Cont R (2005) Long range dependence in financial markets. In: Levy-Vehel J, Lutton E, editors, Fractals in Engineering, Springer London. pp. 159-179.R. Cont, Long range dependence in financial markets, in: J. L
8.4, there may be a connection between the global long-range dependence in the dmbp-data and the local dependency structure in the tails, but note that the estimates in Fig. 8.4 are based on the pseudo-normalized data, so the information from the marginal distribution is not directly present...
A wavelet-based approach to the analysis and modelling of financial time series exhibiting strong long-range dependence: the case of Southeast Europelong-range dependencewavelet transformmultiscale autoregressive (MAR) modelSoutheast European stock markets...
Deviations from the power-law with exponent 3/2 in the case of fBm signals about true long-range dependence. Certainly, we will see later, such test is not so straightforward as real processes such as volatility in the financial markets may be more complicated than one-dimensional stochastic ...
Time-varying jump risk premia in stock index futures returns The model allows for a dynamic jump component with autoregressive jump intensity, long-range dependence in volatility dynamics, and a volatility in mean ... Wing,Hong,Chan,... - 《Journal of Futures Markets》 被引量: 16发表: 2011...
identified in the cross-section of stock returns have become vital parts of modern investment products and financial models. Even though much has been learned about the properties of these portfolios in recent years, one issue still remains unaddressed. Are factor returns long-range dependent (LRD)...
scale-dependence in response is not scale-bound; what begins or dominates in the LLR range need not remain confined there. Cross-scale interactions allow for nuanced dynamics, wherein the non-Gaussian response observed in the LLR range may incorporate marginal contributions from SLR activity, while...
We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary objective is to uncover intricate relationships among different stock indices, leading to the extraction of stock networks. We examine time...
We also show that, with typical sample sizes, DFA is unable to disentangle long memory from short range dependence with characteristic time comparable to the whole sample length. Introduction This paper explores long memory properties of volatility of financial assets. A vast number of authors ([2...
Longevity securitization enables insurers to manage longevity or mortality risk in the life market. Recent empirical studies identify long-range dependence (LRD) in mortality rates using life tables, which casts doubt on the adequacy of Markovian models for actuarial pricing and risk management. This ...