nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒11‒07
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. Extremal dependence tests for contagion By Renée Fry-McKibbin; Cody Yu-Ling Hsiao
  2. Using low frequency information for predicting high frequency variables By Claudia Foroni; Pierre Guérin; Massimiliano Marcellino
  3. With string model to time series forecasting By Richard Pin\v{c}\'ak; Erik Barto\v{s}

  1. By: Renée Fry-McKibbin; Cody Yu-Ling Hsiao
    Abstract: A new test for financial market contagion based on changes in extremal dependence defined as co-kurtosis and co-volatility is developed to identify the propagation mechanism of shocks across international financial markets. The proposed approach captures changes in various aspects of the asset return relationships such as cross-market mean and skewness (co-kurtosis) as well as cross-market volatilities (co-volatility). Monte Carlo experiments show that the tests perform well except for when crisis periods are short in duration. Small crisis sample critical values are calculated for use in this case. In an empirical application involving the global financial crisis of 2008-09, the results show that significant contagion effects are widespread from the US banking sector to global equity markets and banking sectors through either the co-kurtosis or the co-volatility channels, reinforcing that higher order moments matter during crises.
    Keywords: Co-skewness, Co-kurtosis, Co-volatility, Contagion testing, Extremal dependence, Financial crisis, Lagrange multiplier tests.
    JEL: C12 F30 G11 G21
    Date: 2015–11
  2. By: Claudia Foroni (Norges Bank); Pierre Guérin (Bank of Canada); Massimiliano Marcellino (Bocconi University, IGIER and CEPR)
    Abstract: We analyze how to incorporate low frequency information in models for predicting high frequency variables. In doing so, we introduce a new model, the reverse unrestricted MIDAS (RU-MIDAS), which has a periodic structure but can be estimated by simple least squares methods and used to produce forecasts of high frequency variables that also incorporate low frequency information. We compare this model with two versions of the mixed frequency VAR, which so far had been only applied to study the reverse problem, that is, using the high frequency information for predicting low frequency variables. We then implement a simulation study to evaluate the relative forecasting ability of the alternative models in finite samples. Finally, we conduct several empirical applications to assess the relevance of quarterly survey data for forecasting a set of monthly macroeconomic indicators. Overall, it turns out that low frequency information is important, particularly so when it is just released.
    Keywords: Mixed-Frequency VAR models, temporal aggregation, MIDAS models
    JEL: E37 C53
    Date: 2015–10–29
  3. By: Richard Pin\v{c}\'ak; Erik Barto\v{s}
    Abstract: Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial market. We utilize the projections of the real exchange rate dynamics onto the string-like topology in the OANDA market. The latter approach allows us to build the stable prediction models in trading in the financial forex market. The real application of the multi-string structures is provided to demonstrate our ideas for the solution of the problem of the robust portfolio selection. The comparison with the trend following strategies was performed, the stability of the algorithm on the transaction costs for long trade periods was confirmed.
    Date: 2015–11

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