Journal articles: 'Aparchai'

1

Blok, Josine. "Sharing with the Gods. Aparchai and Dekatai in Ancient Greece, written by Jim, Th.S.F." Mnemosyne 70, no. 1 (January 20, 2017): 167–69. http://dx.doi.org/10.1163/1568525x-12342283.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Wagner-Hasel, Beate. "GIFTS FOR THE GODS - T.S.F. Jim Sharing with the Gods. Aparchai and Dekatai in Ancient Greece. Pp. xvi + 373, ills. Oxford: Oxford University Press, 2014. Cased, £80, US$150. ISBN: 978-0-19-870682-3." Classical Review 66, no. 2 (July 20, 2016): 468–70. http://dx.doi.org/10.1017/s0009840x16001177.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Jim, Theodora Suk Fong. "APARCHAIIN THE GREAT LIST OF THASIANTHEÔROI." Classical Quarterly 64, no. 1 (April 16, 2014): 13–24. http://dx.doi.org/10.1017/s0009838813000505.

Full text

Abstract:

One of the most baffling inscriptions has come down to us from the so-called ‘Passage of theTheôroi’ at Thasos. Situated at the north-eastern entrance of the ancient agora, and consisting originally of two walls on either side of a path paved by marble, the monumental passage way had a long list of names inscribed on the inside of its western wall; this is the so-called ‘great list of Thasiantheôroi’. Two of its constituent lists bear the headings ἐπὶ τῆς πρώτης ἀπαρχῆς and ἐπὶ τῆς δευ[τέρη]ς ἀπαρχῆς| οἵδε ἐθεόρεον. The meaning of the wordaparchêand the nature of thetheôroiin question have been the subject of disagreement among historians. The aim of this article is to contribute further suggestions to existing discussions.

APA, Harvard, Vancouver, ISO, and other styles

4

SIDADADOLOG, JUITA HARYATI, I. WAYAN SUMARJAYA, and NI KETUT TARI TASTRAWATI. "PERAMALAN VOLATILITAS RETURN SAHAM MENGGUNAKAN METODE ASYMMETRIC POWER ARCH (APARCH)." E-Jurnal Matematika 9, no. 3 (September 2, 2020): 157. http://dx.doi.org/10.24843/mtk.2020.v09.i03.p293.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

Aurora Luque. "Aparcar es difícil: –Road movie–." Sirena: poesia, arte y critica 2008, no. 2 (2008): 16. http://dx.doi.org/10.1353/sir.0.0031.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Hidayatullah, Syarif, and Mohammad Farhan Qudratullah. "Analisis Risiko Investasi Saham Syariah Dengan Model Value AT Risk-Asymmetric Power Autoregressive Conditional Heterocedasticity (VaR-APARCH)." Jurnal Fourier 6, no. 1 (April 4, 2017): 37. http://dx.doi.org/10.14421/fourier.2017.61.37-43.

Full text

Abstract:

Penelitian ini membahas analisis risiko data runtun waktu dengan model Value at Risk- Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH)dalam pasar modal syariah. Metode yang digunakan dalam penelitian ini adalah penerapan kasus.Data yang digunakan adalah harga penutupan harian saham dalam Jakarta Islamic Index (JII)periode 4 Maret 2013 sampai 8 April 2015.Model APARCH yang dipilih berdasarkan nilai Schwarz Criterion (SC).Langkah-langkah dalam penelitian ini adalah menguji kestasioneran data, mengidentifikasi model ARIMA,mengestimasi parameter model ARIMA, menguji diagnostik model ARIMA, mendeteksi ada tidaknya unsur ARCH atau unsur heteroskedastisitas, uji asimetris data saham, mengestimasi model APARCH, menguji diagnostik model APARCH, dan menghitung risiko dengan VaR-APARCH.Model terbaik yang dipilih adalah ARIMA ((3),0,0) dan APARCH (1,1). Model ini valid untuk menganalisis besar risiko investasi dalam jangka waktu 10 hari ke depan.

