scholarly journals FORECASTING GLOBAL EQUITY INDICES USING LARGE BAYESIAN VARS

2016 ◽  
Vol 69 (3) ◽  
pp. 288-308 ◽  
Author(s):  
Florian Huber ◽  
Tamás Krisztin ◽  
Philipp Piribauer
Keyword(s):  
2012 ◽  
Author(s):  
Jan F. Baldeaux ◽  
Man Chung Fung ◽  
Katja Ignatieva ◽  
Eckhard Platen

2021 ◽  
Vol 13 (2) ◽  
pp. 676
Author(s):  
Ramiz ur Rehman ◽  
Muhammad Zain ul Abidin ◽  
Rizwan Ali ◽  
Safwan Mohd Nor ◽  
Muhammad Akram Naseem ◽  
...  

This study investigates the integration of environmental, social, and governance (ESG) equity indices with conventional indices in Brazil, Russia, India, China, and South Africa (BRICS) individually and across all BRICS countries to better understand regional economic cooperation. Accordingly, we look at daily returns from 13 July 2013 to 28 February 2018 for the Morgan Stanley Capital International (MSCI) ESG indices and MSCI composite indices of the respective countries. To analyze the integration between the ESG equity indices of the sampled countries with their regional and across regional conventional counterparts, the Johansen Co-integration test is employed in this study. Further, the vector error correction model (VECM) is applied to test the causality between the sampled time-series. The impulse response function analysis further explains the impulse responses of each country’s MSCI ESG returns to one standard deviation of innovations to MSCI composite returns of the same country and across countries. Finally, the extent of the MSCI composite returns’ impact on the MSCI ESG returns in the same country indices, and cross-regional indices is examined with variance decomposition analysis. The results suggest that all ESG equity indices are integrated with conventional indices in all BRICS countries. Furthermore, there is a short-or long-run causality between MSCI ESG and MSCI composite equity indices of China and South Africa. Moreover, the study finds only short-run causality between conventional and non-conventional equity indices of Brazil and Russia, whereas we find only long-run causality between India’s non-conventional and conventional equity indices. Finally, the study finds that the all-individual country MSCI ESG equity indices shows a long-run causality with MSCI composite equity indices of all other BRICS countries. The findings also confirm the economic and financial cooperation between the BRICS countries.


Author(s):  
Jan Prüser ◽  
Christoph Hanck

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.


2012 ◽  
Author(s):  
Yigit Atilgan ◽  
K. Ozgur Demirtas
Keyword(s):  

2021 ◽  
Author(s):  
Timothy Little

This thesis presents a time varying regime-switching model for US equity index daily returns. The parameters of the model are estimated recursively with the Kalman lter. We demonstrate our model and parameter estimation technique are effective by demonstrating improvements in model t compared to alternate models. Information from our model is used to build a Finite State Machine trading system with back-tested performance in excess of 15,000% above a buy and hold strategy for the DOW Jones Industrial average from 1928-2012. Similar results are found for both the S&P 500 index and the NASDAQ Composite index over a long period. Our model succeeds at identifying pro table investment opportunities and improving model t with a minimum of parameters.


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