Volatility Of Time Series Research Articles

Addressing Stock Market Time Series Trends and Volatility Using Optimized DE-LSTM Model

Addressing Stock Market Time Series Trends and Volatility Using Optimized DE-LSTM Model

Performance Comparison of Two Statistical Parametric Methods for Outlier Detection and Correction

Abstract Outlier detection and correction referred to as data preprocessing, is crucial in time series analysis and modeling. It has been a challenge to preprocess a volatile time series data possessing intricate trend characteristics. Two well-established statistical parametric methods, such as improved sliding window prediction and portrait dataset-based, perform adequate data preprocessing. While the former is equipped with an optimal window width selection approach, the latter, on the other hand, is based on a data visualization approach named portrait. This paper compares both methods’ preprocessing performance when applied to seasonal time series data with varying time resolutions and complex trend patterns for different content of outliers through detailed result analyses. Further, a new metric to measure outlier correction capability is suggested.

Semimartingale and continuous-time Markov chain approximation for rough stochastic local volatility models

Rough volatility models have recently been empirically shown to provide a good fit to historical volatility time series and implied volatility smiles of SPX options. They are continuous-time stochastic volatility models, whose volatility process is driven by a fractional Brownian motion with Hurst parameter less than half. Due to the challenge that it is neither a semimartingale nor a Markov process, there is no unified method that not only applies to all rough volatility models, but also is computationally efficient. This paper proposes a semimartingale and continuous-time Markov chain (CTMC) approximation approach for the general class of rough stochastic local volatility (RSLV) models. In particular, we introduce the perturbed stochastic local volatility (PSLV) model as the semimartingale approximation for the RSLV model and establish its existence , uniqueness and Markovian representation. We propose a fast CTMC algorithm and prove its weak convergence. Numerical experiments demonstrate the accuracy and high efficiency of the method in pricing European, barrier and American options. Comparing with existing literature, a significant reduction in the CPU time to arrive at the same level of accuracy is observed.

Machine learning of stochastic automata and evolutionary games

A large categories of time series fluctuate dramatically, for example, prices of agriculture produce. Traditional methods in time series and stochastic prediction may not capture such dynamics. This paper tries to use machine learning to tune the model for a real situation by establishing a price determination mechanism on the model of stochastic automata (SA) and evolutionary game (EG). Time series volatility attributed to the chaotic process can be obtained through the learning algorithm of Markov Chain Monte Carlo (MCMC). Using machine learning through the chaotic analysis of stochastic automata and evolutionary games, we find that a more spatially aggregated distribution (smaller entropy) leads to larger time series fluctuations, regardless of the initial distribution of crops. By integrating the factors discovered in this study, we can develop a better learning algorithm in a highly volatile time series in agriculture prices.

Volatility of cryptocurrencies

Many models have been developed to model, estimate and forecast financial time series volatility, amongst which are the most popular autoregressive conditional heteroscedasticity (ARCH) model introduced by Engle (1982) and generalized autoregressive conditional heteroscedasticity (GARCH) model introduced by Bollerslev (1986). The aim of this paper is to determine which type of ARCH/GARCH models can fit the best following cryptocurrencies: Ethereum, Neo, Ripple, Litecoin, Dash, Zcash and Dogecoin. It is found that the EGARCH model is the best fitted model for Ethereum, Zcash and Neo, PARCH model is the best fitted model for Ripple, while for Litecoin, Dash and Dogecoin it depends on the selected distribution and information criterion.

Bayesian latent autoregressive stochastic volatility: an application of naira to eleven exchangeable currencies rates

This paper provides a procedure for estimating Stochastic Volatility (SV) in financial time series via latent autoregressive in a Bayesian setting. A Gaussian distributional combined prior and posterior of all hyper-parameters (autoregressive coefficients) were specified such that the Markov Chain Monte Carlo (MCMC) iterative procedure via the Gibbs and Metropolis-Hasting sampling method was used in estimating the resulting exponentiated forms (quadratic forms) from the posterior kernel density. A case study of Naira to eleven (11) exchangeable currencies\(^,\) rates by Central Bank of Nigeria (CBN) was subjected to the estimated solutions of the autoregressive stochastic volatility. The posterior volatility estimates at 5%, 50%, and 95% quantiles of \({e^{\frac{\mu }{2}}}\) = (0.130041, 0.1502 and 0.1795) respectively unveiled that the Naira-US Dollar exchange rates has the highest rates bartered by fluctuations.

Use of Stochastic Volatility Models in Epidemiological Data: Application to a Dengue Time Series in São Paulo City, Brazil

Background: A study on the dengue daily counting in São Paulo city in a fixed period of time is assumed considering a new regression model approch. Methods: Under a Bayesian approach, it is introduced a polynomial linear regression model in presence of some covariates which could affect the counts of dengue in São Paulo city considered in the logarithm scale, combined with existing stochastic volatility models usually assumed in financial data analysis. Markov Chain Monte Carlo methods are used to get the posterior summaries of interest. Results: The new model approach showed some advantages when compared to other existing times series models usually used to model epidemics data. Conclusion: The use of the polynomial regression model combined with existing volatility models under a Bayesian approach showed that it is possible to get very accurate fit for the counting dengue data in São Paulo city where it is possible to jointly model the means and volatilities (variances) of the epidemiological dengue time series.