APA, Harvard, Vancouver, ISO, and other styles

7

Augustine Kutu, Adebayo, and Harold Ngalawa. "Exchange rate volatility and global shocks in Russia: an application of GARCH and APARCH models." Investment Management and Financial Innovations 13, no. 4 (December 29, 2016): 203–11. http://dx.doi.org/10.21511/imfi.13(4-1).2016.06.

Full text

Abstract:

This study examines global shocks and the volatility of the Russian rubble/United States dollar exchange rate using the symmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH), and Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) models. The GARCH and APARCH are employed under normal (Normal Gaussian) and non-normal (Student’s t and Generalized Error) distributions. Using monthly exchange rate data covering January 1994 – December 2013, the study finds that the symmetric (GARCH) model has the best fit under the non-normal distribution, which improves the overall estimation for measuring conditional variance. Conversely, the APARCH model does not show asymmetric response in exchange rate volatility and global shocks, resulting in no presence of leverage effect. The GARCH model under the Student’s t distribution produces better fit for estimating exchange rate volatility and global shocks in Russia, compared to the APARCH model. Keywords: exchange rate volatility, global Shocks, GARCH and APARCH models. JEL Classification: F30, F31, P33

APA, Harvard, Vancouver, ISO, and other styles

8

Kasse, Irwan, Andi Mariani, Serly Utari, and Didiharyono D. "Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model." JTAM (Jurnal Teori dan Aplikasi Matematika) 5, no. 1 (April 17, 2021): 1. http://dx.doi.org/10.31764/jtam.v5i1.3220.

Full text

Abstract:

Investment can be defined as an activity to postpone consumption at the present time with the aim to obtain maximum profits in the future. However, the greater the benefits, the greater the risk. For that we need a way to predict how much the risk will be borne. Modelling data that experiences heteroscedasticity and asymmetricity can use the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. This research discusses the time series data risk analysis using the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model using the daily closing price data of Bitcoin USD period January 1 2019 to 31 December 2019. The best APARCH model was chosen based on the value of Akaike's Information Criterion (AIC). From the analysis results obtained the best model, namely ARIMA (6,1,1) and APARCH (1,1) with the risk of loss in the initial investment of IDR 100,000,000 in the next day IDR 26,617,000. The results of this study can be used as additional information and apply knowledge about the risk of investing in Bitcoin with the VaR-APARCH model.

APA, Harvard, Vancouver, ISO, and other styles

9

Aparci, Mustafa, and Omer Uz. "Treatment solution by Aparci and Uz." Interactive CardioVascular and Thoracic Surgery 22, no. 5 (April 25, 2016): 699.2–700. http://dx.doi.org/10.1093/icvts/ivw100.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Laurent, Sébastien. "Analytical Derivates of the APARCH Model." Computational Economics 24, no. 1 (August 2004): 51–57. http://dx.doi.org/10.1023/b:csem.0000038851.72226.76.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Ogutu, Carolyn, Betuel Canhanga, and Pitos Biganda. "Modeling Exchange Rate Volatility using APARCH Models." Journal of the Institute of Engineering 14, no. 1 (June 4, 2018): 96–106. http://dx.doi.org/10.3126/jie.v14i1.20072.

Full text

Abstract:

ARCH (Autoregressive Conditional Heteroskedacity) and GARCH (Generalized Autoregressive Conditional Heteroskedacity) models have been used in forecasting fluctuations in exchange rates, commodities and securities and are appropriate for modeling time series in which there is non-constant variance, and in which the variance at one time period is dependent on the variance at a previous time period. In our paper we deal with APARCH models (Arithmetic Power Autoregressive Conditional Heteroskedasticity) in order to fit into a data series with asymmetric characteristics. We use Kenyan, Tanzanian and Mozambican data and perform the time series analysis and obtain a model that characterize the data set under consideration. Journal of the Institute of Engineering, 2018, 14(1): 96-106

APA, Harvard, Vancouver, ISO, and other styles

12

Irene, Yanne, Madona Yunita Wijaya, and Aisyah Muhayani. "World Gold Price Forecast using APARCH, EGARCH and TGARCH Model." InPrime: Indonesian Journal of Pure and Applied Mathematics 2, no. 2 (May 31, 2020): 71–78. http://dx.doi.org/10.15408/inprime.v2i2.14779.

