Seasonality Effects Through Arch And Garch Model
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Seasonality Effects Through Arch And Garch Model
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Author : Rizwan Ahmed
language : en
Publisher:
Release Date : 2016
Seasonality Effects Through Arch And Garch Model written by Rizwan Ahmed and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
The paper examines three seasonal effects from Shanghai Stock market China: the weekend effect, turn of the month and holiday effect. The evidences of weekend effect observed on Friday along with seasonality effect on alternate days of the week. In terms of monthly effect, we have found February anomaly in place of January which contradicts the theory of Tax-Loss selling hypothesis. Moreover, last quarter of the year showing positive healthy returns. However, the study could not find any trend of returns on holiday effects. The ARCH and GARCH model of all three seasonality effects showing significant results. Based on overall outcomes, the Shanghai Stock market is considered as inefficient weak form of market efficiency.
Unobserved Components In Arch Models
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Author : Gabriele Fiorentini
language : en
Publisher:
Release Date : 1994
Unobserved Components In Arch Models written by Gabriele Fiorentini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Autoregression (Statistics) categories.
The Turn Of The Month Effect
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Author : Eleftherios Giovanis
language : en
Publisher:
Release Date : 2015
The Turn Of The Month Effect written by Eleftherios Giovanis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
The current study examines the turn of the month effect on stock returns in 20 countries. This will allow us to explore whether the seasonal patterns usually found in global data; America, Australia, Europe and Asia. Ordinary Least Squares (OLS) is problematic as it leads to unreliable estimations; because of the autocorrelation and Autoregressive Conditional Heteroskedasticity (ARCH) effects existence. For this reason Generalized GARCH models are estimated. Two approaches are followed. The first is the symmetric Generalized ARCH (1,1) model. However, previous studies found that volatility tends to increase more when the stock market index decreases than when the stock market index increases by the same amount. In addition there is higher seasonality in volatility rather on average returns. For this reason the Periodic-GARCH (1,1) is estimated. The findings support the persistence of the specific calendar effect in 19 out of 20 countries examined.
The Econometric Analysis Of Seasonal Time Series
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Author : Eric Ghysels
language : en
Publisher: Cambridge University Press
Release Date : 2001-06-18
The Econometric Analysis Of Seasonal Time Series written by Eric Ghysels and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-06-18 with Business & Economics categories.
Eric Ghysels and Denise R. Osborn provide a thorough and timely review of the recent developments in the econometric analysis of seasonal economic time series, summarizing a decade of theoretical advances in the area. The authors discuss the asymptotic distribution theory for linear nonstationary seasonal stochastic processes. They also cover the latest contributions to the theory and practice of seasonal adjustment, together with its implications for estimation and hypothesis testing. Moreover, a comprehensive analysis of periodic models is provided, including stationary and nonstationary cases. The book concludes with a discussion of some nonlinear seasonal and periodic models. The treatment is designed for an audience of researchers and advanced graduate students.
Economic Time Series
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Author : William R. Bell
language : en
Publisher: CRC Press
Release Date : 2018-11-14
Economic Time Series written by William R. Bell and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-14 with Mathematics categories.
Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time s
Regression Modeling With Actuarial And Financial Applications
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Author : Edward W. Frees
language : en
Publisher: Cambridge University Press
Release Date : 2010
Regression Modeling With Actuarial And Financial Applications written by Edward W. Frees and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Business & Economics categories.
This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
Stochasticity Nonlinearity And Forecasting Of Streamflow Processes
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Author : Wen Wang
language : en
Publisher: IOS Press
Release Date : 2006
Stochasticity Nonlinearity And Forecasting Of Streamflow Processes written by Wen Wang and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computers categories.
Streamflow forecasting is of great importance to water resources management and flood defense. On the other hand, a better understanding of the streamflow process is fundamental for improving the skill of streamflow forecasting. The methods for forecasting streamflows may fall into two general classes: process-driven methods and data-driven methods. Equivalently, methods for understanding streamflow processes may also be broken into two categories: physically-based methods and mathematically-based methods. This thesis focuses on using mathematically-based methods to analyze stochasticity and nonlinearity of streamflow processes based on univariate historic streamflow records, and presents data-driven models that are also mainly based on univariate streamflow time series. Six streamflow processes of five rivers in different geological regions are investigated for stochasticity and nonlinearity at several characteristic timescales.
Econometric Modelling Of Stock Market Intraday Activity
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Author : Luc Bauwens
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Econometric Modelling Of Stock Market Intraday Activity written by Luc Bauwens and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Business & Economics categories.
