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Journal articles on the topic 'Autoregressive duration model (ACD)'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

JEYASREEDHARAN, NAGARATNAM, DAVID E. ALLEN, and JOEY WENLING YANG. "YET ANOTHER ACD MODEL: THE AUTOREGRESSIVE CONDITIONAL DIRECTIONAL DURATION (ACDD) MODEL." Annals of Financial Economics 09, no. 01 (2014): 1450004. http://dx.doi.org/10.1142/s2010495214500043.

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This paper features a new autoregressive conditional duration (ACD) model which sits within the theoretical framework provided by the recently developed observation-driven time series models by Creal et al. (2013): the generalized autoregressive score (GAS) models. The autoregressive conditional directional duration (ACDD) model itself contains three novelties. First, durations (intra-trade intervals or waiting-times) are signed, based on whether a (positive) ask-driven trade or a (negative) bid-driven trade occurred. These signed trade-durations are known as directional durations. Second, as the resultant directional durations are no longer positive and asymmetrical but are symmetrically distributed, the familiar generalized autoregressive conditional heteroskedasticity (GARCH)-like formulation of the ACD process is proposed for modeling these directional durations. Consequently, the proposed model is called the ACDD model. Third, using the alternative GARCH-like formulation, persistence or long-memory in the durations is easily addressed both via the mean and variance equations: the mean equation uses a semi-parametric fractional autoregressive (SEMIFAR) formulation and the variance equation uses a GARCH formulation. The paper demonstrates the flexibility and convenience of the generalized autoregressive score (GAS) model framework in the context of a particular ACD model specification. The model can be viewed as an alternative extension of the "asymmetric ACD model" of Bauwens and Giot (2013) which captures information related to the evolution of prices as well as the quote-durations.

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Cunha, Danúbia R., Roberto Vila, Helton Saulo, and Rodrigo N. Fernandez. "A General Family of Autoregressive Conditional Duration Models Applied to High-Frequency Financial Data." Journal of Risk and Financial Management 13, no. 3 (2020): 45. http://dx.doi.org/10.3390/jrfm13030045.

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In this paper, we propose a general family of Birnbaum–Saunders autoregressive conditional duration (BS-ACD) models based on generalized Birnbaum–Saunders (GBS) distributions, denoted by GBS-ACD. We further generalize these GBS-ACD models by using a Box-Cox transformation with a shape parameter λ to the conditional median dynamics and an asymmetric response to shocks; this is denoted by GBS-AACD. We then carry out a Monte Carlo simulation study to evaluate the performance of the GBS-ACD models. Finally, an illustration of the proposed models is made by using New York stock exchange (NYSE) transaction data.

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Vlahogianni, Eleni I., Matthew G. Karlaftis, and Konstantinos Kepaptsoglou. "Nonlinear Autoregressive Conditional Duration Models for Traffic Congestion Estimation." Journal of Probability and Statistics 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/798953.

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The considerable impact of congestion on transportation networks is reflected by the vast amount of research papers dedicated to congestion identification, modeling, and alleviation. Despite this, the statistical characteristics of congestion, and particularly of its duration, have not been systematically studied, regardless of the fact that they can offer significant insights on its formation, effects and alleviation. We extend previous research by proposing the autoregressive conditional duration (ACD) approach for modeling congestion duration in urban signalized arterials. Results based on data from a signalized arterial indicate that a multiregime nonlinear ACD model best describes the observed congestion duration data while when it lasts longer than 18 minutes, traffic exhibits persistence and slow recovery rate.

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SIN, CHOR-YIU. "QMLE OF A STANDARD EXPONENTIAL ACD MODEL: ASYMPTOTIC DISTRIBUTION AND RESIDUAL CORRELATION." Annals of Financial Economics 09, no. 02 (2014): 1440009. http://dx.doi.org/10.1142/s2010495214400090.

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Since the seminal work by Engle and Russell, (1998), numerous studies have applied their standard/linear ACD(m,q) model (autoregressive conditional duration model of orders m and q) to fit the irregular spaced transaction data. Recently, Araichi et al. (2013) also applied the ACD model to claims in insurance. Many of these papers assume that the standardized error follows a standard exponential distribution. In this paper, we derive the asymptotic distribution of the quasi-maximum likelihood estimator (QMLE) when a standard exponential distribution is used. In other words, we provide robust standard errors for an ACD model. Applying this asymptotic theory, we then derive the asymptotic distribution of the corresponding residual autocorrelation.

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5

Deo, Rohit, Clifford M. Hurvich, Philippe Soulier, and Yi Wang. "CONDITIONS FOR THE PROPAGATION OF MEMORY PARAMETER FROM DURATIONS TO COUNTS AND REALIZED VOLATILITY." Econometric Theory 25, no. 3 (2009): 764–92. http://dx.doi.org/10.1017/s0266466608090294.

