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Accurately Forecasting Model for the Stochastic Volatility Data in Tourism Demand  [PDF]
Ya-Ling Huang, Yen-Hsien Lee
Modern Economy (ME) , 2011, DOI: 10.4236/me.2011.25091
Abstract: This study attempts to enhance the effectiveness of stochastic volatility data. This work presents an empirical case involving the forecasting of tourism demand to demonstrate the efficacy of the accuracy forecasting model. Work combining the grey forecasting model (GM) and Fourier residual modification model to refine the forecasting effectiveness for the stochastic volatility data, which can estimate fluctuations in historical time series. This study makes the following contributions: 1) combining the grey forecasting and Fourier residual modification models to refine the forecasting effectiveness for the stochastic volatility data, 2) providing an effective method for forecasting the number of international visitors to Taiwan, 3) improving the accuracy of short-term forecasting in cases involving sample data with significant fluctuations.
Volatility Forecasting and Volatility Risk Premium  [PDF]
Jingfei Cheng
Journal of Applied Mathematics and Physics (JAMP) , 2015, DOI: 10.4236/jamp.2015.31014

Volatility is an important variable in the financial market. We propose a model-free implied volatility method to measure the volatility and test the volatility risk premium. The model-free implied volatility does not depend on the option pricing model, and extracts information from all the option contracts. We provide empirical evidence from the S & P 500 index option that model-free implied volatility is more accurate to forecast the future volatility and the volatility risk premium does not exist.