Full text

Abstract:

AbstractInvestment is a process of investing money for profit or material result. One investment commodity is gold. Gold is a precious metal in which the value tends to fluctuate over time. This indicates that there is a non-constant variance called heteroscedasticity. The appropriate time-series model to solve this heteroscedasticity problem is ARCH/GARCH. However, this model can't be applied for the financial cases that have an asymmetric effect (the downward and increase tendency in the level of volatility when returns rise and vice versa). Therefore, in this research, we forecast the world gold prices using APARCH, EGARCH, and TGARCH methods. We use the monthly world gold price data from June 1993 until May 2018. The result shows that the best-fitted model to forecasting the world gold prices is EGARCH (1.1). This model has the smallest error than the other models with a Mean Absolute Percentage Error (MAPE) value of 4.66%.Keywords: return; volatilities; heteroscedasticity; asymmetric effect; APARCH; EGARCH; TGARCH. AbstrakInvestasi adalah proses menginvestasikan uang untuk keuntungan atau hasil material. Salah satu komoditas investasi adalah emas. Emas adalah logam mulia yang nilainya cenderung berfluktuasi dari waktu ke waktu. Ini menunjukkan bahwa ada varian non-konstan yang disebut heteroskedastisitas. Metode deret waktu yang tepat untuk menyelesaikan masalah ini adalah ARCH/GARCH. Namun model ini tidak dapat digunakan untuk kasus keuangan yang memiliki efek asimetris (kecenderungan menurun dan meningkatnya volatilitas ketika nilai return naik dan sebaliknya). Oleh karena itu, dalam penelitian ini, kami memprediksi harga emas dunia menggunakan metode APARCH, EGARCH, dan TGARCH dengan data harga emas dunia bulanan pada bulan Juni 1993 - Mei 2018. Hasilnya menunjukkan bahwa, di antara ketiga metode itu, model terbaik untuk memprediksi harga emas dunia adalah EGARCH (1.1) dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 4,66%.Kata kunci: return; volatilitas; heteroskedastisitas; efek asimetris; APARCH; EGARCH; TGARCH.

APA, Harvard, Vancouver, ISO, and other styles

13

AL-ZOUBI, HAITHAM A., and AKTHAM I. MAGHYEREH. "THE RELATIVE RISK PERFORMANCE OF ISLAMIC FINANCE: A NEW GUIDE TO LESS RISKY INVESTMENTS." International Journal of Theoretical and Applied Finance 10, no. 02 (March 2007): 235–49. http://dx.doi.org/10.1142/s0219024907004184.

Full text

Abstract:

We examine the relative risk performance of the Dow Jones Islamic Index (DJIS) and find that the index outperforms the Dow Jones (DJIM) WORLD Index in terms of risk. Using the most recent Value-at-Risk (VaR) methodologies (RiskMetrics, Student-t APARCH, and skewed Student-t APARCH) on the 1996–2005 period, and assuming one-day holding period for both indices with a moving window of 500 day data, we show that the value of VaR is greater for DJIM WORLD than for DJIS Islamic. We interpret the results mainly to the profit-and-loss sharing principle of Islamic finance where banks share the profits and bear losses (Mudarabah) or share both profits and losses (Musharaka) with the firm.