Stock price prediction using deep learning and frequency decomposition

Nonlinearity and high volatility of financial time series have made it difficult to predict stock price. However, thanks to recent developments in deep learning and methods such as long short-term memory (LSTM) and convolutional neural network (CNN) models, significant improvements have been obtained in the analysis of this type of data. Further, empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition (CEEMD) algorithms decomposing time series to different frequency spectra are among the methods that could be effective in analyzing financial time series. Based on these theoretical frameworks, we propose novel hybrid algorithms, i.e., CEEMD-CNN-LSTM and EMD-CNN-LSTM, which could extract deep features and time sequences, which are finally applied to one-step-ahead prediction. The concept of the suggested algorithm is that when combining these models, some collaboration is established between them that could enhance the analytical power of the model. The practical findings confirm this claim and indicate that CNN alongside LSTM and CEEMD or EMD could enhance the prediction accuracy and outperform other counterparts. Further, the suggested algorithm with CEEMD provides better performance compared to EMD.

Monitoring Volatility Change for Time Series Based on Support Vector Regression.

This paper considers monitoring an anomaly from sequentially observed time series with heteroscedastic conditional volatilities based on the cumulative sum (CUSUM) method combined with support vector regression (SVR). The proposed online monitoring process is designed to detect a significant change in volatility of financial time series. The tuning parameters are optimally chosen using particle swarm optimization (PSO). We conduct Monte Carlo simulation experiments to illustrate the validity of the proposed method. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and the stock price of Microsoft Corporation is presented to demonstrate the versatility of our model.

Short and long-term volatility transmission from oil to agricultural commodities – The robust quantile regression approach

This paper investigates permanent and transitory spillover effects from Brent oil futures to four agricultural futures – corn, wheat, soybean and canola. We construct permanent and transitory volatilities via component GARCH model, considering six different distribution functions. Created volatility time-series are embedded in the robust quantile regression framework. Transitory effect from oil market has slightly stronger influence on the agricultural commodities than its permanent counterpart, which is a sign that short-term information flow has more intense effect than fundamental factors. The results indicate that the best diversification instrument in combination with oil is soybean futures, since it is the least subject to oil volatility shocks.

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

Modelling Australian Dollar Volatility at Multiple Horizons with High-Frequency Data

Long-range dependency of the volatility of exchange-rate time series plays a crucial role in the evaluation of exchange-rate risks, in particular for the commodity currencies. The Australian dollar is currently holding the fifth rank in the global top 10 most frequently traded currencies. The popularity of the Aussie dollar among currency traders belongs to the so-called three G’s—Geology, Geography and Government policy. The Australian economy is largely driven by commodities. The strength of the Australian dollar is counter-cyclical relative to other currencies and ties proximately to the geographical, commercial linkage with Asia and the commodity cycle. As such, we consider that the Australian dollar presents strong characteristics of the commodity currency. In this study, we provide an examination of the Australian dollar–US dollar rates. For the period from 18:05, 7th August 2019 to 9:25, 16th September 2019 with a total of 8481 observations, a wavelet-based approach that allows for modelling long-memory characteristics of this currency pair at different trading horizons is used in our analysis. Findings from our analysis indicate that long-range dependence in volatility is observed and it is persistent across horizons. However, this long-range dependence in volatility is most prominent at the horizon longer than daily. Policy implications have emerged based on the findings of this paper in relation to the important determinant of volatility dynamics, which can be incorporated in optimal trading strategies and policy implications.

Fractality and Multifractality in a Stock Market’s Nonstationary Financial Time Series

A financial time series, such as a stock market index, foreign exchange rate, or a commodity price, fluctuates heavily and shows scaling behaviors. Scaling and multi-scaling behaviors are measured for a nonstationary time series, such as stock market indices, high-frequency stock prices of individual stocks, or the volatility time series of a stock index. We review the fractality, multi-scaling, and multifractality of the financial time series of a stock market. We introduce a detrended fluctuation analysis of the financial time series to extract fluctuation patterns. Multifractality is measured using various methods, such as generalized Hurst exponents, the generalized partition function method, a detrended fluctuation analysis, the detrended moving average method, and a wavelet transformation.

