48,021 research outputs found
Long-Range Dependence in Financial Markets: a Moving Average Cluster Entropy Approach
A perspective is taken on the intangible complexity of economic and social
systems by investigating the underlying dynamical processes that produce, store
and transmit information in financial time series in terms of the
\textit{moving average cluster entropy}. An extensive analysis has evidenced
market and horizon dependence of the \textit{moving average cluster entropy} in
real world financial assets. The origin of the behavior is scrutinized by
applying the \textit{moving average cluster entropy} approach to long-range
correlated stochastic processes as the Autoregressive Fractionally Integrated
Moving Average (ARFIMA) and Fractional Brownian motion (FBM). To that end, an
extensive set of series is generated with a broad range of values of the Hurst
exponent and of the autoregressive, differencing and moving average
parameters . A systematic relation between \textit{moving average
cluster entropy}, \textit{Market Dynamic Index} and long-range correlation
parameters , is observed. This study shows that the characteristic
behaviour exhibited by the horizon dependence of the cluster entropy is related
to long-range positive correlation in financial markets. Specifically, long
range positively correlated ARFIMA processes with differencing parameter , and are consistent with
\textit{moving average cluster entropy} results obtained in time series of
DJIA, S\&P500 and NASDAQ
Building energy performance characterisation based on dynamic analysis and co-heating test
A demonstration zero-carbon neighborhood is being raised in the city of Kortrijk, Belgium in the framework of the ECO-Life project within the CONCERTO initiative. A holistic approach is applied to achieve the zero-carbon targets, considering all aspects that are relevant for energy supply. Accordingly, alongside the integration of renewable energy sources in the community, a low-temperature district heating system is being implemented to cover the heat demand. In this context, full scale testing of building thermal performances, by use of a co-heating test and flux measurements, can be useful to analyze the thermal performance of the building envelope in situ. For that reason, as part of a more general study regarding low-energy building, co-heating test, blower-door test and flux measurements in several apartments were executed. Therefore, the paper focuses on characterization of the thermal dynamic behavior of an apartment, as a first approximation of data analysis of a monitoring system involving whole buildings. In addition, in the present study, the capability of linear regression techniques to characterize the thermal behavior of a newly built low-energy apartment in Belgium is investigated. The strengths and weaknesses of different models are identified. The limitation and possibilities of regression models are evaluated in the face of their applicability as a simplified building equation model. The identified model structure is going to be used within a complex simulation model of an entire district heating system with around 200 dwelling. Finally, the potential of this kind of regression models to be used as part of the operational control scheme of a district heating system is presented
Discussion of âAn analysis of global warming in the Alpine region based on nonlinear nonstationary time series modelsâ by F. Battaglia and M. K. Protopapas
The annual temperatures recorded for the last two centuries in fifteen european stations around the Alps are analyzed. They show a global warming whose growth rate is not however constant in time. An analysis based on linear Arima models does not provide accurate results. Thus, we propose threshold nonlinear nonstationary models based on several regimes both in time and in levels. Such models fit all series satisfactorily, allow a closer description of the temperature changes evolution, and help to discover the essential differences in the behavior of the different stations
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Are property prices non-linear? An investigation of the behaviour of US REITs and UK property company shares
Linear models of market performance may be misspecified if the market is subdivided into distinct regimes exhibiting different behaviour. Price movements in the US Real Estate Investment Trusts and UK Property Companies Markets are explored using a Threshold Autoregressive (TAR) model with regimes defined by the real rate of interest. In both US and UK markets, distinctive behaviour emerges, with the TAR model offering better predictive power than a more conventional linear autoregressive model. The research points to the possibility of developing trading rules to exploit the systematically different behaviour across regimes
Bounded Influence Approaches to Constrained Mixed Vector Autoregressive Models
The proliferation of many clinical studies obtaining multiple biophysical signals from several individuals repeatedly in time is increasingly recognized, a recognition generating growth in statistical models that analyze cross-sectional time series data. In general, these statistical models try to answer two questions: (i) intra-individual dynamics of the response and its relation to some covariates; and, (ii) how this dynamics can be aggregated consistently in a group. In response to the first question, we propose a covariate-adjusted constrained Vector Autoregressive model, a technique similar to the STARMAX model (Stoffer, JASA 81, 762-772), to describe serial dependence of observations. In this way, the number of parameters to be estimated is kept minimal while offering flexibility for the model to explore higher order dependence. In response to (ii), we use mixed effects analysis that accommodates modelling of heterogeneity among cross-sections arising from covariate effects that vary from one cross-section to another. Although estimation of the model can proceed using standard maximum likelihood techniques, we believed it is advantageous to use bounded influence procedures in the modelling (such as choosing constraints) and parameter estimation so that the effects of outliers can be controlled. In particular, we use M-estimation with a redescending bounding function because its influence function is always bounded. Furthermore, assuming consistency, this influence function is useful to obtain the limiting distribution of the estimates. However, this distribution may not necessarily yield accurate inference in the presence of contamination as the actual asymptotic distribution might have wider tails. This led us to investigate bootstrap approximation techniques. A sampling scheme based on IID innovations is modified to accommodate the cross-sectional structure of the data. Then the M-estimation is applied to each bootstrap sample naively to obtain the asymptotic distribution of the estimates.We apply these strategies to the extracted BOLD activation from several regions of the brain from a group of individuals to describe joint dynamic behavior between these locations. We used simulated data with both innovation and additive outliers to test whether the estimation procedure is accurate despite contamination
A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market
We propose a dynamic network model where two mechanisms control the
probability of a link between two nodes: (i) the existence or absence of this
link in the past, and (ii) node-specific latent variables (dynamic fitnesses)
describing the propensity of each node to create links. Assuming a Markov
dynamics for both mechanisms, we propose an Expectation-Maximization algorithm
for model estimation and inference of the latent variables. The estimated
parameters and fitnesses can be used to forecast the presence of a link in the
future. We apply our methodology to the e-MID interbank network for which the
two linkage mechanisms are associated with two different trading behaviors in
the process of network formation, namely preferential trading and trading
driven by node-specific characteristics. The empirical results allow to
recognise preferential lending in the interbank market and indicate how a
method that does not account for time-varying network topologies tends to
overestimate preferential linkage.Comment: 19 pages, 6 figure
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