nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒05‒16
four papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Forecasting risk measures based on structural breaks in the correlation matrix By Duan, Fang
  2. Variational Heteroscedastic Volatility Model By Zexuan Yin; Paolo Barucca
  3. A multivariate GARCH model with an infinite hidden Markov mixture By Li, Chenxing
  4. Asymptotic Theory for Moderate Deviations from the Unit Boundary in Quantile Autoregressive Time Series By Christis Katsouris

  1. By: Duan, Fang
    Abstract: Correlation models, such as Constant Conditional Correlation (CCC) GARCH model or Dynamic Conditional Correlation (DCC) GARCH model, play a crucial role in forecasting Value-at-Risk (VaR) or Expected Shortfall (ES). The additional inclusion of constant correlation tests into correlation models has been proven to be helpful in terms of the improvement of the accuracy of VaR or ES forecasts. Galeano & Wied (2017) suggested an algorithms for detecting structural breaks in the correlation matrix whereas Duan & Wied (2018) proposed a residual based testing procedure for constant correlation matrix which allows for time-varying marginal variances. In this chapter, we demonstrate the application of aforementioned correlation testing procedures and compare its performance in backtesting VaR and ES predictions. Portfolios in consideration are constructed from four stock indices DAX30, STOXX50, FTSE100 and S&P500.
    Keywords: structural break tests,correlation model,value-at-risk,expected shortfall
    JEL: C12 C32 C53 C58
    Date: 2022
  2. By: Zexuan Yin; Paolo Barucca
    Abstract: We propose Variational Heteroscedastic Volatility Model (VHVM) -- an end-to-end neural network architecture capable of modelling heteroscedastic behaviour in multivariate financial time series. VHVM leverages recent advances in several areas of deep learning, namely sequential modelling and representation learning, to model complex temporal dynamics between different asset returns. At its core, VHVM consists of a variational autoencoder to capture relationships between assets, and a recurrent neural network to model the time-evolution of these dependencies. The outputs of VHVM are time-varying conditional volatilities in the form of covariance matrices. We demonstrate the effectiveness of VHVM against existing methods such as Generalised AutoRegressive Conditional Heteroscedasticity (GARCH) and Stochastic Volatility (SV) models on a wide range of multivariate foreign currency (FX) datasets.
    Date: 2022–04
  3. By: Li, Chenxing
    Abstract: This paper proposes a new Bayesian semiparametric model that combines a multivariate GARCH (MGARCH) component and an infinite hidden Markov model. The new model nonparametrically approximates both the shape of unknown returns distributions and their short-term evolution. It also captures the smooth trend of the second moment with the MGARCH component and the potential skewness, kurtosis, and volatility roughness with the Bayesian nonparametric component. The results show that this more-sophisticated econometric model not only has better out-of-sample density forecasts than benchmark models, but also provides positive economic gains for a CRRA investor at different risk-aversion levels when transaction costs are assumed. After considering the transaction costs, the proposed model dominates all benchmark models/portfolios when No Short-Selling or No Margin-Trading restriction is imposed.
    Keywords: Multivariate GARCH; IHMM; Bayesian nonparametric; Portfolio allocation; Transaction costs
    JEL: C11 C14 C32 C34 C53 C58
    Date: 2022–03–16
  4. By: Christis Katsouris
    Abstract: We establish the asymptotic theory in quantile autoregression when the model parameter is specified with respect to moderate deviations from the unit boundary of the form (1 + c / k) with a convergence sequence that diverges at a rate slower than the sample size n. Then, extending the framework proposed by Phillips and Magdalinos (2007), we consider the limit theory for the near-stationary and the near-explosive cases when the model is estimated with a conditional quantile specification function and model parameters are quantile-dependent. Additionally, a Bahadur-type representation and limiting distributions based on the M-estimators of the model parameters are derived. Specifically, we show that the serial correlation coefficient converges in distribution to a ratio of two independent random variables. Monte Carlo simulations illustrate the finite-sample performance of the estimation procedure under investigation.
    Date: 2022–04

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