Identification of structural multivariate GARCH models

The class of multivariate GARCH models is widely used to quantify and monitor volatility and correlation dynamics of financial time series. While many specifications have been proposed in the literature, these models are typically silent about the system inherent transmission of implied orthogonalized shocks to vector returns. In a framework of non-Gaussian independent structural shocks, this paper proposes a loss statistic, based on higher order co-moments, to discriminate in a data-driven way between alternative structural assumptions about the transmission scheme, and hence identify the structural model. Consistency of identification is shown theoretically and via a simulation study. In its structural form, a four dimensional system comprising US and Latin American stock market returns points to a substantial volatility transmission from the US to the Latin American markets. The identified structural model improves the estimation of classical measures of portfolio risk, as well as corresponding variations.

Stochastic volatility forecasting of the Finnish housing market

ABSTRACT The purpose of the article is to assess the in-sample fit and the out-of-sample forecasting performances of four stochastic volatility (SV) models in the Finnish housing market. The competing models are the vanilla SV, the SV model where the latent volatility follows a stationary AR(2) process, the heavy-tailed SV and the SV with leverage effects. The models are estimated using Bayesian technique, and the results reveal that the SV with leverage effects is the best model for modelling the Finnish house price volatility. The heavy-tailed SV model provides accurate out-of-sample volatility forecasts in most of the studied regions. Additionally, the models’ performances are noted to vary across almost all cities and sub-areas, and by apartment types. Moreover, the AR(2) component substantially improves the in-sample fit of the standard SV, but it is unimportant for the out-of-sample forecasting performance. The study outcomes have crucial implications, such as portfolio management and investment decision-making. To establish suitable time-series volatility forecasting models of this housing market, these study outcomes will be compared to the performances of their GARCH models counterparts.

A Study on Price Volatility and Properties of Time-series for the Tangerine Price in Jeju

A Study on Price Volatility and Properties of Time-series for the Tangerine Price in Jeju

Financial intermediation and capital reallocation

To understand the link between financial intermediation activities and the real economy, we build a general equilibrium model in which agency frictions in the financial sector affect the efficiency of capital reallocation across firms and generate aggregate economic fluctuations. We develop a recursive policy iteration approach to fully characterize the nonlinear equilibrium dynamics and the off-steady-state crisis behavior. In our model, adverse shocks to agency frictions exacerbate capital misallocation and manifest themselves as variations in total factor productivity at the aggregate level. Our model endogenously generates countercyclical volatility in the aggregate time series and countercyclical dispersion in the marginal product of capital and asset returns in the cross-section.

Uncertainty shocks and business cycle research

We review the literature on uncertainty shocks and business cycle research. First, we motivate the study of uncertainty shocks by documenting the presence of time-variation in the volatility of macroeconomic time series. Second, we enumerate the mechanisms that researchers have postulated to link uncertainty shocks and business cycles. Third, we outline how we can specify uncertainty shocks. Fourth, we postulate a real business cycle model augmented with financial frictions and uncertainty shocks. Fifth, we use the model to illustrate our previous discussions and to show how uncertainty shocks can be expansionary, a useful finding in several contexts.

Volatility Forecasts Jakarta Composite Index (JCI) and Index Stock Volatility Sector with Estimated Time Series

This study aims to explore the comparative ability of forecasting models and the time series volatility of capital markets in Indonesia using JCI daily index data and sectoral indices from January 2010 to December 2014. The use of ARCH-family ARCH model (1.1) and GARCH (1.1) used to capture symmetrical effects, while TGARCH (1.1), EGARCH (1.1), APGARCH (1.1) on asymmetric effects. The results show that JCI return has an asymmetrical effect and the closest forecasting model is EGARCH (1.1). Returns for AGRI, MINING, BASICIND, INFRA, FIN, TRADE indices also have asymmetrical effects but are modeled with TGARCH (1.1). Meanwhile, the MISCIND, CONSUMER, PROPERTY indexes have a symmetrical effect and are modeled with GARCH (1.1). These models can explain forecasting closest to the real as well as provide guidance investors in the Indonesia capital market as one of the emerging markets

The (in)efficiency of NYMEX energy futures: A multifractal analysis

We performed a systematic analysis to investigate the multifractal properties and Efficient Market Hypothesis (EMH) in time series of volatility for NYMEX (New York Mercantile Exchange) energy futures using the Multifractal Detrended Fluctuations Analysis (MF-DFA). We studied the generalized Hurst exponent h(q) and the Rényi exponent τ(q) for each record and quantify their statistical properties which allowed us to observe separately the contributing small scale (primarily via the negative moments q) and the large scale (via the positive moments q). We also calculated the multifractal spectrum f(α) and used a fourth-degree polynomial regression fit to estimate the complexity parameters that describe the degree of multifractality of the underlying process. The results obtained from our fitting procedure show that the volatility time series of NYMEX energy futures display overall persistent behavior (α0>0.5), a higher degree of multifractality and the dominance of large fluctuations. In this sense, our empirical results related to (α0>0.5) categorically reject the EMH. Furthermore, our results presented that Crude oil (Light-Sweet, Cushing, Oklahoma), the No. 2 heating oil (New York Harbor) and Propane (Mont Belvieu, Texas) energy futures are highly concentrated with behavior more closely associated to oligopolistic market.