nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒12‒11
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis By Dat Thanh Tran; Alexandros Iosifidis; Juho Kanniainen; Moncef Gabbouj
  2. A Neural Stochastic Volatility Model By Rui Luo; Weinan Zhang; Xiaojun Xu; Jun Wang
  3. Testing the lag length of vector autoregressive models: A power comparison between portmanteau and Lagrange multiplier tests By Raja Ben Hajria; Salah Khardani; Hamdi Raïssi
  4. Volatility Spillovers across Global Asset Classes: Evidence from Time and Frequency Domains By Aviral Kumar Tiwari; Juncal Cunado; Rangan Gupta; Mark E. Wohar

  1. By: Dat Thanh Tran; Alexandros Iosifidis; Juho Kanniainen; Moncef Gabbouj
    Abstract: Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale Limit Order Book (LOB) dataset show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations.
    Date: 2017–12
  2. By: Rui Luo; Weinan Zhang; Xiaojun Xu; Jun Wang
    Abstract: In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms stronge baseline methods, including the deterministic models, such as GARCH and its variants, and the stochastic MCMC-based models, and the Gaussian-process-based, on the average negative log-likelihood measure.
    Date: 2017–11
  3. By: Raja Ben Hajria; Salah Khardani; Hamdi Raïssi
    Abstract: In this paper we provide an asymptotic theoretical power comparison in the Bahadur sense, between the portmanteau and Breusch-Godfrey Lagrange Multiplier (LM) tests for the goodness-of-fit checking of vector autoregressive (VAR) models. The merits and the drawbacks of the studied tests are illustrated using Monte Carlo experiments.
    Keywords: VAR model, VECM model, Cointegration, Residual autocorrelations, Portmanteau tests, Lagrange Multiplier tests.
    JEL: C22 C01
    Date: 2017–11
  4. By: Aviral Kumar Tiwari (Center for Energy and Sustainable Development (CESD), Montpellier Business School, Montpellier, France); Juncal Cunado (University of Navarra, School of Economics, Edificio Amigos, Pamplona, Spain); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha USA, and School of Business and Economics, Loughborough University)
    Abstract: This paper analyzes the volatility spillovers across four global asset classes namely, stock, sovereign bonds, credit default swaps (CDS) and currency from September 2009 to September 2016, using both a time-domain and a frequency-domain framework. When the Diebold and Yilmaz (2012) methodology is applied, the estimated total connectedness index is 3.67%, suggesting a low level of connection among the four markets. Furthermore, the results show that the stock and CDS markets are net transmitters of volatility, while foreign exchange and bond markets are net receivers of the spillovers. When the Barunik and Krehlik (2017) frequency-domain analysis is carried out, the results indicate, first, that at higher frequencies, the degree of connectedness increases, and, second, that the stock market becomes the only net transmitter of volatility spillovers across the markets.
    Keywords: Volatility Spillovers, Financial Markets
    JEL: C32 E44 G10 G11
    Date: 2017–12

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