APARCH Model Research Articles

PENERAPAN MODEL ASYMMETRIC POWER AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (APARCH) TERHADAP HARGA MINYAK MENTAH DUNIA

Heteroscedasticity poses a challenge in ARIMA modeling by causing residual variance to be non-constant, leading to less efficient estimates. This issue often arises in time series data due to volatility, which measures data fluctuation over time. To address heteroscedasticity, models like ARCH and GARCH incorporate variance changes into forecasting. However, they lack the ability to capture asymmetry, the difference in impact between good and bad news on volatility. The APARCH model, on the other hand, addresses this by modeling volatility with asymmetry elements. Daily world crude oil prices, known for high volatility, serve as a case study for this research. By employing the APARCH model, the study aims to forecast these prices accurately. Results indicate that the APARCH(1,1) model outperforms the best GARCH model, ARCH(2), as it yields a smaller Mean Absolute Percentage Error (MAPE) of 6.033487. This highlights the superior accuracy of APARCH in forecasting data with heteroscedasticity issues, particularly in the context of daily crude oil prices.

Applications of GARCH Models in Forecasting Financial Market Volatility: Insights from Leading Global Stock Indexes

This study investigates the volatility dynamics of major global stock indexes, including the FTSE 100, Hang Seng Index, NIKKEI 225, and S&P 500, using a range of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The analysis spans a comprehensive 20-year period from January 1, 2004, to December 31, 2023, encompassing diverse market conditions such as bull and bear markets, the 2008 financial crisis, and the COVID-19 pandemic. The methodology includes preprocessing steps such as calculating daily log returns, performing descriptive statistics, and conducting stationarity and ARCH effect tests to ensure data suitability for volatility modelling. The study evaluates several GARCH models, including GARCH, EGARCH, NGARCH, APARCH, GJR-GARCH, and TGARCH, to forecast volatility and address both symmetric and asymmetric effects. The TGARCH model exhibits strong performance in capturing leverage effects and asymmetries, particularly for the FTSE 100 and Hang Seng Index. The APARCH model performs well for the S&P 500, demonstrating sensitivity to past shocks. Overall, the findings underscore the importance of advanced GARCH models in accurately predicting volatility in global financial markets, highlighting the TGARCH model's effectiveness in addressing asymmetries and providing insights into selecting appropriate models for enhanced financial analysis and risk management.

Volatility and models based on the extreme value theory for gold returns

In this study, we use daily gold log-returns to analyse the quality of forecasting expected shortfalls (ES) using volatility and models based on the extreme value theory (EVT). ES forecasts were calculated for conditional APARCH models formed on the entire distribution of returns, as well as for EVT models. The results of ES forecasts for each model were verified using the backtesting procedure proposed by Acerbi and Szekely. The results show that EVT models provide more accurate one-day ahead ES forecasts compared to the other models. Moreover, the asymmetric theoretical distributions for innovations of EVT models allow the improvement of the accuracy of ES forecasting.

Modeling the News Impact on Volatility to Refugees Movement from LMICs: An APARCH Approach

Political and economic instability, armed conflicts, and weather abnormalities may introduce a high degree of volatility to the refugee movement. Refugee movement volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it differently. APARCH is one of the GARCH extension types of models that are popularly used to capture asymmetric volatility. In this paper, an attempt is made to model the volatility of the yearly refugee movement for the low-and middle-income countries using the APARCH model. The key finding of this study is that GARCH-extension type models indicating past volatility of the annual number of refugees is significant, influencing current volatility. The News Impact Curve is derived from the fitted APARCH model, which confirmed the presence of asymmetry in the volatility of the refugee movement. The News Impact Curve of the models for both groups of countries described that negative shocks of the same magnitude have a larger impact on volatility to refugee movement than positive shocks.

