49 research outputs found
APARCH Models Estimated by Support Vector Regression
This thesis presents a comprehensive study of asymmetric power autoregressive conditional heteroschedasticity (APARCH) models for modelling volatility in financial return data. The goal is to estimate and forecast volatility in financial data with excess kurtosis, volatility clustering and asymmetric distribution. Models based on maximum likelihood estimation (MLE) will be compared to the kernel based support vector regression (SVR). The popular Gaussian kernel and a wavelet based kernel will be used for the SVR. The methods will be tested on empirical data, including stock index prices, credit spreads and electric power prices. The results indicate that asymmetric power models are needed to capture the asseymtry in the data. Furthermore, SVR models are able to improve estimation and forecasting accuracy, compared with the APARCH models based on MLE.Masteroppgave i statistikkSTAT399MAMN-STA
Modeling the volatility of cryptocurrencies: an empirical application of stochastic volatility models
This paper compares a number of stochastic volatility (SV) models for modeling and predicting the volatility of the four most capitalized cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin). The standard SV model, models with heavy-tails and moving average innovations, models with jumps, leverage effects and volatility in mean were considered. The Bayes factor for model fit was largely in favor of the heavy-tailed SV model. The forecasting performance of this model was also found superior than the other competing models. Overall, the findings of this study suggest using the heavy-tailed stochastic volatility model for modeling and forecasting the volatility of cryptocurrencies
Volatility and correlation: Modeling and forecasting using Support Vector Machines
Several Realized Volatility and Correlation estimators have been introduced. The estimators which are defined based on high frequency data converge to the true estimators faster than their counterparts even under Market Microstructure Noise. Also a strategy for multivariate volatility estimation has been introduced. The strategy which is an incorporation of Support Vector Machine with Multiresolution Analysis based on wavelets affords higher performance of estimation than the single estimation
Optimal forecasting accuracy using Lp‑norm combination
A well-known result in statistics is that a linear combination of two-point forecasts has a smaller Mean Square Error (MSE) than the two competing forecasts themselves (Bates and Granger in J Oper Res Soc 20(4):451–468, 1969). The only case in which no improvements are possible is when one of the single forecasts is already the optimal one in terms of MSE. The kinds of combination methods are various, ranging from the simple average
(SA) to more robust methods such as the one based on median or Trimmed Average (TA) or Least Absolute Deviations or optimization techniques (Stock and Watson in J Forecast 23(6):405–430, 2004). Standard regression-based combination approaches may fail to get a realistic result if the forecasts show high collinearity in several situations or the data distribution is not Gaussian. Therefore, we propose a forecast combination method based on Lp-norm estimators. These estimators are based on the Generalized Error Distribution, which is a generalization of the Gaussian distribution, and they can be used to solve the cases of multicollinearity and non-Gaussianity. In order to demonstrate the potential of Lpnorms, we conducted a simulated and an empirical study, comparing its performance with other standard-regression combination approaches. We carried out the simulation study with diferent values of the autoregressive parameter, by alternating heteroskedasticity and homoskedasticity. On the other hand, the real data application is based on the daily Bitfnex historical series of bitcoins (2014–2020) and the 25 historical series relating to companies
included in the Dow Jonson, were subsequently considered. We showed that, by combining diferent GARCH and the ARIMA models, assuming both Gaussian and non-Gaussian distributions, the Lp-norm scheme improves the forecasting accuracy with respect to other regression-based combination procedures
Prediction of nonlinear nonstationary time series data using a digital filter and support vector regression
Volatility is a key parameter when measuring the size of the errors made in modelling returns
and other nonlinear nonstationary time series data. The Autoregressive Integrated Moving-
Average (ARIMA) model is a linear process in time series; whilst in the nonlinear system, the
Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Markov Switching
GARCH (MS-GARCH) models have been widely applied. In statistical learning theory,
Support Vector Regression (SVR) plays an important role in predicting nonlinear and
nonstationary time series data. We propose a new class model comprised of a combination of
a novel derivative Empirical Mode Decomposition (EMD), averaging intrinsic mode function
(aIMF) and a novel of multiclass SVR using mean reversion and coefficient of variance (CV)
to predict financial data i.e. EUR-USD exchange rates. The proposed novel aIMF is capable
of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict
exchange rates. Our simulation results show that our model significantly outperforms
simulations by state-of-art ARIMA, GARCH, Markov Switching generalised Autoregressive
conditional Heteroskedasticity (MS-GARCH), Markov Switching Regression (MSR) models
and Markov chain Monte Carlo (MCMC) regression.Open Acces
XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting
Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method
Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations
The increased digitalisation and monitoring of the energy system opens up
numerous opportunities to decarbonise the energy system. Applications on low
voltage, local networks, such as community energy markets and smart storage
will facilitate decarbonisation, but they will require advanced control and
management. Reliable forecasting will be a necessary component of many of these
systems to anticipate key features and uncertainties. Despite this urgent need,
there has not yet been an extensive investigation into the current
state-of-the-art of low voltage level forecasts, other than at the smart meter
level. This paper aims to provide a comprehensive overview of the landscape,
current approaches, core applications, challenges and recommendations. Another
aim of this paper is to facilitate the continued improvement and advancement in
this area. To this end, the paper also surveys some of the most relevant and
promising trends. It establishes an open, community-driven list of the known
low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape
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