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We establish sufficient conditions on durations that are stationary with finite variance and memory parameter$d \in [0,{\textstyle{1 \over 2}})$to ensure that the corresponding counting processN(t) satisfies VarN(t) ~Ct2d+1(C> 0) ast→ ∞, with the same memory parameter$d \in [0,{\textstyle{1 \over 2}})$that was assumed for the durations. Thus, these conditions ensure that the memory parameter in durations propagates to the same memory parameter in the counts. We then show that any autoregressive conditional duration ACD(1,1) model with a sufficient number of finite moments yields short memory in counts, whereas any long memory stochastic duration model withd> 0 and all finite moments yields long memory in counts, with the samed. Finally, we provide some results about the propagation of long memory to the empirically relevant case of realized variance estimates affected by market microstructure noise contamination.

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6

Shi, Yong, Wei Dai, Wen Long, and Bo Li. "Improved ACD-Based Financial Trade Durations Prediction Leveraging LSTM Networks and Attention Mechanism." Mathematical Problems in Engineering 2021 (January 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/7854512.

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The liquidity risk factor of security market plays an important role in the formulation of trading strategies. A more liquid stock market means that the securities can be bought or sold more easily. As a sound indicator of market liquidity, the transaction duration is the focus of this study. We concentrate on estimating the probability density function p Δ t i + 1 | G i , where Δ t i + 1 represents the duration of the (i + 1)-th transaction and G i represents the historical information at the time when the (i + 1)-th transaction occurs. In this paper, we propose a new ultrahigh-frequency (UHF) duration modelling framework by utilizing long short-term memory (LSTM) networks to extend the conditional mean equation of classic autoregressive conditional duration (ACD) model while retaining the probabilistic inference ability. And then, the attention mechanism is leveraged to unveil the internal mechanism of the constructed model. In order to minimize the impact of manual parameter tuning, we adopt fixed hyperparameters during the training process. The experiments applied to a large-scale dataset prove the superiority of the proposed hybrid models. In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer.

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7

Bortoluzzo, Adriana B., Pedro A. Morettin, and Clelia M. C. Toloi. "Time-varying autoregressive conditional duration model." Journal of Applied Statistics 37, no. 5 (2010): 847–64. http://dx.doi.org/10.1080/02664760902914458.

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8

Meitz, Mika, and Pentti Saikkonen. "ERGODICITY, MIXING, AND EXISTENCE OF MOMENTS OF A CLASS OF MARKOV MODELS WITH APPLICATIONS TO GARCH AND ACD MODELS." Econometric Theory 24, no. 5 (2008): 1291–320. http://dx.doi.org/10.1017/s0266466608080511.

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This paper studies a class of Markov models that consist of two components. Typically, one of the components is observable and the other is unobservable or “hidden.” Conditions under which geometric ergodicity of the unobservable component is inherited by the joint process formed of the two components are given. This implies existence of initial values such that the joint process is strictly stationary and β-mixing. In addition to this, conditions for the existence of moments are also obtained, and extensions to the case of nonstationary initial values are provided. All these results are applied to a general model that includes as special cases various first-order generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional duration (ACD) models with possibly complicated nonlinear structures. The results only require mild moment assumptions and in some cases provide necessary and sufficient conditions for geometric ergodicity.

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9

Liang, Y., A. Thavaneswaran, and B. Abraham. "Joint Estimation Using Quadratic Estimating Function." Journal of Probability and Statistics 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/372512.

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A class of martingale estimating functions is convenient and plays an important role for inference for nonlinear time series models. However, when the information about the first four conditional moments of the observed process becomes available, the quadratic estimating functions are more informative. In this paper, a general framework for joint estimation of conditional mean and variance parameters in time series models using quadratic estimating functions is developed. Superiority of the approach is demonstrated by comparing the information associated with the optimal quadratic estimating function with the information associated with other estimating functions. The method is used to study the optimal quadratic estimating functions of the parameters of autoregressive conditional duration (ACD) models, random coefficient autoregressive (RCA) models, doubly stochastic models and regression models with ARCH errors. Closed-form expressions for the information gain are also discussed in some detail.

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10

Li, W. K., and Philip L. H. Yu. "On the residual autocorrelation of the autoregressive conditional duration model." Economics Letters 79, no. 2 (2003): 169–75. http://dx.doi.org/10.1016/s0165-1765(02)00303-8.

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11

Lu, Wanbo, and Rui Ke. "A generalized least squares estimation method for the autoregressive conditional duration model." Statistical Papers 60, no. 1 (2016): 123–46. http://dx.doi.org/10.1007/s00362-016-0830-3.

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12

Gómez–Déniz, Emilio, and Jorge V. Pérez–Rodríguez. "Mixture inverse Gaussian for unobserved heterogeneity in the autoregressive conditional duration model." Communications in Statistics - Theory and Methods 46, no. 18 (2017): 9007–25. http://dx.doi.org/10.1080/03610926.2016.1200094.

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13

Ye, Wuyi, and Ruyu Zhao. "Dependent structure and risk analysis of S&P 500 Index's continuously rising returns and continuously falling returns." Journal of Risk Finance 22, no. 1 (2021): 93–109. http://dx.doi.org/10.1108/jrf-01-2020-0003.

