scholarly journals Recent developments in volatility modeling and applications

2006 ◽  
Vol 2006 ◽  
pp. 1-23 ◽  
Author(s):  
A. Thavaneswaran ◽  
S. S. Appadoo ◽  
C. R. Bector
Keyword(s):  
Normal Distribution ◽  
High Frequency ◽  
Time Series Data ◽  
Financial Time Series ◽  
Empirical Studies ◽  
High Frequency Data ◽  
Series Data ◽  
Frequency Data ◽  
Arma Process ◽  
Volatility Clustering

In financial modeling, it has been constantly pointed out that volatility clustering and conditional nonnormality induced leptokurtosis observed in high frequency data. Financial time series data are not adequately modeled by normal distribution, and empirical evidence on the non-normality assumption is well documented in the financial literature (details are illustrated by Engle (1982) and Bollerslev (1986)). An ARMA representation has been used by Thavaneswaran et al., in 2005, to derive the kurtosis of the various class of GARCH models such as power GARCH, non-Gaussian GARCH, nonstationary and random coefficient GARCH. Several empirical studies have shown that mixture distributions are more likely to capture heteroskedasticity observed in high frequency data than normal distribution. In this paper, some results on moment properties are generalized to stationary ARMA process with GARCH errors. Application to volatility forecasts and option pricing are also discussed in some detail.

Dynamic financial economic fluctuation model based on non-normal distribution

2020 ◽  
pp. 1-10
Author(s):  
Li Wang
Keyword(s):  
Normal Distribution ◽  
High Frequency ◽  
Extreme Value ◽  
High Order ◽  
High Frequency Data ◽  
Order Moment ◽  
Frequency Data ◽  
High Order Moment ◽  
Model Based ◽  
Skewness And Kurtosis

This paper discusses the modeling of financial volatility under the condition of non-normal distribution. In order to solve the problem that the traditional central moment cannot estimate the thick-tailed distribution, the L-moment which is widely used in the hydrological field is introduced, and the autoregressive conditional moment model is used for static and dynamic fitting based on the generalized Pareto distribution. In order to solve the dimension disaster of multidimensional conditional skewness and kurtosis modeling, the multidimensional skewness and kurtosis model based on distribution is established, and the high-order moment model is deduced. Finally, the problems existing in the traditional investment portfolio are discussed, and on this basis, the high-order moment portfolio is further studied. The results show that the key lies in the selection of the model and the assumption of asset probability distribution. Financial risk analysis can be effective only with a large sample. High-frequency data contain more information and can provide rich data resources. The conditional generalized extreme value distribution can well describe the time-varying characteristics of scale parameters and shape parameters and capture the conditional heteroscedasticity in the high-frequency extreme value time series. Better describe the persistence and aggregation of the extreme value of high frequency data as well as the peak and thick tail characteristics of its distribution.

scholarly journals Modeling Conditional Dependence of Stock Returns Using a Copula-based GARCH Model

2017 ◽  
Vol 6 (2) ◽  
pp. 32
Author(s):  
Eun-Joo Lee ◽  
Noah Klumpe ◽  
Jonathan Vlk ◽  
Seung-Hwan Lee
Keyword(s):  
Stock Returns ◽  
Time Series Data ◽  
Financial Time Series ◽  
Garch Model ◽  
Series Data ◽  
Future Price ◽  
Investment Strategies ◽  
Volatility Clustering ◽  
Technology Stocks ◽  
Stock Portfolio

Investigating dependence structures of stocks that are related to one another should be an important consideration in managing a stock portfolio, among other investment strategies. To capture various dependence features, we employ copula to overcome the limitations of traditional linear correlations. Financial time series data is typically characterized by volatility clustering of returns that influences an estimate of a stock’s future price. To deal with the volatility and dependence of stock returns, this paper provides procedures of combining a copula with a GARCH model which leads to the construction of a multivariate distribution. Using the copula-based GARCH approach that describes the tail dependences of stock returns, we carry out Monte Carlo simulations to predict a company’s movements in the stock market. The procedures are illustrated in two technology stocks, Apple and Samsung.

scholarly journals Improved algorithm for cleaning high frequency data: An analysis of foreign currency

2015 ◽  
Vol 12 (3) ◽  
pp. 125-132
Author(s):  
Nirodha I. Jayawardena ◽  
Jason West ◽  
Neda Todorova ◽  
Bin Li
Keyword(s):  
High Frequency ◽  
Foreign Currency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Volatility Clustering ◽  
Time Series Modelling ◽  
Non Gaussian ◽  
Irregular Spacing ◽  
The Empirical Analysis