APA, Harvard, Vancouver, ISO, and other styles

14

Rusu, Andrei. "Rolling window VaR: an EVT approach." Virgil Madgearu Review of Economic Studies and Research 13, no. 2 (October 23, 2020): 147–68. http://dx.doi.org/10.24193/rvm.2020.13.66.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

15

Setiawan, E., N. Herawati, and K. Nisa. "Modeling Stock Return Data using Asymmetric Volatility Models : A Performance Comparison based on the Akaike Information Criterion and Schwarz Criterion." Journal of Engineering and Scientific Research 1, no. 1 (June 1, 2019): 40. http://dx.doi.org/10.23960/jesr.v1i1.9.

Full text

Abstract:

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) modelhas been widely used in time series forecasting especially with asymmetricvolatility data. As the generalization of autoregressive conditionalheteroscedasticity model, GARCH is known to be more flexible to lag structures.Some enhancements of GARCH models were introduced in literatures, among themare Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) andAsymmetric Power GARCH (APGARCH) models. This paper aims to compare theperformance of the three enhancements of the asymmetric volatility models bymeans of applying the three models to estimate real daily stock return volatilitydata. The presence of leverage effects in empirical series is investigated. Based onthe value of Akaike information and Schwarz criterions, the result showed that thebest forecasting model for daily stock return data is the APARCH model.Keywords: Volatility, GARCH, TGARCH, EGARCH, APARCH, AIC and SC.

APA, Harvard, Vancouver, ISO, and other styles

16

Tan, Bin. "Estimation of Value-at-Risk Based on ARFIMA-FIAPARCH-SKST Model." Advanced Materials Research 601 (December 2012): 464–69. http://dx.doi.org/10.4028/www.scientific.net/amr.601.464.

Full text

Abstract:

This paper focus mainly on some important stylized facts in financial market, such as long memory, asymmetry and leverage effect, and so on, and apply ARFIMA-APARCH-SKST model to measure dynamic Value at Risk, at the same time, ARMA-EGARCH(APARCH)-SKST, ARFIMA- FIEGARCH-SKST are used to compare empirical effect of different risk model, at last, we apply LRT method to test accuracy of risk model. Our results indicate that all models used in this paper can measure dynamic VaR at 95%, 99% and 99.5% confidence levels, and there is no significant difference for different risk model for different stock markets. Moreover, we find also that long memory is not more valuable stylized fact than asymmetry for SSEC and S&P500.

APA, Harvard, Vancouver, ISO, and other styles

17

Li, Yushu. "Estimating and Forecasting APARCH-Skew-tModel by Wavelet Support Vector Machines." Journal of Forecasting 33, no. 4 (March 14, 2014): 259–69. http://dx.doi.org/10.1002/for.2275.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Mighri, Zouheir, and Faysal Mansouri. "Modeling international stock market contagion using multivariate fractionally integrated APARCH approach." Cogent Economics & Finance 2, no. 1 (November 11, 2014): 963632. http://dx.doi.org/10.1080/23322039.2014.963632.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Chai, Shanglei, Zhen Zhang, Mo Du, and Lei Jiang. "Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach." Complexity 2020 (December 10, 2020): 1–18. http://dx.doi.org/10.1155/2020/8872307.

Full text

Abstract:

Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.

APA, Harvard, Vancouver, ISO, and other styles

20

Uwilingiyimana, Charline, Abdou Kâ Diongue, and Carlos Ogouyandjou. "Adaptive Hyperbolic Asymmetric Power ARCH (A-HY-APARCH) model: Stability and Estimation." Afrika Statistika 15, no. 4 (October 1, 2020): 2511–28. http://dx.doi.org/10.16929/as/2020.2511.170.

