Is stochastic volatility more flexible than garch?

This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical properties usually observed in high frequency financial time series: high kurtosis, small first order autocorrelation of squared observations and slow decay towards zero of the autocorrelation coefficients of squared observations. We show that the ARSV(1) model is more flexible than the GARCH(1,1) model in the sense that it is able to generate series with higher kurtosis and smaller first order autocorrelation of squares for a wider variety of parameter specifications. Our results may help to clarify some puzzles raised in the empirical analysis of real financial time series

Detecting level shifts in the presence of conditional heteroscedasticity.

The objective of this paper is to analyze the finite sample performance of two variants of the likelihood ratio test for detecting a level shift in uncorrelated conditionally heteroscedastic time series. We show that the behavior of the likelihood ratio test is not appropriate in this context whereas if the test statistic is appropriately standardized, it works better. We also compare two alternative procedures for testing for several level shifts. The results are illustrated by analyzing daily returns of exchange rates

DETECTING LEVEL SHIFTS IN THE PRESENCE OF CONDITIONAL HETEROSCEDASTICITY

The objective of this paper is to analyze the finite sample performance of two variants of the likelihood ratio test for detecting a level shift in uncorrelated conditionally heteroscedastic time series. We show that the behavior of the likelihood ratio test is not appropriate in this context whereas if the test statistic is appropriately standardized, it works better. We also compare two alternative procedures for testing for several level shifts. The results are illustrated by analyzing daily returns of exchange rates.EGARCH, GARCH, Likelihood Ratio, Stochastic Volatility.

Estimating and Forecasting GARCH Volatility in the Presence of Outiers

The main goal when fitting GARCH models to conditionally heteroscedastic time series is to estimate the underlying volatilities. It is well known that outliers affect the estimation of the GARCH parameters. However, little is known about their effects when estimating volatilities. In this paper, we show that when estimating the volatility by using Maximum Likelihood estimates of the parameters, the biases incurred can be very large even if estimated parameters have small biases. Consequently, we propose to use robust procedures. In particular, a simple robust estimator of the parameters is proposed and shown that its properties are comparable with other more complicated ones available in the literature. The properties of the estimated and predicted volatilities obtained by using robust filters based on robust parameter estimates are analyzed. All the results are illustrated using daily S&P500 and IBEX35 returns.Heteroscedasticity, M-estimator, QML estimator, Robustness, Financial Markets

SPURIOUS AND HIDDEN VOLATILITY

This paper analyzes the effects caused by outliers on the identification and estimation of GARCH models. We show that outliers can lead to detect spurious conditional heteroscedasticity and can also hide genuine ARCH effects. First, we derive the asymptotic biases caused by outliers on the sample autocorrelations of squared observations and their effects on some homoscedasticity tests. Then, we obtain the asymptotic biases of the OLS estimates of ARCH(p) models and analyze their finite sample behaviour by means of extensive Monte Carlo experiments. The finite sample results are extended to GLS and ML estimates ARCH(p) and GARCH(1,1) models.GARCH, Outliers, Heteroscedasticity

Modeling the live-pig trade network in Georgia: Implications for disease prevention and control.

Live pig trade patterns, drivers and characteristics, particularly in backyard predominant systems, remain largely unexplored despite their important contribution to the spread of infectious diseases in the swine industry. A better understanding of the pig trade dynamics can inform the implementation of risk-based and more cost-effective prevention and control programs for swine diseases. In this study, a semi-structured questionnaire elaborated by FAO and implemented to 487 farmers was used to collect data regarding basic characteristics about pig demographics and live-pig trade among villages in the country of Georgia, where very scarce information is available. Social network analysis and exponential random graph models were used to better understand the structure, contact patterns and main drivers for pig trade in the country. Results indicate relatively infrequent (a total of 599 shipments in one year) and geographically localized (median Euclidean distance between shipments = 6.08 km; IQR = 0-13.88 km) pig movements in the studied regions. The main factors contributing to live-pig trade movements among villages were being from the same region (i.e., local trade), usage of a middleman or a live animal market to trade live pigs by at least one farmer in the village, and having a large number of pig farmers in the village. The identified villages' characteristics and structural network properties could be used to inform the design of more cost-effective surveillance systems in a country which pig industry was recently devastated by African swine fever epidemics and where backyard production systems are predominant

DETECTING LEVEL SHIFTS IN THE PRESENCE OF CONDITIONAL HETEROSCEDASTICITY.

The objective of this paper is to analyze the finite sample performance of two variants of the likelihood ratio test for detecting a level shift in uncorrelated conditionally heteroscedastic time series. We show that the behavior of the likelihood ratio test is not appropriate in this context whereas if the test statistic is appropriately standardized, it works better. We also compare two alternative procedures for testing for several level shifts. The results are illustrated by analyzing daily returns of exchange rates.

IT integration, operations flexibility and performance: an empirical study

Purpose: This study examines the relationship between IT implementation and performance with manufacturing flexibility based on a sample drawn from a set of manufacturing firms. Design/methodology/approach: The relationships were analyzed using structural equations modelling (SEM) using EQS 6.2 software. Previously, an explanatory factor analysis confirmed one-dimensionality of the scales, Cronbach’s alpha was calculated to evaluate its internal consistency and a confirmatory factor analysis was run to observe scales’ validity. Findings: This research proves a significant positive and direct effect of IT implementation on operations performance with 4 out of 6 flexibility dimensions (Machine, Labour, Material handling and Volume). Mix and Routing flexibility dimensions show no significant impact on firm performance. Research limitations/implications: It is necessary to be cautious when generalizing this findings these findings, as service firms were not part of the sample even when statistical results prove robustness suggesting that the findings are quite reliable. Some flexibility dimensions show no significant impact in performance (Routing and Mix flexibility). This is consistent with the fact that these flexibility dimensions act as variability absorbers within the manufacturing process. Future research lines: Future studies can focus on determining further internal and environmental factors that affect operations flexibility according to specific sector characteristics. Originality/value: This research proves a significant positive and direct effect of IT implementation on operations performance. Results show not only the links between IT implementation and operations performance, but also the magnitude of every impact. The model considers IT integration as the degree of alignment that existing technology resources in a firm have with the business strategy, in terms of importance and support for this strategyPeer Reviewe