Forecasting volatility with the multifractal random walk model  [PDF]
Jean Duchon,Raoul Robert,Vincent Vargas
Mathematics , 2008,
Abstract: We study the problem of forecasting volatility for the multifractal random walk model. In order to avoid the ill posed problem of estimating the correlation length T of the model, we introduce a limiting object defined in a quotient space; formally, this object is an infinite range logvolatility. For this object and the non limiting object, we obtain precise prediction formulas and we apply them to the problem of forecasting volatility and pricing options with the MRW model in the absence of a reliable estimate of the average volatility and T.
Journal of Applied Quantitative Methods , 2009,
Abstract: Empirical studies, such as Lamoureux and Lastrapes (1993), Guo (1998), Fouque et al. (2000) show that the market price of volatility risk is nonzero and time varying. This paper provides a theoretical investigation of the market price of volatility risk. We consider that the market price of volatility risk is a function of two variables: the price of underlying asset and its volatility. We suggest a closed-form solution for the price of volatility risk under the conditions of stochastic volatility and of correlation between the underlying asset price and its volatility. This formula involves in a direct way the unobservable market price of volatility risk. We prove that the correlation between underlying price and its volatility has no impact on the price of volatility risk. Finally, we present empirical results using the prices of CAC 40 index and of CAC 40 index call options from January 2006 to December 2007.
Conceptual Approach to Forecasting Demand Концептуальный подход к прогнозированию спроса  [PDF]
Andreishina Nataliia B.
Business Inform , 2013,
Abstract: The article considers a conceptual approach to forecasting demand on products of a production or trading company using economic and mathematical methods. It justifies importance of modelling and forecasting consumer demand on goods. It provides a classification of methods of forecasting demand in two dimensions: from subjective to objective ones and from na?ve to cause-effect ones. It systemises groups of factors that influence demand and analyses character of their influence. It develops a concept of forecasting demand on products of a company using economic and mathematical methods, which consists of four stages: identification of factors that influence demand, selection of mathematical dependence, check of adequacy and accuracy of the model and forecast of demand. It builds forecast demand models for a specific trading company: Brown’s adaptive polynomial model of the first order; and two-factor model, demand in which depends on the price of a good and its changes. It checks adequacy of each model and performs forecast of demand on products of a company. В статье рассмотрен концептуальный подход к прогнозированию спроса на продукцию производственного или торгового предприятия экономико-математическими методами. Обоснована актуальность моделирования и прогнозирования потребительского спроса на товары. Приведена классификация методов прогнозирования спроса по двум измерениям: от субъективных к объективным и от наивных к причинно-следственным. Систематизированы группы факторов, влияющих на спрос, проанализирован характер их влияния. Создана концепция прогнозирования спроса на продукцию предприятия экономико-математическими методами, которая состоит из четырех этапов: определение факторов, влияющих на спрос, выбор математической зависимости, проверка адекватности и точности модели и прогноз спроса. Для конкретного торгового предприятия построены прогнозные модели спроса: адаптивная полиномиальная модель Брауна первого порядка; двухфакторная модель, в которой спрос за
Intraday volatility forecasting: analysis of alternative distributions  [cached]
Paulo Sérgio Ceretta,Fernanda Galv?o de Barba,Kelmara Mendes Vieira,Fernando Casarin
Revista Brasileira de Finan?as , 2011,
Abstract: Volatility forecasting has been of great interest both in academic and professional fields all over the world. However, there is no agreement about the best model to estimatevolatility. New models include measures of skewness, changes of regimes and different distributions; few studies, though, have considered different distributions. This paper aims to investigate how the specification of a distribution influences the performance of volatility forecasting on Ibovespa intraday data, using the APARCH model. The forecasts were carried out assuming six distinct distributions: normal, skewed normal, t-student, skewed t-student, generalized and skewed generalized. The results evidence that the model considering the skewed t-student distribution offered the best fit to the data inside the sample, on the other hand, the model assuming a normal distribution provided a better out-of-the-sample performance forecast.
Parametric inference and forecasting in continuously invertible volatility models  [PDF]
Olivier Wintenberger,Sixiang Cai
Statistics , 2011,
Abstract: We introduce the notion of continuously invertible volatility models that relies on some Lyapunov condition and some regularity condition. We show that it is almost equivalent to the ability of the volatilities forecasting using the parametric inference approach based on the SRE given in [16]. Under very weak assumptions, we prove the strong consistency and the asymptotic normality of the parametric inference. Based on this parametric estimation, a natural strongly consistent forecast of the volatility is given. We apply successfully this approach to recover known results on univariate and multivariate GARCH type models and to the EGARCH(1,1) model. We prove the strong consistency of the forecasting as soon as the model is invertible and the asymptotic normality of the parametric inference as soon as the limiting variance exists. Finally, we give some encouraging empirical results of our approach on simulations and real data.
Modeling and Forecasting of Ghana’s Inflation Volatility  [PDF]
Abdul-Karim Iddrisu, Dominic Otoo, Iddrisu Wahab Abdul, Sylvia Ankamah
American Journal of Industrial and Business Management (AJIBM) , 2019, DOI: 10.4236/ajibm.2019.94064
In this paper, we assessed volatility of Ghana’s inflation rates for 2000 to 2018 using the auto-regressive conditionally heteroskedasticity (ARCH), generalized ARCH (GARCH), and the exponential GARCH (EGARCH) models. The inflation data were obtained from the Ghana Statistical Service (GSS). The proposed model should be able to provide projections of inflation volatility from 2019 and beyond. The results showed that higher order models are required to properly explain Ghana’s inflation volatility and the EGARCH(12, 1) is the best fitting model for the data. The EGARCH(12, 1) model is robust to model and forecast volatility of inflation rates. Also, the results suggest that we are forecasting increasing volatility and there is increasing trend in general prices of goods and services for 2018 and beyond. The forecasts figures revealed that Ghana’s economy is likely to be unstable in 2018 and 2019. This study therefore recommends that policy makers and industry players need to put in place stringent monetary and fiscal policies that would put the anticipated increase in inflation under control. The models were implemented using R software.
Forecasting Realized Volatility Using Subsample Averaging  [PDF]
Huiyu Huang, Tae-Hwy Lee
Open Journal of Statistics (OJS) , 2013, DOI: 10.4236/ojs.2013.35044

When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While subsample averaging has been proposed and used in estimating RV, this paper is the first that uses subsample averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, then generates forecasts from each subsample, and then combines these forecasts. We find that in daily S&P 500 return realized volatility forecasts, subsample averaging generates better forecasts than those using only one subsample.

Modeling and forecasting exchange rate volatility in time-frequency domain  [PDF]
Jozef Barunik,Tomas Krehlik,Lukas Vacha
Quantitative Finance , 2012,
Abstract: This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of volatility into several timescales approximates the behaviour of traders at corresponding investment horizons. The proposed methodology is moreover able to account for impact of jumps due to a recently proposed jump wavelet two scale realized volatility estimator. We propose a realized Jump-GARCH models estimated in two versions using maximum likelihood as well as observation-driven estimation framework of generalized autoregressive score. We compare forecasts using several popular realized volatility measures on foreign exchange rate futures data covering the recent financial crisis. Our results indicate that disentangling jump variation from the integrated variation is important for forecasting performance. An interesting insight into the volatility process is also provided by its multiscale decomposition. We find that most of the information for future volatility comes from high frequency part of the spectra representing very short investment horizons. Our newly proposed models outperform statistically the popular as well conventional models in both one-day and multi-period-ahead forecasting.
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