PERAMALAN VOLATILITAS RETURN NILAI TUKAR RUPIAH TERHADAP US DOLLAR MENGGUNAKAN METODE EGARCH, TGARCH, DAN APARCH

Exchange rates play a crucial role among macroeconomic variables, exerting a significant influence on a country's economic landscape. Fluctuations in these rates can impact a nation's stability and economic activities. Consequently, it becomes essential to engage in forecasting endeavors, particularly in predicting the exchange rate of the rupiah against foreign currencies, with a focus on the US dollar. Certain instances in financial data reveal an asymmetric volatility response, often referred to as the leverage effect. To address this challenge, asymmetric GARCH models, including EGARCH, TGARCH, and APARCH, prove instrumental. This research endeavors to identify the most effective model among EGARCH, TGARCH, and APARCH using data pertaining to the rupiah's exchange rate against the US Dollar from March 2, 2020, to June 2, 2022. The findings indicate that the APARCH (1,2) model stands out as the optimal choice for predicting volatility, boasting the smallest AIC value in comparison to its counterparts. As per the research outcomes, volatility witnessed a decline from the initial day to the fourteenth day.

Estimation of Bid Price Ratio by Housing Type Using the APARCH Model

Estimation of Bid Price Ratio by Housing Type Using the APARCH Model

Modeling Asymmetric Volatility: A News Impact Curve Approach

Seasonal production, weather abnormalities, and high perishability introduce a high degree of volatility to potato prices. Price volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it in a dissimilar way. GARCH is a symmetric model, and it cannot capture asymmetric price volatility. EGARCH, APARCH, and GJR-GARCH models are popularly used to capture asymmetric price volatility. In this paper, an attempt is made to model the price volatility of the weekly wholesale modal price of potatoes for the Agra, Ahmedabad, Bengaluru, Delhi, Kolkata, and Mumbai markets using the above-mentioned models. The News Impact Curves (NICs) are derived from the fitted models, which confirmed the presence of asymmetry in the price volatility. To this end, NICs are used to describe the degree of asymmetry in volatility present in different markets.

Countercyclical and time-varying reward to risk and the equity premium

We study whether the equity premium is related to volatility or variance, whether the reward to market risk is positive, and whether it behaves in a counter-cyclical fashion. Using APARCH models for the conditional market risk, we compare the traditional and the new testing approach of Antell and Vaihekoski (2019) on the monthly US equity premium from 1928 to 2018. The results from the new approach give stronger support for the pricing of volatility rather than variance and for positive reward to market risk. The support for timevarying and countercyclical reward to risk coefficient is smaller than previously thought.

COVID19 Outbreak Impact on International Stock Markets Volatility Contagion

We analyze volatility contagion between the U.S. and Chinese stock markets and international capital markets. The volatility is modeled using: GARCH, TARCH, EGARCH, APARCH, IGARCH, FIGARCH, ACGARCH and GAS models under Gaussian, GED and t-Student distributions. 21,000 intraday observations of thirteen markets from January/1st to June/25th 2020 are employed. Once volatility is modeled, the incidence of Chinese and American markets on the rest of the bourses is tested employing Vector Autoregressive Markov Switching Models. Evidence confirms incidence of the Chinese and American capital markets volatility in other markets volatility; common breakpoints and Intermarket incidence in high volatility periods stand out.

Peramalan Volatilitas IHSG dan Estimasi Value-at-Risk Menggunakan Model Student APARCH

This study aim to find the best model to predict daily volatility of IHSG using APARCH model under student-t distribution, estimate value-at-risk measure based on the model and compare it with the value-at-risk measure estimated from GARCH. This study limits the search only on APARCH model class. The sample are IHSG dataset which is derived from yahoo finance, starting from Jan 1st, 2015 to Dec 31th, 2021. The result suggest that value-at-risk measure estimated using student APARCH are more adept to negatif shock than that of GARCH. Nevertheless, the result of value-at-risk back-testing do not show any significant differences between those two.

Long memory and volatility persistence across BRICS stock markets

This study aims to assess the persistence of volatility shocks and long memory in stock market returns and volatility in Brazil, Russia, India, China, and South Africa (BRICS), employing the GARCH, APARCH, ARFIMA, and FIGARCH models from January 2000 to November 2019. The results of GARCH confirm evidence of persistence in volatility shocks, while APARCH indicates the existence of leverage effects in all BRICS stock markets. The results of the ARFIMA and FIGARCH models offer significant indications of long-range dependence in the mean returns and volatility series, posing a challenge to the Efficient Market Hypothesis. The findings have implications for investors, traders, portfolio managers and policymakers who need to understand the varying behavior of stock returns and volatility across BRICS to make valid investment judgments before taking decisions.