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PurposeThe stock market price time series can be divided into two processes: continuously rising and continuously falling. The authors can effectively prevent the stock market from crashing by accurately estimating the risk on continuously rising returns (CRR) and continuously falling returns (CFR).Design/methodology/approachThe authors add an exogenous variable into Log-autoregressive conditional duration (Log-ACD) model, and then apply our extended Log-ACD model and Archimedean copula to estimate the marginal distribution and conditional distribution of CRR and CFR. Plus, the authors analyze the conditional value at risk (CVaR) and present back-test results of the CVaR. The back-test shows that our proposed risk estimation method has a good estimation power for the risk of the CRR and CFR, especially the downside risk. In addition, the authors detect whether the dependent structure between the CRR and CFR changes using the change point test method.FindingsThe empirical results indicate that there is no change point here, suggesting that the results on the dependent structure and risk analysis mentioned above are stable. Therefore, major financial events will not affect the dependent structure here. This is consistent with the point that the CRR and CFR can be analyzed to obtain the trend of stock returns from a more macro perspective than daily stock returns scholars usually study.Practical implicationsThe risk estimation method of this paper is of great significance in understanding stock market risk and can provide corresponding valuable information for investment advisors and public policy regulators.Originality/valueThe authors defined a new stock returns, CRR and CFR, since it is difficult to analyze and predict the trend of stock returns according to daily stock returns because of the small autocorrelation among daily stock returns.

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14

Chou, Heng-Chih. "Using the autoregressive conditional duration model to analyse the process of default contagion." Applied Financial Economics 22, no. 13 (2012): 1111–20. http://dx.doi.org/10.1080/09603107.2011.641927.

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15

Padhye, Nikhil S., and Sandra K. Hanneman. "Cosinor Analysis for Temperature Time Series Data of Long Duration." Biological Research For Nursing 9, no. 1 (2007): 30–41. http://dx.doi.org/10.1177/1099800407303509.

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The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

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16

Małecka, Marta. "Testing for a serial correlation in VaR failures through the exponential autoregressive conditional duration model." Statistics in Transition New Series 22, no. 1 (2021): 145–62. http://dx.doi.org/10.21307/stattrans-2021-008.

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17

Chen, Feng, and Peter Hall. "Inference for a Nonstationary Self-Exciting Point Process with an Application in Ultra-High Frequency Financial Data Modeling." Journal of Applied Probability 50, no. 4 (2013): 1006–24. http://dx.doi.org/10.1239/jap/1389370096.

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Self-exciting point processes (SEPPs), or Hawkes processes, have found applications in a wide range of fields, such as epidemiology, seismology, neuroscience, engineering, and more recently financial econometrics and social interactions. In the traditional SEPP models, the baseline intensity is assumed to be a constant. This has restricted the application of SEPPs to situations where there is clearly a self-exciting phenomenon, but a constant baseline intensity is inappropriate. In this paper, to model point processes with varying baseline intensity, we introduce SEPP models with time-varying background intensities (SEPPVB, for short). We show that SEPPVB models are competitive with autoregressive conditional SEPP models (Engle and Russell 1998) for modeling ultra-high frequency data. We also develop asymptotic theory for maximum likelihood estimation based inference of parametric SEPP models, including SEPPVB. We illustrate applications to ultra-high frequency financial data analysis, and we compare performance with the autoregressive conditional duration models.

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18

Olivas-Padilla, Brenda Elizabeth, Sotiris Manitsaris, Dimitrios Menychtas, and Alina Glushkova. "Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables." Sensors 21, no. 7 (2021): 2497. http://dx.doi.org/10.3390/s21072497.

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In industry, ergonomists apply heuristic methods to determine workers’ exposure to ergonomic risks; however, current methods are limited to evaluating postures or measuring the duration and frequency of professional tasks. The work described here aims to deepen ergonomic analysis by using joint angles computed from inertial sensors to model the dynamics of professional movements and the collaboration between joints. This work is based on the hypothesis that with these models, it is possible to forecast workers’ posture and identify the joints contributing to the motion, which can later be used for ergonomic risk prevention. The modeling was based on the Gesture Operational Model, which uses autoregressive models to learn the dynamics of the joints by assuming associations between them. Euler angles were used for training to avoid forecasting errors such as bone stretching and invalid skeleton configurations, which commonly occur with models trained with joint positions. The statistical significance of the assumptions of each model was computed to determine the joints most involved in the movements. The forecasting performance of the models was evaluated, and the selection of joints was validated, by achieving a high gesture recognition performance. Finally, a sensitivity analysis was conducted to investigate the response of the system to disturbances and their effect on the posture.

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19

Ferriani, Fabrizio. "Traders and time: who moves the market?" Studies in Economics and Finance 32, no. 1 (2015): 74–97. http://dx.doi.org/10.1108/sef-03-2014-0065.