Over the past 25 years, applied econometrics has undergone tremen dous changes, with active developments in fields of research such as time series, labor econometrics, financial econometrics and simulation based methods. Time series analysis has been an active field of research since the seminal work by Box and Jenkins (1976), who introduced a gen eral framework in which time series can be analyzed. In the world of financial econometrics and the application of time series techniques, the ARCH model of Engle (1982) has shifted the focus from the modelling of the process in itself to the modelling of the volatility of the process. In less than 15 years, it has become one of the most successful fields of 1 applied econometric research with hundreds of published papers. As an alternative to the ARCH modelling of the volatility, Taylor (1986) intro duced the stochastic volatility model, whose features are quite similar to the ARCH specification but which involves an unobserved or latent component for the volatility. While being more difficult to estimate than usual GARCH models, stochastic volatility models have found numerous applications in the modelling of volatility and more particularly in the econometric part of option pricing formulas. Although modelling volatil ity is one of the best known examples of applied financial econometrics, other topics (factor models, present value relationships, term structure 2 models) were also successfully tackled.
Seasonality Revisited
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Author : Kevin B. Burk
language : en
Publisher: Serendipity Press
Release Date : 2021-05-05
Seasonality Revisited written by Kevin B. Burk and has been published by Serendipity Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-05 with categories.
Seasonality RevisitedThe Use of Irregular Seasonality in Quantitative Time Series Analysis and Forecasting This monograph revisits the theory of seasonality by asserting that seasonality is a quality of time, and that the calendar and the clock are not the only ways to measure time. Every season has an effect that can be quantified using Cohen's d, which makes it possible to compare seasonal effects between different seasonal models. The historic variance of the effect sizes can be used to determine the predictive value of an individual season for that data set. Irregular seasonal models, derived from the cycles of the planet Mercury, are compared to regular seasonal models using Calendar Month seasonality as the baseline reference of significance. The quantified significance of Calendar Month seasonality is used as the baseline of comparison to evaluate the potential significance of the irregular Mercury-based seasonal models. The seasonal models are compared across three extensive and unrelated data sets: Transportation On-Time Performance, Car Crash, and Financial Market data. The Mercury-based irregular seasonal models showed a far greater percentage of significant seasons than the Calendar Month model. The ability to compare and contrast seasonal effects in this way demonstrates the practical value of the effect- and variance-based approach to quantifying seasonal influences. It also confirms that irregular seasonal models can reveal patterns in time series data that are otherwise undetectable. To test the value of incorporating irregular seasonal influences in time series forecasting, 76 quarterly forecasts covering a 19-year period from 2000-2018 were generated for each of 430 individual financial data sets (10 stock market indexes, 379 individual stocks, 21 commodities, 10 interest rates and bonds, and 10 currency exchange rates). The aggregate accuracy of twelve different forecast models was then ranked and compared with both mean absolute percentage error (MAPE) and root mean square error (RMSE) The forecast models include five non-seasonal traditional forecast models (ARIMA, ESM, HOLT, MEAN, and NAÏVE), the seasonal forecast generated using the M15 Sign + Speed seasons, and six hybrid seasonal forecasts that combine the seasonal forecast data with the forecast data of each of the traditional forecast models. Two different methods, designated, E3 and M3, were used to generate the seasonal forecasts for the entire data set. For the forecasts that used the E3 model, the hybrid seasonal forecasts were more accurate than their non-seasonal counterparts 71.30% of the time (MAPE) and 66.93% of the time (RMSE). For the forecasts that used the M3 model, the hybrid seasonal forecasts were more accurate than their non-seasonal counterparts 74.37% of the time (MAPE) and 72.33% of the time (RMSE). This clearly demonstrates that including the irregular seasonal influences has a significant chance of improving the accuracy of the forecast. The conclusions of this study are that there is clear and consistent value in this new approach to seasonality, both with quantitative time series analysis and with quantitative time series forecasting. These discoveries are worth further serious consideration and exploration.
A Comprehensive Assessment Of The Role Of Risk In U S Agriculture
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Author : Richard E. Just
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
A Comprehensive Assessment Of The Role Of Risk In U S Agriculture written by Richard E. Just and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Technology & Engineering categories.
After all the research on agricultural risk to date, the treatment of risk in agricultural research is far from harmonious. Many competing risk models have been proposed. Some new methodologies are largely untested. Some of the leading empirical methodologies in agricultural economic research are poorly suited for problems with aggregate data where risk averse behavior is less likely to be important. This book is intended to (i) define the current state of the literature on agricultural risk research, (ii) provide a critical evaluation of economic risk research on agriculture to date and (iii) set a research agenda that will meet future needs and prospects. This type of research promises to become of increasing importance because agricultural policy in the United States and elsewhere has decidedly shifted from explicit income support objectives to risk-related motivations of helping farmers deal with risk. Beginning with the 1996 Farm Bill, the primary set of policy instruments from U.S. agriculture has shifted from target prices and set aside acreage to agricultural crop insurance. Because this book is intended to have specific implications for U.S. agricultural policy, it has a decidedly domestic scope, but clearly many of the issues have application abroad. For each of the papers and topics included in this volume, individuals have been selected to give the strongest and broadest possible treatment of each facet of the problem. The result is this comprehensive reference book on the economics of agricultural risk.