A long-memory integer-valued time series model, INARFIMA, for financial application

Quoreshi, Shahiduzzaman

doi:10.1080/14697688.2012.711911

Cited by 18 publications

(21 citation statements)

References 29 publications

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“…Batten et al (2014) point out the importance of long-memory models in forecasting the high-frequency Value-at-Risk. The performance of long-memory models for stock return volatility is discussed in Quoreshi (2014). Apart from those areas, our proposed A-HYEGARCH model can be widely adopted for other fields related to financial volatility or risk management, such as portfolio optimization.…”

Section: Discussionmentioning

confidence: 99%

Modeling High Frequency Data with Long Memory and Structural Change: A-HYEGARCH Model

Shi

Yang

2018

Risks

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Abstract:In this paper, we propose an Adaptive Hyperbolic EGARCH (A-HYEGARCH) model to estimate the long memory of high frequency time series with potential structural breaks. Based on the original HYGARCH model, we use the logarithm transformation to ensure the positivity of conditional variance. The structural change is further allowed via a flexible time-dependent intercept in the conditional variance equation. To demonstrate its effectiveness, we perform a range of Monte Carlo studies considering various data generating processes with and without structural changes. Empirical testing of the A-HYEGARCH model is also conducted using high frequency returns of S&P 500, FTSE 100, ASX 200 and Nikkei 225. Our simulation and empirical evidence demonstrate that the proposed A-HYEGARCH model outperforms various competing specifications and can effectively control for structural breaks. Therefore, our model may provide more reliable estimates of long memory and could be a widely useful tool for modelling financial volatility in other contexts.

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Section: Discussionmentioning

confidence: 99%

Modeling High Frequency Data with Long Memory and Structural Change: A-HYEGARCH Model

Shi

Yang

2018

Risks

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“…Note Under the assumption that there exists some covariates that commonly influence

Y_{t}^{false[1 false]}

and

Y_{t}^{false[2 false]}

, that is,

{bold-italicx}_{t} = {[x_{t 1}, x_{t 2}, \dots, x_{italictj}, \dots, x_{italictp}]}^{⊤}

with p explanatory effects, then we assume the link predictor of the innovations

λ_{t}^{[k]} = \exp false(x_{t}^{⊤} β^{false[k false]} false)

, similar to Quoreshi (2006a,b), where

{bold-italicβ}^{[k]} = {[β_{1}^{[k]}, β_{2}^{[k]}, \dots, β_{j}^{[k]}, \dots, β_{p}^{[k]}]}^{⊤}

. Hence, the unknown parameters of the BINAR(1) model consist of the vector of regression parameters

{false[β^{false[1 false]}, β^{false[2 false]} false]}^{⊤}

and the set of correlation coefficients

{false[ρ_{11}, ρ_{12}, ρ_{21}, ρ_{22} false]}^{⊤}

.…”

Section: The Model Constructionmentioning

confidence: 99%

A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties

Bakouch

Sunecher

Khan

et al. 2020

Aus NZ J of Statistics

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This paper considers modelling of a non-stationary bivariate integer-valued autoregressive process of order 1 (BINAR(1)) where the cross-dependence between the counting series is formed through the relationship of the current series with the previous-lagged count series observations while the pair of innovations is independent and marginally Poisson. In addition, this paper proposes a generalised quasi-likelihood (GQL) estimating equation based on the exact specification of the mean score and the auto-covariance structure. The proposed approach is also compared with other popular techniques such as conditional maximum likelihood (CML), generalised least squares (GLS) and generalised method of moment (GMM) based on simulated data from the proposed BINAR(1). Moreover, the model is applied to weekly series of day and night road accidents arising in some regions of Mauritius and is compared with other existing BINAR(1) models.

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“…The model is called fractionally integrated moving average conditional heteroskedasticity (FIMACH). The model, designed for non-integer data, follows Quoreshi (2014). One important way the FIMACH model differs from the ARFIMA model class is that it can study the level series for the heteroskedasticity property.…”

Section: Introductionmentioning

confidence: 99%

Equity Market Contagion in Return Volatility during Euro Zone and Global Financial Crises: Evidence from FIMACH Model

Quoreshi

Uddin

Jienwatcharamongkhol

2019

JRFM

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The current paper studies equity markets for the contagion of squared index returns as a proxy for stock market volatility, which has not been studied earlier. The study examines squared stock index returns of equity in 35 markets, including the US, UK, Euro Zone and BRICS (Brazil, Russia, India, China and South Africa) countries, as a proxy for the measurement of volatility. Results from the conditional heteroskedasticity long memory model show the evidence of long memory in the squared stock returns of all 35 stock indices studied. Empirical findings show the evidence of contagion during the global financial crisis (GFC) and Euro Zone crisis (EZC). The intensity of contagion varies depending on its sources. This implies that the effects of shocks are not symmetric and may have led to some structural changes. The effect of contagion is also studied by decomposing the level series into explained and unexplained behaviors.

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A long-memory integer-valued time series model, INARFIMA, for financial application

Cited by 18 publications

References 29 publications

Modeling High Frequency Data with Long Memory and Structural Change: A-HYEGARCH Model

Modeling High Frequency Data with Long Memory and Structural Change: A-HYEGARCH Model

A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties

Equity Market Contagion in Return Volatility during Euro Zone and Global Financial Crises: Evidence from FIMACH Model

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