionária refutou os resultados do arcabouço I(1)-I(0) que sugerem a não co-integração entre as séries consumo das famílias e renda disponível. A variabilidade relativa dessas séries foi analisada por meio da análise em multiresolução de ondaletas. Concluiu-se que, nas baixas escalas, a variabilidade entre as séries varia em função da escala temporal envolvida. A doutrina da paridade do poder de compra com dados brasileiros foi revisitada por meio da análise de co-integração fracionária. / The long-memory ARFIMA models proved to be more versatile in this study to the analysis of endurance in time series compare to the ARIMA models. The impulse-response functions of the fractionally integrated models indicate that this class of models more adequately gathers the data enclosed in the low frequencies of the series and thus these models are more befitted to evaluate how economic shocks are settled in the medium and long terms. Simulation studies unveiled that the unit root tests applied to long-memory processes have low power, and that the maximum likelihood estimators as well as those based on wavelet spectrum are efficient in estimating the fractional difference parameter. Empirical studies have found highly persistent components in the Brazilian series of the product, unemployment and consumption. The fractional co-integration analysis rebutted the results of the I(1)-I(0) framework, which suggest the non co-integration between the series of families\' consumption and the disposable income. The relative variability of these series was investigated through a wavelet multiresolution analysis. It was concluded that, in small scales, the variability between the series changes according to the time scale involved. The Purchasing Power Parity doctrine with Brazilian data has been revisited through the fractional co-integration analysis.