Combination Of Long Memory Research Articles

Combining Long and Short Memory in Time Series Models: the Role of Asymptotic Correlations of the [formula omitted

A major practical problem in the application of the long memory ARFIMA model has been distinguishing between the long memory and short memory components and subsequent estimation of the model. The asymptotic correlations between the Maximum Likelihood Estimators (MLE) of the long memory parameter, d, and the short memory parameters within an ARFIMA estimation are derived. The correlation in an ARFIMA(1,d,0) model can be as large as −0.95. However, MLE still works well in these high correlation cases; even for non stationary situations. Similarly, QMLE also performs well in simulations where the innovations have t or skewed t densities. MLE also performs very well for ARFIMA(3,d,0) models when there is a moderate level of persistence in the short memory part. However, MLE can perform extremely poorly when there is a combination of long memory and substantial persistence in the short memory component. Some of these points are illustrated in the analysis of some Realized Volatility time series which contain long memory. The correlation in “gap ARFIMA” models is comparatively low which indicates that MLE is quite stable in this case. Some suggestions and recommendations for applied work are provided.

Market efficiency in the art markets using a combination of long memory, fractal dimension, and approximate entropy measures

In this paper, we investigate the efficiency in the art markets, using a generalized spectral test (GST) in a rolling window approach to detect departure from the martingale difference hypothesis (MDH) and trace the periods of market efficiency over time. Then we complement our results using the approximate entropy, the rescaled range analysis, and fractal dimension. We combine the three measures in an Efficiency Index for each market. Applying these methods, we find that the Modern Art, Paintings, Post-war, Prints, the USA market, and the global market in Euro show the largest values for the Approximate Entropy. Using the rescaled range estimates, we find that all markets are characterized by persistent behavior and, then using the Efficiency Index, our results indicate overwhelming evidence of market inefficiency in almost all sectors. Finally, we support our findings with some explanation of the reasons behind market inefficiency, related to asymmetrical information, influential galleries power, and differentiated pieces and talents in the art markets.

Realized Volatility Forecasting of Agricultural Commodity Futures Using Long Memory and Regime Switching

AbstractWe investigate the dynamic properties of the realized volatility of five agricultural commodity futures by employing the high‐frequency data from Chinese markets and find that the realized volatility exhibits both long memory and regime switching. To capture these properties simultaneously, we utilize a Markov switching autoregressive fractionally integrated moving average (MS‐ARFIMA) model to forecast the realized volatility by combining the long memory process with regime switching component, and compare its forecast performances with the competing models at various horizons. The full‐sample estimation results show that the dynamics of the realized volatility of agricultural commodity futures are characterized by two levels of long memory: one associated with the low‐volatility regime and the other with the high‐volatility regime, and the probability to stay in the low‐volatility regime is higher than that in the high‐volatility regime. The out‐of‐sample volatility forecast results show that the combination of long memory with switching regimes improves the performance of realized volatility forecast, and the proposed model represents a superior out‐of‐sample realized volatility forecast to the competing models. Copyright © 2016 John Wiley & Sons, Ltd.

Joint Tests for Non-linearity and Long Memory: The Case of Purchasing Power Parity

A pervasive finding of unit roots in macroeconomic data often runs counter to intuition regarding the stochastic nature of the process under consideration. Two econometric techniques have been utilized in an attempt to resolve the finding of unit roots, namely long memory and models that depart from linearity. While the use of long memory and stochastic regime switching models have developed almost independently of each other, it is now clear that the two modeling techniques can be intimately linked. In particular, both modeling techniques have been used in isolation to study the dynamics of the real exchange rate. To determine the importance of each technique in this context, I employ a testing and estimation procedure that allows one to jointly test for long memory and non-linearity (regime switching behavior) of the STAR variety. I find that there is substantial evidence of non-linear behavior for the real exchange rate for many developing and European countries, with little evidence for ESTAR non-linearity for countries outside the European continent including Japan and Canada. In cases where non-linearity is found, I also find significant evidence of long memory for the majority of the countries in my sample. Thus, long memory and non-linearity can also be viewed as compliments rather than substitutes. The linear model in isolation appears to be inadequate for breaking down the paradox known as the PPP puzzle. On the other hand, a combination of long memory and non-linearity may be a promising research avenue for pursuing an answer to the paradox.