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Purpose – This paper is aimed to investigate the impact of different categories of traders on price and volume durations at Euronext Paris. The two series are respectively related to the instantaneous volatility and the market liquidity; hence, they are particularly suited to test microstructure hypotheses. Design/methodology/approach – A Log-autoregressive conditional duration model was adopted to include the information on the traders’ identity at the transaction level. High-frequency data were used and how the informed traders and the liquidity provider affect the arrival of market events was studied. The robustness of our results was also checked by testing different distributions and controlling for microstructure effects. Findings – It was found that informed traders and the liquidity provider exert a dominant role in accelerating the market activity. This result depends on the state of the market, i.e. it is effective only during periods of high frequency of transactions. The estimates for price durations show that a high instantaneous volatility can be mainly ascribed to a great concentration of informed traders. Informed traders are also found to shorten volume durations by clustering small-size orders to disguise their private signal. For both durations, the liquidity provider is also found to foster the market activity, likely because of his contractual duties. Originality/value – The article is of interest for researchers in the field of market microstructure, as well as for specialists in the high-frequency trading. Results provide an empirical confirmation of information models which theorize an accelerating effect for informed trading. To the best of the authors’ knowledge, this is the first contribution to study the impact of traders’categories at the transaction level and with different definitions of durations.

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Meitz, Mika, and Timo Teräsvirta. "Evaluating Models of Autoregressive Conditional Duration." Journal of Business & Economic Statistics 24, no. 1 (2006): 104–24. http://dx.doi.org/10.1198/073500105000000081.

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21

Zheng, Yao, Yang Li, and Guodong Li. "On Fréchet autoregressive conditional duration models." Journal of Statistical Planning and Inference 175 (August 2016): 51–66. http://dx.doi.org/10.1016/j.jspi.2016.02.009.

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22

Fernandes, Marcelo, and Joachim Grammig. "A family of autoregressive conditional duration models." Journal of Econometrics 130, no. 1 (2006): 1–23. http://dx.doi.org/10.1016/j.jeconom.2004.08.016.

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23

Gupta, Rangan, Marius Jurgilas, Alain Kabundi, and Stephen M. Miller. "MONETARY POLICY AND HOUSING SECTOR DYNAMICS IN A LARGE-SCALE BAYESIAN VECTOR AUTOREGRESSIVE MODEL / PINIGŲ POLITIKA IR BŪSTO SEKTORIAUS DINAMIKA TAIKANT PLATAUS MASTO BAJESO VEKTORINĮ AUTOREGRESINĮ MODELĮ." International Journal of Strategic Property Management 16, no. 1 (2012): 1–20. http://dx.doi.org/10.3846/1648715x.2011.621466.

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Our paper considers the channel whereby monetary policy, a federal funds rate shock, affects the dynamics of the US housing sector. The analysis uses impulse response functions obtained from a large-scale Bayesian vector autoregressive model that incorporates 143 monthly macroeconomic variables over the period of 1986:01 to 2003:12, including 21 variables relating to the housing sector at the national and four Census regions. We find at the national level that housing starts, housing permits, and housing sales fall in response to the tightening of monetary policy. Housing sales react more quickly and sharply than starts and permits and exhibit more duration. Housing prices show the weakest response to the monetary policy shock. At the regional level, we conclude that the housing sector in the south drives the national findings in the sense that the response patterns in the South most closely match the response patterns in the nation as a whole. The West's responses differs the most from the other regions, especially for the impulse responses of housing starts and permits. Santrauka Straipsnyje nagrinėjama, kokiais kanalais pinigų politika (sukrėtimai dėl federalinių fondų palūkanų normos) veikia JAV būsto sektoriaus dinamiką. Analizei naudojamos reakcijos į impulsus funkcijos, gautos iš plataus masto Bajeso vektorinio autoregresinio modelio, kuris apima laikotarpį nuo 1986 m. sausio mėn. iki 2003 m. gruodžio mėn. ir 143 mėnesinius makroekonominius kintamuosius, įskaitant 21 su būsto sektoriumi susijusį kintamąjį nacionaliniu mastu pagal keturis statistinius regionus (angl. Census regions). Nustatyta, kad, nacionaliniu lygmeniu griežtėjat pinigų politikai, statoma mažiau naujų būstų, išduodama mažiau leidimų gyvenamajai statybai ir parduodama mažiau būstų. Prekybos būstais sektorius reaguoja sparčiau ir aštriau nei naujų statybų ir leidimų sektorius, o reakcija trunka ilgiau. Nustatyta, kad būstų kainos į pinigų politikos sukrėtimus reaguoja menkiausiai. Daroma išvada, kad regioniniu lygmeniu Pietų regiono būstų sektorius labiausiai prisideda prie nacionalinių išvadų tuo požiūriu, jog reakcijos pobūdis Pietuose panašiausias į bendrą nacionalinį reakcijos pobūdį. Vakarų reakcija nuo kitų regionų skiriasi labiausiai, ypač kalbant apie reakcijas į impulsus, susijusias su statomais būstais ir statybų leidimais.

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Feng, Yanhong, Dilong Xu, Pierre Failler, and Tinghui Li. "Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation." Sustainability 12, no. 16 (2020): 6523. http://dx.doi.org/10.3390/su12166523.