High-frequency data are notorious for their noise and asynchrony, which may bias or contaminate the empirical analysis of prices and returns. In this study, we develop a novel data filtering approach that simultaneously addresses volatility clustering and irregular spacing, which are inherent characteristics of high-frequency data. Using high frequency currency data collected at five-minute intervals, we find the presence of vast microstructure noise coupled with random volatility clusters, and observe an extremely non-Gaussian distribution of returns. To process non-Gaussian high-frequency data for time series modelling, we propose two efficient and robust standardisation methods that cater for volatility clusters, which clean the data and achieve near-normal distributions. We show that the filtering process efficiently cleans high-frequency data for use in empirical settings while retaining the underlying distributional properties

scholarly journals MULTIVARIATE AR SYSTEMS AND MIXED FREQUENCY DATA: G-IDENTIFIABILITY AND ESTIMATION

2015 ◽  
Vol 32 (4) ◽  
pp. 793-826 ◽  
Author(s):  
Brian D.O. Anderson ◽  
Manfred Deistler ◽  
Elisabeth Felsenstein ◽  
Bernd Funovits ◽  
Lukas Koelbl ◽  
...  
Keyword(s):  
High Frequency ◽  
Time Series Data ◽  
Series Data ◽  
Frequency Data ◽  
Linear Transformations ◽  
Mixed Frequency ◽  
Second Moments ◽  
Flow Variables ◽  
Parameter Values ◽  
Mixed Frequency Data

This paper is concerned with the problem of identifiability of the parameters of a high frequency multivariate autoregressive model from mixed frequency time series data. We demonstrate identifiability for generic parameter values using the population second moments of the observations. In addition we display a constructive algorithm for the parameter values and establish the continuity of the mapping attaching the high frequency parameters to these population second moments. These structural results are obtained using two alternative tools viz. extended Yule Walker equations and blocking of the output process. The cases of stock and flow variables, as well as of general linear transformations of high frequency data, are treated. Finally, we briefly discuss how our constructive identifiability results can be used for parameter estimation based on the sample second moments.

scholarly journals Bayesian Analysis of Intraday Stochastic Volatility Models of High-Frequency Stock Returns with Skew Heavy-Tailed Errors

2021 ◽  
Vol 14 (4) ◽  
pp. 145
Author(s):  
Makoto Nakakita ◽  
Teruo Nakatsuma
Keyword(s):  
Stock Returns ◽  
Stochastic Volatility ◽  
High Frequency ◽  
Stock Price ◽  
Information Criterion ◽  
High Frequency Data ◽  
Return Volatility ◽  
Frequency Data ◽  
Volatility Clustering ◽  
Variance Gamma

Intraday high-frequency data of stock returns exhibit not only typical characteristics (e.g., volatility clustering and the leverage effect) but also a cyclical pattern of return volatility that is known as intraday seasonality. In this paper, we extend the stochastic volatility (SV) model for application with such intraday high-frequency data and develop an efficient Markov chain Monte Carlo (MCMC) sampling algorithm for Bayesian inference of the proposed model. Our modeling strategy is two-fold. First, we model the intraday seasonality of return volatility as a Bernstein polynomial and estimate it along with the stochastic volatility simultaneously. Second, we incorporate skewness and excess kurtosis of stock returns into the SV model by assuming that the error term follows a family of generalized hyperbolic distributions, including variance-gamma and Student’s t distributions. To improve efficiency of MCMC implementation, we apply an ancillarity-sufficiency interweaving strategy (ASIS) and generalized Gibbs sampling. As a demonstration of our new method, we estimate intraday SV models with 1 min return data of a stock price index (TOPIX) and conduct model selection among various specifications with the widely applicable information criterion (WAIC). The result shows that the SV model with the skew variance-gamma error is the best among the candidates.

Autonomous self-evolving forecasting models for price movement in high frequency trading: Evidence from Taiwan

2020 ◽  
Vol 24 (5) ◽  
pp. 1175-1206
Author(s):  
Chien-Feng Huang ◽  
Hsiao-Chi Wu ◽  
Po-Chun Chen ◽  
Bao Rong Chang
Keyword(s):  
High Frequency ◽  
Prediction Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Supply And Demand ◽  
High Frequency Trading ◽  
Series Data ◽  
Order Book ◽  
Price Movement ◽  
Increasing Demand

Among FinTech research and applications, forecasting financial time series data has been a challenging task because this kind of data is typically quite noisy and non-stationary. A recent line of financial research centers around trading through financial data on the microscopic level, which is the holy grail of high-frequency trading (HFT), as the higher the data frequency, the more profitable opportunities may appear. The advancement in HFT modeling has also facilitated more understanding towards price formation because the supply and demand of a stock can be comprehended more easily from the microstructure of the order book. Instead of traditional statistical methods, there has been increasing demand for the development of more reliable prediction models due to the recent progress in Computational Intelligence (CI) technologies. In this study, we aim to develop novel CI-based methodologies for the forecasting task of price movement in HFT. Our goal is to conduct a study for autonomous genetic-based models that allow the forecasting systems to self-evolve. The results show that our proposed method can improve upon the previous ones and advance the current state of Fintech research.

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