Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

Härdle, Wolfgang Karl; Hautsch, Nikolaus; Mihoci, Andrija

doi:10.1002/jae.2376

Cited by 38 publications

(16 citation statements)

References 25 publications

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“…In financial econometrics it is a challenging task to understand the trading volume and the order flow dynamics. Local adaptive multiplicative error models are used for analysing and forecasting of high-frequency time series, see Härdle et al (2014). Their framework employs the local parametric approach (LPA) originally proposed by Spokoiny (1998) to the multiplicative error models (MEM) introduced by Engle (2002).…”

Section: Local Adaptive Multiplicative Error Modelsmentioning

confidence: 99%

“…Motivated by econometric literature we assume that the error term follows a (standard) exponential distribution and thus focus on the Exponential-ACD (EACD) model, see, e.g., Engle and Russell (1998) and Härdle et al (2014). By utilising the (quasi) maximum likelihood estimation, the (quasi) log-likelihood function over a (right-end) fixed interval…”

Section: Multiplicative Error Modelsmentioning

confidence: 99%

“…This paper introduces a flexible framework for an useful data-driven application of timevarying dynamics for retrieving valuable information from transaction data at financial markets. The proposed financial steps and trading execution strategies start by employing the the so-called local adaptive multiplicative error models by Härdle et al (2014) due to their demonstrated predictive accuracy. In adaptive forecasting of aggregate trading volume series these models statistically outperform benchmark models.…”

Section: Introductionmentioning

confidence: 99%

“…The modelling and the forecasting part of our study integrates the local parametric approach originally proposed by Spokoiny (1998) and the multiplicative error models introduced by Engle (2002) and therefore accommodates time-varying parameters, see, e.g., Härdle et al (2014). From statistical perspective, in (volume and order flow) time series modelling, longer data intervals induce a large modelling bias and shorter ones lead to quite volatile parameters.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive order flow forecasting with multiplicative error models

Mihoci¹,

Ting

et al. 2022

Digit Finance

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Section: Local Adaptive Multiplicative Error Modelsmentioning

confidence: 99%

Section: Multiplicative Error Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive order flow forecasting with multiplicative error models

Mihoci¹,

Ting

et al. 2022

Digit Finance

Self Cite

View full text Add to dashboard Cite

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“…Čížek, Härdle and Spokoiny (2009), Härdle, Hautsch and Mihoci (2015) and Schröder (2016) suggested the use of adaptive pointwise selection procedures for studying volatility, parameter variations and local trend estimation in financial time series. Our current work explores these ideas for the forecasting of VaR, as a remedy to the problem of window size selection.…”

Section: Introductionmentioning

confidence: 99%

A study on window-size selection for threshold and bootstrap value-at-risk models

Smith¹,

Huang²

2019

JRMV

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This paper investigates the effects of window size selection on various models for Value-at-Risk (VaR) forecasting using high performance computing. Subsequently, automated procedures using change-point analysis for optimal window size selection are proposed. In particular, stationary bootstrapping and the peaksover-threshold methods are utilized for the rolling daily VaR estimation and are contrasted with the classical conditional Gaussian model. It is evidenced that change-point procedures can, on average, result in more adequate risk predictions than a predetermined fixed window size. The data sets analyzed include indices across 5 continents, i.e., the Dow Jones Industrial Average Index (DJI), the Financial Times Stock Exchange 100 Index (UKX), the NIKKEI Top 225 Index (NKY), the Johannesburg Stock Exchange Top 40 Index (JSE Top40), the Ibovespa Brazil Sao Paulo Stock Exchange All Index (IBOV), and the Bombay Stock Exchange Top 500 Index (BSE 500).

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An adaptive approach to forecasting three key macroeconomic variables for transitional China

Niu

Chen

2017

Economic Modelling

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We propose the use of a local autoregressive (LAR) model for adaptive estimation and forecasting of three of China's key macroeconomic variables: GDP growth, inflation and the 7-day interbank lending rate. The approach takes into account possible structural changes in the data-generating process to select a local homogeneous interval for model estimation, and is particularly well-suited to a transition economy experiencing ongoing shifts in policy and structural adjustment. Our results indicate that the proposed method outperforms alternative models and forecast methods, especially for forecast horizons of 3 to 12 months. Our 1-quarter ahead adaptive forecasts even match the performance of the well-known CMRC Langrun survey forecast. The selected homogeneous intervals indicate gradual changes in growth of industrial production driven by constant evolution of the real economy in China, as well as abrupt changes in interestrate and inflation dynamics that capture monetary policy shifts.

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Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

Abstract: We propose a local adaptive multiplicative error model (MEM) accommodating time- JEL classification: C41, C51, C53, G12, G17

Cited by 38 publications

References 25 publications

Adaptive order flow forecasting with multiplicative error models

Adaptive order flow forecasting with multiplicative error models

A study on window-size selection for threshold and bootstrap value-at-risk models

An adaptive approach to forecasting three key macroeconomic variables for transitional China

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