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Due to multiple properties, the international crude oil price is influenced by various and complex interrelated factors from different determinants in different periods. However, the previous studies on crude oil price fluctuation with economic policy uncertainty (EPU) haven’t taken a wider range of volatility sources into their analysis frameworks. In this paper, the time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model is introduced in order to avoid important information loss, as well as capture the time-varying impact on crude oil price fluctuation by EPU. Furthermore, the differences on crude oil fluctuations from net-oil exporting and net-oil importing country’s EPU are also elaborated. Here are three findings as follows. First, the impacts of global EPU on the crude oil price volatility show time-varying characteristics both in time duration and time-points. Second, the instantaneous impacts of global EPU on the price volatility of crude oil are directly relevant to major events, and the impacts are different in event types as well. Third, the time-varying characteristics depicting the impacts of EPU in countries who are net-oil exporter and net-oil importer on price volatility of crude oil show heterogeneity in fluctuation range, fluctuation intensity, and stage.

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Bhatti, Chad R. "The Birnbaum–Saunders autoregressive conditional duration model." Mathematics and Computers in Simulation 80, no. 10 (2010): 2062–78. http://dx.doi.org/10.1016/j.matcom.2010.01.011.

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Siakoulis, Vasileios. "Bank failure intensity modeling: an ACD model approach." Journal of Risk Finance 19, no. 5 (2018): 454–77. http://dx.doi.org/10.1108/jrf-11-2016-0151.

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PurposeThe purpose of this study is to employ a duration-based approach to model the inter-arrival times of bank failures in the US banking system for the period of 1934-2014, in line with the suggestions of Focardi and Fabozzi (2005), who used a similar model for explaining contagion in credit portfolios.Design/methodology/approachConditional duration models that allow duration between bank failures to depend linearly or nonlinearly on its past history are estimated and evaluated.FindingsThe authors find evidence of strong persistence along with nonmonotonic hazard rates, which imply a financial contagion pattern, according to which a high frequency of bank failures generates turbulence, which shortly after leads to additional fails, whereas prolonged periods without abnormal events signify the absence of contagious dependence, which increases the relative periods between bank failure appearance. Further, the authors obtain statistically significant results when they allow duration to depend linearly on past information variables that capture systemic bank crisis factors along with stock and bond market effects.Originality/valueThe originality of this study consists in proposing a new time series approach for the prediction of bank probability of default by incorporating a default-risk contagion mechanism. As contagious bank failures are a key topic in macroprudential supervision, this study could be of value for supervisory authorities in setting pro-active actions and tightening regulatory measures.

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Pokhriyal, H., and N. Balakrishna. "Bootstrap prediction intervals for autoregressive conditional duration models." Journal of Statistical Computation and Simulation 89, no. 15 (2019): 2930–50. http://dx.doi.org/10.1080/00949655.2019.1644513.

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28

Chen, Y. T., and C. S. Hsieh. "Generalized Moment Tests for Autoregressive Conditional Duration Models." Journal of Financial Econometrics 8, no. 3 (2010): 345–91. http://dx.doi.org/10.1093/jjfinec/nbq016.

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Saart, Patrick W., Jiti Gao, and David E. Allen. "Semiparametric Autoregressive Conditional Duration Model: Theory and Practice." Econometric Reviews 34, no. 6-10 (2014): 849–81. http://dx.doi.org/10.1080/07474938.2014.956594.

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Aronica, G. T., and B. Bonaccorso. "Climate Change Effects on Hydropower Potential in the Alcantara River Basin in Sicily (Italy)." Earth Interactions 17, no. 19 (2013): 1–22. http://dx.doi.org/10.1175/2012ei000508.1.

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Abstract In recent years, increasing attention has been paid to hydropower generation, since it is a renewable, efficient, and reliable source of energy, as well as an effective tool to reduce the atmospheric concentrations of greenhouse gases resulting from human activities. At the same time, however, hydropower is among the most vulnerable industries to global warming, because water resources are closely linked to climate changes. Indeed, the effects of climate change on water availability are expected to affect hydropower generation with special reference to southern countries, which are supposed to face dryer conditions in the next decades. The aim of this paper is to qualitatively assess the impact of future climate change on the hydrological regime of the Alcantara River basin, eastern Sicily (Italy), based on Monte Carlo simulations. Synthetic series of daily rainfall and temperature are generated, based on observed data, through a first-order Markov chain and an autoregressive moving average (ARMA) model, respectively, for the current scenario and two future scenarios at 2025. In particular, relative changes in the monthly mean and standard deviation values of daily rainfall and temperature at 2025, predicted by the Hadley Centre Coupled Model, version 3 (HadCM3) for A2 and B2 greenhouse gas emissions scenarios, are adopted to generate future values of precipitation and temperature. Synthetic series for the two climatic scenarios are then introduced as input into the Identification of Unit Hydrographs and Component Flows from Rainfall, Evapotranspiration and Streamflow Data (IHACRES) model to simulate the hydrological response of the basin. The effects of climate change are investigated by analyzing potential modification of the resulting flow duration curves and utilization curves, which allow a site's energy potential for the design of run-of-river hydropower plants to be estimated.