Modelling and Forecasting Volatility of Crude Oil Returns in Nigeria based on Six Error Innovations

Modeling price volatility of crude oil (PVCO) is pertinent because of the overbearing impact on any oil-producing economy. This study aimed at evaluating the performance of some volatility models in modeling and forecasting crude oil returns. Utilizing daily returns data from October 23, 2009, to March 23, 2020, this study attempted to capture the dynamics of crude oil price volatility in Nigeria using a symmetric and asymmetric GARCH models. In our research, we considered the generalized autoregressive conditional heteroscedastic model (GARCH), Exponential (E-GARCH), Glosten, Jagannathan and Runkle (GJR-GARCH) and Asymmetric Power (AP-ARCH) under six error innovations that include the skewed variant of the student-t, generalized error and normal distribution. From the results obtained, it was discovered that the AP-ARCH (1, 1) model performed better in the fitting and performance evaluation phase. The skew Student’s t-distribution (SStD) was also reported to be the best performing error innovation in most of the models. Based upon these results, we conclude that the AP-ARCH (1, 1)-SStD model is the best model for capturing the dynamics of crude oil returns in Nigeria.

The impact of COVID-19 on S&P500 sector indices and FATANG stocks volatility: An expanded APARCH model

In this paper we hypothesize that not all stocks and sectors are affected equally by COVID-19 in terms of return volatility. Specifically, we hypothesize that at least some sectors (Information Technology, Consumer Discretionary, Telecom Services, Consumer Staples and Energy) must show statistically significant differences. We analyze eleven SP500 sectors and FATANG stocks, estimating an Asymmetric Power GARCH model including a dummy variable to account for the outbreak. Results reveal an exacerbation of volatility after February 2020 and validate our hypothesis with few exceptions. Based on a likelihood ratio test, the null hypothesis is rejected in most cases in favor of our APARCH(1, 1).

Choosing the Best Performing Garch Model for Sri Lanka Stock Market by Non-Parametric Specification Test

This paper examines the performance of different kind of GARCH models with Gaussian, Student-t and generalized error distribution for Colombo Stock Exchange (CSE), in Sri Lanka. Analyzing the daily closing price index of CSE from January 02, 2007 to March 10, 2013. It was found that the Asymmetric GARCH models give better result than symmetric GARCH model. According to distributional assumption these models under Student-t as well as generalized error provided better fit than normal distributional assumption. The Non-Parametric Specification test suggest that the GARCH, EGARCH, TARCH and APARCH models with Student-t distributional assumption are the most successful model for CSE.

ANALYSIS OF THE DEPENDENCE STRUCTURE BETWEEN MINERALS PRICES AND THE RAND/USD EXCHANGE RATE USING COPULAS

Copula functions are flexible tools for modelling the dependence structure between variables. In this research, we extend the literature on currency-commodity relationship using copulas. We examine the dependence structure between gold, platinum mineral prices and RAND/USD exchange rate. ARMA (1, 1)-EGARCH (1, 1) and ARMA(1, 1)-APARCH (1, 1) models under different error terms including normal, student-t and skewed student-t were fitted to the returns of commodity prices and the exchange rate. Constants and time varying copulas were then employed to examine the commodity-currency dependence structure. The results show evidence of a positive, strong dependence between gold, platinum prices and the RAND/USD exchange rate. The analysis relies on Clayton, rotated Clayton, Student-t, Gumbel, rotated Gumbel, Plackett and Joe Clayton copulas and provide an indication of leverage effects. The results of the time varying Normal copula indicate that fluctuations in gold and platinum prices generate Rand/USD volatility.

Rolling window VaR: an EVT approach

In this study, a method of estimating value-at-risk is proposed. This method combines elements of extreme value theory (EVT), the APARCH model (Ding et al. 1993) and the rolling window method. The research was conducted using 20 stock market indexes worldwide during 2006-2019. Value-at-risk was estimated via 12 competing models which were evaluated using 5 tests. The back testing results indicate that the best model was the one which takes into consideration the asymmetric character of financial data (APARCH with skewed normal distribution), the Generalized Pareto Distribution for modeling the tail of the financial returns distribution and the rolling window approach. The methodologies discussed in this paper could provide a useful tool for both financial entities and regulatory authorities.