Full text

Abstract:

In this paper, a new asymmetric GARCH type model that generalizes the Hyperbolic Asymmetric Power ARCH (HY-APARCH) process is proposed. The proposed model takes into consideration some characteristics of financial time series data like volatility clustering, long memory and structural changes. The necessary and sufficient conditions for the asymptotic stability of the model are derived and parameter estimation methods are proposed. The Monte Carlo Simulations are done to prove the performance of the estimation method

APA, Harvard, Vancouver, ISO, and other styles

21

Stoupos, Nikolaos, and Apostolos Kiohos. "Post-communist countries of the EU and the euro: Dynamic linkages between exchange rates." Acta Oeconomica 67, no. 4 (December 2017): 511–38. http://dx.doi.org/10.1556/032.2017.67.4.2.

Full text

Abstract:

The sovereign debt crisis of 2010 in the euro area significantly decelerated the monetary integration of the EU. The main purpose of this paper is to explore whether five post-communist member states of the EU are mature enough to adopt the euro. We used nominal exchange rates in the error correction model with asymmetric power ARCH (ECM-APARCH). Our results highlight that EU membership positively increased the impact of the euro on the currency of each of these countries in the short-run. In contrast, the long-term effect of the euro on each currency is negative for the Czech Republic, Hungary, and Croatia. Wholly different results were obtained for Poland and Romania. The APARCH model showed that the negative responses of the euro had a greater or neutral effect on the conditional variance of each currency instead of the positive responses. The debt crisis of the euro area had no impact on the dynamic linkages between the currencies. Our research concludes that Croatia, the Czech Republic, and Hungary are not ready to join the euro area in the near future. On the other hand, the currencies of Poland and Romania are already aligned with the fluctuations of the euro.

APA, Harvard, Vancouver, ISO, and other styles

22

Cortes Garcia, Christian Camilo, and Alvaro Javier Cangrejo Esquivel. "Propuesta de un modelo de volatilidad a los precios de cierre en las acciones CÉMEX LATAM HOLDINGS durante el periodo 15/noviembre/2012 al 27/octubre/2017." Ingeniería y Región 19 (June 30, 2018): 22–34. http://dx.doi.org/10.25054/22161325.1463.

Full text

Abstract:

En este trabajo se presenta un modelo de volatilidad que explique los retornos de precios de cierre diarios de las acciones CLH de la compañía multinacional para la industria de la construcción CEMEX, tomando como referencia la volatilidad con ventana móvil 20 datos y modelos de volatilidad condicional tales como los GARCH, TGARCH, IGARCH, EGARCH y APARCH. El modelo que mejor explica la volatilidad condicional de los retornos es el IGARCH(1,1).

APA, Harvard, Vancouver, ISO, and other styles

23

Freitas, Clailton Ataídes de, and Thelma Sáfadi. "Volatilidade dos Retornos de Commodities Agropecuárias Brasileiras: um teste utilizando o modelo APARCH." Revista de Economia e Sociologia Rural 53, no. 2 (June 2015): 211–28. http://dx.doi.org/10.1590/1234-56781806-9479005302002.

Full text

Abstract:

Resumo:Este estudo analisou (2005-2013) a persistência, a alavancagem e a variância incondicional dos retornos de commodities agropecuárias3. Assim, recorreu-se ao modelo denominado APARCH. As estimativas apontaram que a alavancagem não foi confirmada nessas séries; a variância condicional foi assimétrica nos retornos do etanol, do café, do algodão, do boi gordo e do bezerro; as volatilidades mais intensas, embora com convergência às suas médias históricas, ocorreram nos retornos do açúcar, da soja, do café, do trigo, do frango e do boi gordo; as maiores volatilidades incondicionais foram dos retornos do etanol, do frango, do algodão, da soja e do açúcar.

APA, Harvard, Vancouver, ISO, and other styles

24

Araújo, Breno Valente Fontes, Marcos Antônio de Camargos, and Frank Magalhães de Pinho. "Modeling conditional volatility by incorporating non-regular trading hours into the APARCH model." Revista Contabilidade & Finanças 30, no. 80 (August 2019): 202–15. http://dx.doi.org/10.1590/1808-057x201806100.