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Focardi, Sergio M., and Frank J. Fabozzi. "An autoregressive conditional duration model of credit‐risk contagion." Journal of Risk Finance 6, no. 3 (2005): 208–25. http://dx.doi.org/10.1108/15265940510599829.

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32

Wei, Liu, Hui-min Wang, and Min Chen. "Least absolute deviation estimation of autoregressive conditional duration model." Acta Mathematicae Applicatae Sinica, English Series 27, no. 2 (2011): 243–54. http://dx.doi.org/10.1007/s10255-011-0059-9.

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33

Luca, Giovanni De, and Giampiero M. Gallo. "Time-Varying Mixing Weights in Mixture Autoregressive Conditional Duration Models." Econometric Reviews 28, no. 1-3 (2008): 102–20. http://dx.doi.org/10.1080/07474930802387944.

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Wenjuan, Wei. "Study on the Duration of Market Microstructure Theory." International Journal of Business and Management 12, no. 10 (2017): 252. http://dx.doi.org/10.5539/ijbm.v12n10p252.

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This paper starts from the theory of market microstructure, by researching in the development of market microstructure and the theoretical framework, it is found that the proposed duration model is of great significance to market participants. Therefore, based on the theory of market microstructure, this paper summarizes and analyzes the related theories and applications of ACD model.

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Bień-Barkowska, Katarzyna. "Extension and verification of the asymmetric autoregressive conditional duration models." International Journal of Computer Mathematics 94, no. 11 (2017): 2223–38. http://dx.doi.org/10.1080/00207160.2017.1283019.

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Fernandes, Marcelo, Marcelo C. Medeiros, and Alvaro Veiga. "A (Semi)Parametric Functional Coefficient Logarithmic Autoregressive Conditional Duration Model." Econometric Reviews 35, no. 7 (2014): 1221–50. http://dx.doi.org/10.1080/07474938.2014.977071.

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Grammig, Joachim, and Kai‐Oliver Maurer. "Non‐monotonic hazard functions and the autoregressive conditional duration model." Econometrics Journal 3, no. 1 (2000): 16–38. http://dx.doi.org/10.1111/1368-423x.00037.

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Hirano, F., D. Van der Heijde, F. A. Van Gaalen, R. B. M. Landewé, C. Gaujoux-Viala, and S. Ramiro. "SAT0375 DETERMINANTS OF PATIENT’S GLOBAL ASSESSMENT OF WELL-BEING IN EARLY AXIAL SPONDYLOARTHRITIS; 5-YEAR LONGITUDINAL DATA FROM THE DESIR COHORT." Annals of the Rheumatic Diseases 79, Suppl 1 (2020): 1135–36. http://dx.doi.org/10.1136/annrheumdis-2020-eular.816.

