Local Adaptive Multiplicative Error Models for High- Frequency Forecasts

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

doi:10.2139/ssrn.2315830

Cited by 11 publications

(12 citation statements)

References 21 publications

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“…The latter specification allows for changing parameters but requires to impose a priori structures on the form of the transition and on the number of underlying regimes. Alternatively, Haerdle et al (2012) propose to adaptively estimate the MEM based on a window of varying length and thus providing updated parameter estimates at each point in time.…”

Section: The Baseline Memmentioning

confidence: 99%

Chasing Volatility: A Persistent Multiplicative Error Model with Jumps

Caporin

Rossi²,

Magistris

2014

SSRN Journal

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The realized volatility of financial returns is characterized by persistence and occurrence of unpredictable large increments. To capture those features, we introduce the Multiplicative Error Model with jumps (MEM-J). When a jump component is included in the multiplicative specification, the conditional density of the realized measure is shown to be a countably infinite mixture of Gamma and K distributions. Strict stationarity conditions are derived. A Monte Carlo simulation experiment shows that maximum likelihood estimates of the model parameters are reliable even when jumps are rare events. We estimate alternative specifications of the model using a set of daily bipower measures for 7 stock indexes and 16 individual NYSE stocks. The estimates of the jump component confirm that the probability of jumps dramatically increases during the financial crises. Compared to other realized volatility models, the introduction of the jump component provides a sensible improvement in the fit, as well as for in-sample and out-of-sample volatility tail forecasts.

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Section: The Baseline Memmentioning

confidence: 99%

Chasing Volatility: A Persistent Multiplicative Error Model with Jumps

Caporin

Rossi²,

Magistris

2014

SSRN Journal

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“…The local parametric approach crucially rests on the sequencing test of local time-homogeneity to search for an interval of homogeneity among the considered intervals ( = 0, 1, … , ) at a fixed time point 0 . Here, we follow Härdle et al (2015) and Čížek et al (2009) and adopt the local change point detection test, in which the null hypothesis on parameter homogeneity for the intervals up to is tested against the alternative hypothesis that a change point at unknown location within interval exists. Assuming that the homogeneity assumption of interval −1 has not been rejected, the test statistic for testing possible change points in interval is defined via the corresponding fitted log-likelihood ( ; , ) by:…”

Section: Local Change Point Detection Testmentioning

confidence: 99%

“…Since the true distribution of the test statistic is unknown, the critical values have to be determined by simulation using the general approach of testing theory: to provide a prescribed performance of the procedure under the null hypothesis (Čížek et al, 2009;Chen and Niu, 2014;Härdle et al, 2015).…”

Section: Calculation Of Critical Valuesmentioning

confidence: 99%

Adaptive local parametric estimation of crop yields: implications for crop insurance rate making

Shen

2017

European Review of Agricultural Economics

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A rigorous estimation model of crop yields which ensures accurate and actuarially sound insurance premiums is of utmost importance to maintain sustainable and viable risk management solutions for producers, insurers, and governments. A major challenge in estimating crop yield models arises from non-stationarity of the data generating process due to technological change and climate change. In this paper, we introduce a local adaptive parametric approach to deal with the non-stationarity of crop yields and to estimate the time-varying parameters of crop yield models. Results from an empirical application to major crops in the U.S. indicate that the proposed model precisely captures the evolution of crop yield risks: yield risks for corn and cotton are decreasing, but are increasing for winter wheat. In terms of forecasting performance, the adaptive local parametric model, in general, outperforms the linear spline model that is commonly used in the current rating methodology. A rating analysis suggests that the proposed model has the potential to obtain more accurate rates and that most current insurance premium rates are overestimated for corn and cotton, but are underestimated for winter wheat.

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“…Huberman and Halka (2001) evidenced the serial dependence of bid-ask spread and depth in the AutoRegressive model. Härdle, Hautsch and Mihoci (2015) proposed a local adaptive multiplicative error model to forecast the high-frequency series of one-minute cumulative trading volumes of several NASDAQ blue chip stocks. Serial dependence also exists in limit order demand and supply, see Dierker, Kim, Lee and Morck (2014).…”

Section: Vfarrandbidaskcurveplotmentioning

confidence: 99%

Forecasting Limit Order Book Liquidity Supply-Demand Curves with Functional Autoregressive Dynamics

2016

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Limit order book contains comprehensive information of liquidity on bid and ask sides. We propose a Vector Functional AutoRegressive (VFAR) model to describe the dynamics of the limit order book and demand curves and utilize the fitted model to predict the joint evolution of the liquidity demand and supply curves. In the VFAR framework, we derive a closed-form maximum likelihood estimator under sieves and provide the asymptotic consistency of the estimator. In application to limit order book records of 12 stocks in NASDAQ * Corresponding author: Wee Song Chua Email: a0054070@u.nus.edu Phone: +65-6516 3470 Fax: +65-6872 3919 This research was supported by the FRC grant at the National University of Singapore. Support from IRTG 1792 "High Dimensional Non Stationary Time Series", Humboldt-Universität zu Berlin, is gratefully acknowledged. 1 traded from 2 Jan 2015 to 6 Mar 2015, it shows the VAR model presents a strong predictability in liquidity curves, with R 2 values as high as 98.5 percent for insample estimation and 98.2 percent in out-of-sample forecast experiments. It produces accurate 5−, 25− and 50−minute forecasts, with root mean squared error as low as 0.09 to 0.58 and mean absolute percentage error as low as 0.3 to 4.5 percent.

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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 11 publications

References 21 publications

Chasing Volatility: A Persistent Multiplicative Error Model with Jumps

Chasing Volatility: A Persistent Multiplicative Error Model with Jumps

Adaptive local parametric estimation of crop yields: implications for crop insurance rate making

Forecasting Limit Order Book Liquidity Supply-Demand Curves with Functional Autoregressive Dynamics

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