Full text

Abstract:

ABSTRACT This study aims to evaluate how the after-market and pre-opening periods affect the estimation of conditional volatility one day ahead. Volatility features quite a lot in Finance studies because it is a fundamental parameter in derivatives pricing, the efficient allocation of portfolios, and risk management. The results are relevant for investment agents to be able to refine volatility forecasting models and achieve better results in derivatives pricing, risk management, and portfolio optimization. We used the asymmetric power autoregressive conditional heteroscedasticity (APARCH) model, incorporating the after-market, pre-opening, and total overnight periods to assess whether they contain important information for modeling volatility. We analyzed the 20 stocks of Brazilian companies listed on the São Paulo Stock, Commodities, and Futures Exchange (BM&FBovespa) and also belonging to the BR Titans 20 with ADRs listed on the New York Stock Exchange and the Nasdaq. The results were evaluated in-sample using the corrected Akaike information criterion (AICc) and the statistical significance of the coefficients, and out-of-sample using root mean squared error (RMSE), mean absolut percentage error (MAPE), the R² of the Mincer-Zarnowitz regression, and the Diebold Mariano test. The analysis does not enable it to be claimed which is the best model, because there is no unanimity among all the stocks; however, non-regular trading hours were shown to incorporate important information for most of the stocks. Furthermore, the models that incorporated the pre-opening period generally obtained superior results to the models that incorporated the after-market period, demonstrating that this period contains important information for forecasting conditional volatility.

APA, Harvard, Vancouver, ISO, and other styles

25

Thorlie, Milton Abdul, Lixin Song, Muhammad Amin, and Xiaoguang Wang. "Modeling and forecasting of stock index volatility with APARCH models under ordered restriction." Statistica Neerlandica 69, no. 3 (February 18, 2015): 329–56. http://dx.doi.org/10.1111/stan.12062.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Conrad, Christian, Menelaos Karanasos, and Ning Zeng. "Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study." Journal of Empirical Finance 18, no. 1 (January 2011): 147–59. http://dx.doi.org/10.1016/j.jempfin.2010.05.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Gunay, Samet, and Audil Khaki. "Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models." Journal of Risk and Financial Management 11, no. 2 (June 9, 2018): 30. http://dx.doi.org/10.3390/jrfm11020030.

Full text

Abstract:

Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot markets for risk managers. Capturing conditional distribution, fat tails and price spikes properly is crucial to the correct measurement of risk. This paper is an attempt to model volatility of energy futures under different distributions. In empirical analysis, we estimate the volatility of Natural Gas Futures, Brent Oil Futures and Heating Oil Futures through GARCH and APARCH models under gev, gat and alpha-stable distributions. We also applied various VaR analyses, Gaussian, Historical and Modified (Cornish-Fisher) VaR, for each variable. Results suggest that the APARCH model largely outperforms the GARCH model, and gat distribution performs better in modeling fat tails in returns. Our results also indicate that the correct volatility level, in gat distribution, is higher than those suggested under normal distribution with rates of 56%, 45% and 67% for Natural Gas Futures, Brent Oil Futures and Heating Oil Futures, respectively. Implemented VaR analyses also support this conclusion. Additionally, VaR test results demonstrate that energy futures display riskier behavior than S&P 500 returns. Our findings suggest that for optimum risk management and trading strategies, risk managers should consider alternative distributions in their models. According to our results, prices in energy markets are wilder than the perception of normal distribution. In this regard, regulators and policy makers should enhance transparency and competitiveness in the energy markets to protect consumers.

APA, Harvard, Vancouver, ISO, and other styles

28

Silva, Carlos Alberto Gonçalves da. "O efeito assimétrico na volatilidade dos preços do etanol no Estado de São Paulo: uma aplicação do modelo Asymmetric power autoregressive conditional heteroskedasticity." Revista Brasileira de Administração Científica 12, no. 2 (December 2, 2020): 1–12. http://dx.doi.org/10.6008/cbpc2179-684x.2021.002.0001.