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Background:A framework has been proposed to explain which disease outcomes impact quality of life or well-being in patients with axSpA; this was based on cross-sectional data and patients with radiographic axSpA.1Objectives:To investigate the determinants of patient’s well-being over time, and the influence of contextual factors on these relationships in patients with early axSpA.Methods:Five-year data from DESIR, a cohort of early axSpA, were analysed. Clinical data were collected every 6 months up to 2 years and annually thereafter. X-rays and MRI of the spine and SIJ were performed at baseline, 2, and 5 years. The outcome was BAS-G, the patient’s global assessment of the disease impact on well-being. Generalized estimating equations (GEE) were used to test the relationship between potential explanatory variables from 5 domains (disease activity, physical function, spinal mobility, structural damage, spinal and SIJ inflammation) and BAS-G over 5 years. Longitudinal relationships were analysed using an autoregressive GEE model. Contextual factors (patient’s educational level, gender and age) were tested as potential effect modifiers or confounders.Results:A total of 708 patients were included, mean age 33.7 (SD 8.6) years, 46% male, 41% lower educated. Higher scores of the individual questions of BASDAI on fatigue (Q1) (β [95% CI]: 0.17 [0.13-0.22]), back pain (Q2) (0.51 [0.46-0.56]), peripheral joint pain (Q3) (0.08 [0.04-0.12]) and severity of morning stiffness (Q5) (0.08 [0.03-0.13]), and BASFI (0.14 [0.08-0.19]) were independently associated with a higher BAS-G over time (Table 1). In the autoregressive GEE model, all variables except for the BASDAI Q5 showed true longitudinal associations with BAS-G. Age, gender and educational level were neither effect modifiers nor confounders.Table 1.Factors associated with BAS-G over time.Multivariable GEE modelMultivariable autoregressive GEE model §Coefficient (95% CI)Coefficient (95% CI)BASDAI Q1 (fatigue, 0-10)0.17 (0.13 to 0.22)*0.15 (0.10 to 0.20)*BASDAI Q2 (back pain, 0-10)0.51 (0.46 to 0.56)*0.54 (0.47 to 0.60)*BASDAI Q3 (peripheral joint pain, 0-10)0.08 (0.04 to 0.12)*0.13 (0.08 to 0.19)*BASDAI Q4 (enthesitis, 0-10)0.03 (-0.01 to 0.07)0.02 (-0.04 to 0.08)BASDAI Q5 (severity of morning stiffness, 0-10)0.08 (0.03 to 0.13)*0.06 (-0.01 to 0.13)BASDAI Q6 (duration of morning stiffness, 0-10)0.03 (-0.01 to 0.07)0.05 (-0.01 to 0.11)SJC28 (0-28)0.01 (-0.11 to 0.13)0.10 (-0.11 to 0.31)TJC53 (0-159) ¶-0.01 (-0.02 to 0.01)-0.01 (-0.03 to 0.01)MASES (0-39)0.00 (-0.02 to 0.02)-0.00 (-0.03 to 0.02)CRP (mg/L)0.01 (-0.00 to 0.01)0.00 (-0.01 to 0.01)Any EAM (presence vs absence)-0.05 (-0.21 to 0.11)-0.09 (-0.28 to 0.10)BASFI (0-10)0.14 (0.08 to 0.19)*0.08 (0.00 to 0.16)*BASMI linear (0-10)-0.07 (-0.16 to 0.02)-0.10 (-0.22 to 0.02)mNY grading (0-8)0.01 (-0.03 to 0.06)0.06 (0.01 to 0.12)*mSASSS (0-72)-0.01 (-0.04 to 0.02)0.00 (-0.03 to 0.04)* p-value < 0.05¶ Each joint graded 0-3§Adjusted for the outcome (i.e. BAS-G) one year before, in order to disentangle the cross-sectional and longitudinal relationships between outcomes and allow the interpretation of a longitudinal relationshipConclusion:A higher level of back pain was associated with a worsening of the patient’s well-being in early axSpA, as were, though to a lesser extent, higher levels of fatigue, morning stiffness, peripheral joint pain and physical disability. Contextual factors like age, gender and educational level did not have an impact on these relationships. Thus, the previously proposed framework of disease outcomes also applies to patients with early axSpA and to outcomes over time.References:[1]Machado, P. ARD 2011.Disclosure of Interests:Fumio Hirano Paid instructor for: Ono pharmaceuticals, Astellas Pharma Inc, Sumitomo Dainippon Pharma, Chugai Pharmaceutical Co., Ltd., Désirée van der Heijde Consultant of: AbbVie, Amgen, Astellas, AstraZeneca, BMS, Boehringer Ingelheim, Celgene, Cyxone, Daiichi, Eisai, Eli-Lilly, Galapagos, Gilead Sciences, Inc., Glaxo-Smith-Kline, Janssen, Merck, Novartis, Pfizer, Regeneron, Roche, Sanofi, Takeda, UCB Pharma; Director of Imaging Rheumatology BV, Floris A. van Gaalen: None declared, Robert B.M. Landewé Consultant of: AbbVie; AstraZeneca; Bristol-Myers Squibb; Eli Lilly & Co.; Galapagos NV; Novartis; Pfizer; UCB Pharma, Cecile Gaujoux-Viala: None declared, Sofia Ramiro Grant/research support from: MSD, Consultant of: Abbvie, Lilly, Novartis, Sanofi Genzyme, Speakers bureau: Lilly, MSD, Novartis

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Leiva, Víctor, Helton Saulo, Jeremias Leão, and Carolina Marchant. "A family of autoregressive conditional duration models applied to financial data." Computational Statistics & Data Analysis 79 (November 2014): 175–91. http://dx.doi.org/10.1016/j.csda.2014.05.016.

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Engle, Robert F., and Jeffrey R. Russell. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data." Econometrica 66, no. 5 (1998): 1127. http://dx.doi.org/10.2307/2999632.

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Pyrlik, Vladimir. "Autoregressive conditional duration as a model for financial market crashes prediction." Physica A: Statistical Mechanics and its Applications 392, no. 23 (2013): 6041–51. http://dx.doi.org/10.1016/j.physa.2013.07.072.

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Lu, Wanbo, Rui Ke, and Jingwen Liang. "A moment closed form estimator for the autoregressive conditional duration model." Statistical Papers 57, no. 2 (2014): 329–44. http://dx.doi.org/10.1007/s00362-014-0652-0.

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Kwok, Simon Sai Man. "The Autoregressive Conditional Marked Duration Model: Statistical Inference to Market Microstructure." Journal of Data Science 7, no. 2 (2021): 189–201. http://dx.doi.org/10.6339/jds.2009.07(2).438.

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Mehana, Mohamed, Mohamed Abdelrahman, Yasmin Emadeldin, Jai S. Rohila, and Raghupathy Karthikeyan. "Impact of Genetic Improvements of Rice on Its Water Use and Effects of Climate Variability in Egypt." Agriculture 11, no. 9 (2021): 865. http://dx.doi.org/10.3390/agriculture11090865.

