Locally Adaptive Factor Processes for Multivariate Time Series
Authors: Daniele Durante, Bruno Scarpa, David B. Dunson
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance is assessed in simulations and illustrated in a financial application. In Section 4 we compare our model to BCR and to some of the most widely used models for multivariate stochastic volatility, through simulation studies. Finally in Section 5 an application to National Stock Market Indices across countries is examined. |
| Researcher Affiliation | Academia | Daniele Durante EMAIL Bruno Scarpa EMAIL Department of Statistical Sciences University of Padua Padua 35121, Italy David B. Dunson EMAIL Department of Statistical Science Duke University Durham, NC 27708-0251, USA |
| Pseudocode | Yes | Appendix A. Posterior Computation For a fixed truncation level L and a latent factor dimension K the detailed steps of the Gibbs sampler for posterior computations are: 1. Define the vector of the latent states and the error terms in the state space equation... |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code, nor does it provide a link to a code repository. Code is not mentioned as being available in supplementary materials or upon request. |
| Open Datasets | Yes | In this application we focus our attention on the multivariate weekly time series of the main 33 (i.e. p = 33) National Stock Market Indices from 12/07/2004 to 25/06/2012. Figure 5 shows the main features in terms of stationarity, mean patterns and volatility of two selected National Stock Market Indices downloaded from http://finance.yahoo. com/. |
| Dataset Splits | Yes | To analyze the performance of the online updating algorithm in LAF model, we simulate 50 new observations {yi}150 i=101 with ti 2 T o = {101, . . . , 150}, considering the same and 0 used in the generating mechanism for the first simulated data set and taking the 50 subsequent observations of the bumps functions for the dictionary elements { (ti)}150 i=101; finally the additional latent mean dictionary elements { (ti)}150 i=101 are simulated as before maintaining the continuity with the previously simulated functions { (ti)}100 i=1. According to the algorithm described in Subsection 3.3, we apply the online updating algorithm presented in Subsection 3.3, to the new set of weekly observations {yi}422 i=416 from 02/07/2012 to 13/08/2012 conditioning on posterior estimates of the Gibbs sampler based on observations {yi}415 i=1 available up to 25/06/2012. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running its experiments or simulations. |
| Software Dependencies | No | The paper refers to statistical models and algorithms like GARCH(1,1), PC-GARCH, GO-GARCH, and DCC-GARCH, but it does not specify the software libraries or packages used to implement them, nor does it provide any version numbers for any software. |
| Experiment Setup | Yes | Posterior computation for LAF is performed by using truncation levels L = K = 2 (at higher level settings we found that the shrinkage prior on results in posterior samples of the elements in the additional columns concentrated around 0). We place a Ga(1, 0.1) prior on the precision parameters σ 2 j and choose a1 = a2 = 2. As regards the n GP prior for each dictionary element lk(t) with l = 1, . . . , L and k = 1, . . . , K , we choose di use but proper priors for the initial values by setting σ2 11 = 1000 and place an Inv Ga(2, 108) prior on each σ2 Alk in order to allow less smooth behavior according to a previous graphical analysis of (ti) estimated via EWMA. Similarly we set σ2 k = 100 in the prior for the initial values of the latent state equations resulting from the n GP prior for k(t), and consider a = a B = b = b B = 0.005 to balance the rough behavior induced on the nonparametric mean functions by the settings of the n GP prior on lk(t), as suggested from previous graphical analysis. Note also that for posterior computation, we first scale the predictor space to (0, 1], leading to δi = 1/100, for i = 1, . . . , 100. For inference in BCR we consider the same previous hyperparameters setting for and 0 priors as well as the same truncation levels K and L , while the length scale in GP prior for lk(t) and k(t) has been set to 10 using the data-driven heuristic outlined in Fox and Dunson (2011). In both cases we run 50,000 Gibbs iterations discarding the first 20,000 as burn-in and thinning the chain every 5 samples. |