Full text

Abstract:

O presente artigo examina a volatilidade dos retornos semanais dos preços do etanol hidratado por meio do modelo de variância condicional, também chamado heteroscedástico. A análise compreende o período de 29 de novembro de 2002 a 20 de novembro de 2020. Os resultados empíricos demonstraram as reações de persistência e assimetria na variância dos respectivos retornos, ou seja, boas e más notícias impactam diferentemente sobre a volatilidade dos retornos de acordo com o modelo ARMA(1,1)- APARCH (1,1), com distribuição generalizada de erro, do ponto de vista da realização de previsões.

APA, Harvard, Vancouver, ISO, and other styles

29

Divisekara, Roshani W., Ruwan D. Nawarathna, and Lakshika S. Nawarathna. "Forecasting of Global Market Prices of Major Financial Instruments." Journal of Probability and Statistics 2020 (September 14, 2020): 1–11. http://dx.doi.org/10.1155/2020/1258463.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

30

Sousa, Thiago R., Cira E. Otiniano, and Silvia R. Lopes. "A note about the delta-moment in ARMA-APARCH models with stable conditional distributions and GEV." Selecciones Matemáticas 5, no. 1 (June 30, 2018): 7–16. http://dx.doi.org/10.17268/sel.mat.2018.01.02.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Bagchi, Bhaskar. "Volatility spillovers between crude oil price and stock markets: evidence from BRIC countries." International Journal of Emerging Markets 12, no. 2 (April 18, 2017): 352–65. http://dx.doi.org/10.1108/ijoem-04-2015-0077.

Full text

Abstract:

Purpose The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times. Design/methodology/approach The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects. Findings For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries. Originality/value The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

APA, Harvard, Vancouver, ISO, and other styles

32

Rutkowska-Ziarko, Anna, and Przemysław Garsztka. "Assessing the Efficiency of Investment Fund Management Using Quantile Risk Measures." Olsztyn Economic Journal 11, no. 3 (September 30, 2016): 277–98. http://dx.doi.org/10.31648/oej.2933.

Full text

Abstract:

The aim of the research is to compare the efficiency of managing selected Polish investment funds in various phases of stock market condition. The Value at Risk (VaR) and Conditional Value at Risk (CVaR) is used to construct efficiency ratios of fund management. Those funds investing in financial instruments have the most stable expected rate of return and the lowest risk, in all the analysed periods which made them highly effective. The article also discusses the alternative methods to VaR and CVaR estimation which are used in the study. It is noted VaR and CVaR estimates obtained using backtesting and using APARCH models give similar results.

APA, Harvard, Vancouver, ISO, and other styles

33

Rico Belda, Paz. "No linealidad y asimetría en el proceso generador del Índice Ibex35." Studies of Applied Economics 31, no. 2 (March 29, 2020): 555. http://dx.doi.org/10.25115/eea.v31i2.3340.

Full text

Abstract:

This paper analyzes the behaviour of Ibex35 from January 1999 to December 2001, in order to check if it follows a different process from random walk so its return is not a white noise and it can be predictable, against the efficient market hypothesis. For that, a nonlinear generating process of return will be considered and a STAR-APARCH model will be specified. This model allows a nonlinear behaviour in the conditional mean and in the conditional variance. The empirical results show that the Ibex35 follows a nonlinear and asymmetric process, both in the conditional mean as in the conditional variance, so the weak-version of efficient market hypothesis is rejected.

APA, Harvard, Vancouver, ISO, and other styles

34

Krężołek, Dominik. "Selected GARCH‑type Models in the Metals Market – Backtesting of Value‑at‑Risk." Acta Universitatis Lodziensis. Folia Oeconomica 5, no. 331 (January 19, 2018): 185–203. http://dx.doi.org/10.18778/0208-6018.331.12.