Forecasting of Global Market Prices of Major Financial Instruments

One of the easiest and fastest ways of building a healthy financial future is investing in the global market. However, the prices of the global market are highly volatile due to the impact of economic crises. Therefore, future prediction and comparison lead traders to make the low-risk decisions with price. The present study is based on time series modelling to forecast the daily close price values of financial instruments in the global market. The forecasting models were tested with two sample sizes, namely, 5-year close price values for correlation analysis and 3-year close price values for model building from 2013 January to 2018 January. The forecasting capabilities were compared for both ARIMA and GARCH class models, namely, TGARCH, APARCH, and EGARCH. The best-fitting model was selected based on the minimum value of the Akaike information criterion (AIC) and Bayesian information criteria (BIC). Finally, the comparison was carried out between ARIMA and GARCH class models using the measurement of forecast errors, based on the Root Mean Square Deviation (RMSE), Mean Absolute Error (MAE), and Mean absolute percentage error (MAPE). The GARCH model was the best-fitted model for Australian Dollar, Feeder cattle, and Coffee. The APARCH model provides the best out-of-sample performance for Corn and Crude Oil. EGARCH and TGARCH were the better-fitted models for Gold and Treasury bond, respectively. GARCH class models were selected as the better models for forecasting than the ARIMA model for daily close price values in global financial market instruments.

PERAMALAN VOLATILITAS RETURN SAHAM MENGGUNAKAN METODE ASYMMETRIC POWER ARCH (APARCH)

Model APARCH is one of the asymmetric GARCH models. These models are able to capture the incidence of good news and bad news in the volatility. The APARCH model has an asymmetric coefficient to cope with leverage effect by modeling a leverage that has heteroscedasticity and asymmetric effect condition. The results of this research were obtained by the appropriate APARCH model. The model is the APARCH(1,2) model because all parameters are significant. Thus, proceeds from the volatility of stock return for the next 14 days with the model volatility APARCH(1,2) increased from period one to period fourteen.

Volatility specifications versus probability distributions in VaR forecasting

AbstractWe provide evidence suggesting that the assumption on the probability distribution for return innovations is more influential for value‐at‐risk (VaR) performance than the conditional volatility specification. We also show that some recently proposed asymmetric probability distributions and the APARCH and FGARCH volatility specifications beat more standard alternatives for VaR forecasting, and they should be preferred when estimating tail risk. The flexibility of the free power parameter in conditional volatility in the APARCH and FGARCH models explains their better performance. Indeed, our estimates suggest that for a number of financial assets the dynamics of volatility should be specified in terms of the conditional standard deviation. We draw our results on VaR forecasting performance from (i) a variety of backtesting approaches, (ii) the model confidence set approach, as well as (iii) establishing a ranking among alternative VaR models using a precedence criterion that we introduce in this paper.

Genelleştirilmiş Otoregresif Koşullu Değişen Varyans Modelleri İle Bitcoin Volatilitesinin Analizi (Analysis of Bitcoin Volatility with Generalized Autoregressive Conditional Heteroskedastic Models)

Purpose – In the last decade, digital innovations in the field of finance emerge mainly due to Blockchain technology. The most widely used product of Blockchain technology in the world is cryptocurrencies. Bitcoin draws attention to both market capitalization and transaction volume. The analysis of the volatility of Bitcoin has great significance both theoretically and practically. Therefore, the research aims to determine the most suitable volatility model in Bitcoin investment analysis and present a model suggestion that can be used for predicting the future. Design/methodology/approach – In this study, daily logarithmic return series between 29.04.2013-17.04.2019 are used to analyze Bitcoin volatility. While calculating the return series, daily closing prices of Bitcoin are based on. As a research method, generalized autoregressive conditional heteroskedastic (GARCH) models are tested. In this study, ARCH, GARCH, GJR / TARCH, EGARCH, APARCH, and CGARCH type models are compared for volatility modeling of Bitcoin logarithmic return series. Findings – The study found that the best method for the analysis of Bitcoin volatility is the EGARCH model. Another important finding of the study is that negative shocks are more effective on Bitcoin returns than positive shocks. Discussion – The study contributes to the literature in terms of analyzing the dynamics of Bitcoin volatility, which is a new and digital product, with various models and current data set. Bitcoin investors and researchers can predict returns by using the volatility equation in result of this research. Another output of the study is that negative news effects Bitcoin returns more than positive news. Using this information, traders will be able to take positions on Bitcoin prices.