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Developing and disseminating resilient rice cultivars with increased productivity is a key solution to the problem of limited natural resources such as land and water. We investigated trends in rice cultivation areas and the overall production in Egypt between 2000 and 2018. This study identified rice cultivars that showed potential for high productivity when cultivated under limited irrigation. The results indicated that there were significant annual reductions in both the rice-cultivated area (−1.7% per year) and the production (−1.9% per year) during the study period. Among the commonly cultivated varieties, Sakha101 showed the highest land unit productivity, while Sakha102 showed the highest water unit productivity. The impact of deploying new cultivars was analyzed by substitution scenarios. The results showed that substituting cultivars Giza179 and Sakha107 has the potential to increase land productivity by 15.8% and 22.6%, respectively. This could result in 0.8 million m3 in water savings compared to 2018 water consumption. Long-term impacts of climate variability on the minimum and maximum temperature, relative humidity, and average precipitation during on- and off-season for rice productivity were also analyzed using an autoregressive distributed lag (ARDL) model. The results indicated that climate variability has an overall negative impact on rice productivity. Specifically, minimum temperature and on- and off-season precipitation had major long-term impacts, while higher relative humidity had a pronounced short-term impact on rice yields. The study revealed that short-duration cultivars with higher yields provided greater net savings in irrigation resources. These analyses are critical to guide the development of strategic management plans to mitigate short- and long-term climate effects on overall rice production and for developing and deploying improved rice varieties for sustainable rice production.

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Bonomi, A. G., G. Plasqui, A. H. C. Goris, and K. R. Westerterp. "Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer." Journal of Applied Physiology 107, no. 3 (2009): 655–61. http://dx.doi.org/10.1152/japplphysiol.00150.2009.

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Accelerometers are often used to quantify the acceleration of the body in arbitrary units (counts) to measure physical activity (PA) and to estimate energy expenditure. The present study investigated whether the identification of types of PA with one accelerometer could improve the estimation of energy expenditure compared with activity counts. Total energy expenditure (TEE) of 15 subjects was measured with the use of double-labeled water. The physical activity level (PAL) was derived by dividing TEE by sleeping metabolic rate. Simultaneously, PA was measured with one accelerometer. Accelerometer output was processed to calculate activity counts per day (ACD) and to determine the daily duration of six types of common activities identified with a classification tree model. A daily metabolic value (METD) was calculated as mean of the MET compendium value of each activity type weighed by the daily duration. TEE was predicted by ACD and body weight and by ACD and fat-free mass, with a standard error of estimate (SEE) of 1.47 MJ/day, and 1.2 MJ/day, respectively. The replacement in these models of ACD with METD increased the explained variation in TEE by 9%, decreasing SEE by 0.14 MJ/day and 0.18 MJ/day, respectively. The correlation between PAL and METD ( R2 = 51%) was higher than that between PAL and ACD ( R2 = 46%). We conclude that identification of activity types combined with MET intensity values improves the assessment of energy expenditure compared with activity counts. Future studies could develop models to objectively assess activity type and intensity to further increase accuracy of the energy expenditure estimation.

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Zhang, Michael Yuanjie, Jeffrey R. Russell, and Ruey S. Tsay. "A nonlinear autoregressive conditional duration model with applications to financial transaction data." Journal of Econometrics 104, no. 1 (2001): 179–207. http://dx.doi.org/10.1016/s0304-4076(01)00063-x.

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Chiang, Min-Hsien, and Li-Min Wang. "Additive Outlier Detection and Estimation for the Logarithmic Autoregressive Conditional Duration Model." Communications in Statistics - Simulation and Computation 41, no. 3 (2012): 287–301. http://dx.doi.org/10.1080/03610918.2011.586481.

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Hong, Yongmiao, and Yoon-Jin Lee. "Detecting misspecifications in autoregressive conditional duration models and non-negative time-series processes." Journal of Time Series Analysis 32, no. 1 (2010): 1–32. http://dx.doi.org/10.1111/j.1467-9892.2010.00681.x.

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Omori, Yasuhiro. "Discrete Duration Model Having Autoregressive Random Effects with Application to Japanese Diffusion Index." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 33, no. 1 (2003): 1–22. http://dx.doi.org/10.14490/jjss.33.1.

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Yue, Xiao-Guang, Xue-Feng Shao, Rita Yi Man Li, et al. "Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy." Journal of Risk and Financial Management 13, no. 4 (2020): 66. http://dx.doi.org/10.3390/jrfm13040066.

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This study first analyzes the national and global infection status of the Coronavirus Disease that emerged in 2019 (COVID-19). It then uses the trend comparison method to predict the inflection point and Key Point of the COVID-19 virus by comparison with the severe acute respiratory syndrome (SARS) graphs, followed by using the Autoregressive Integrated Moving Average model, Autoregressive Moving Average model, Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors, and Holt Winter’s Exponential Smoothing to predict infections, deaths, and GDP in China. Finally, it discusses and assesses the impact of these results. This study argues that even if the risks and impacts of the epidemic are significant, China’s economy will continue to maintain steady development.

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