Full text

Abstract:

Risk analysis in the financial market requires the correct evaluation of volatility in terms of both prices and asset returns. Disturbances in quality of information, the economic and political situation and investment speculations cause incredible difficulties in accurate forecasting. From the investor’s point of view, the key issue is to minimise the risk of huge losses. This article presents the results of using some selected GARCH‑type models, ARMA‑GARCH and ARMA‑APARCH, in evaluating volatility of asset returns in the metals market. To assess the level of risk, the Value‑at‑Risk measure is used. The comparison between real and estimated losses (in terms of VaR) is made using the backtesting procedure.

APA, Harvard, Vancouver, ISO, and other styles

35

Silva, Carlos Alberto Gonçalves da. "A Volatilidade e assimetria dos preços das ações Banco do Brasil: Uma abordagem do modelo APARCH." Brazilian Journal of Business 2, no. 3 (2020): 2668–3683. http://dx.doi.org/10.34140/bjbv2n3-055.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Nugroho, Didit B., Bambang Susanto, and Saragah R. Prathama. "Estimation of Exchange Rate Volatility using APARCH-type Models: A Case Study of Indonesia (2010–2015)." Jurnal Ekonomi dan Ekonomi Studi Pembangunan 9, no. 1 (March 5, 2017): 65–75. http://dx.doi.org/10.17977/um002v9i12017p065.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Cortes Garcia, Christian Camilo, and Alvaro Javier Cangrejo Esquivel. "Modelo de volatilidad en un mercado financiero colombiano." Comunicaciones en Estadística 11, no. 2 (December 21, 2018): 191–218. http://dx.doi.org/10.15332/2422474x.3841.

Full text

Abstract:

En este trabajo se presenta una breve introducción a los instrumentos estadísticos y modelos necesarios para explicar la volatilidad de los precios de activos, al seguir la metodología de series temporales e involucrar el efecto de heterocedasticidad condicional. Con estos lineamientos definidos, se modela la volatilidad de los retornos en los precios de cierre diarios de acciones de la empresa colombiana de Cementos Argos S.A al tomar como referencia los modelos ARCH, GARCH, TGARCH, IGARCH, EGARCH, APARCH y SV$t$-AR(1) con el fin de determinar la efectividad de los modelos por fuera de la muestra. El modelo que mejor explica la volatilidad condicional de los retornos es el EGARCH(1,1) y el modelo que mejor realiza pronósticos de volatilidad es el SVt-AR(1).

APA, Harvard, Vancouver, ISO, and other styles

38

Duppati, Geeta, Anoop S. Kumar, Frank Scrimgeour, and Leon Li. "Long memory volatility in Asian stock markets." Pacific Accounting Review 29, no. 3 (August 7, 2017): 423–42. http://dx.doi.org/10.1108/par-02-2016-0009.

Full text

Abstract:

Purpose The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory. Design/methodology/approach This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory. Findings Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts. Practical implications The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management. Social implications It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks. Originality/value This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

APA, Harvard, Vancouver, ISO, and other styles

39

Bagchi, Bhaskar. "Volatility spillovers between exchange rates and Indian stock markets in the post-recession period: an APARCH approach." International Journal of Monetary Economics and Finance 9, no. 3 (2016): 225. http://dx.doi.org/10.1504/ijmef.2016.078395.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Pasha, G. R., Tahira Qasim, and Muhammad Aslam. "Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models." LAHORE JOURNAL OF ECONOMICS 12, no. 2 (July 1, 2007): 115–49. http://dx.doi.org/10.35536/lje.2007.v12.i2.a6.

Full text

Abstract:

In this paper we compare the performance of different GARCH models such as GARCH, EGARCH, GJR and APARCH models, to characterize and forecast financial time series volatility in Pakistan. The comparison is carried out by comparing symmetric and asymmetric GARCH models with normal and fat-tailed distributions for the innovations, over short and long forecast horizons. The forecasts are evaluated according to a set of statistical loss functions. Daily data on the Karachi Stock Exchange (KSE) 100 index are analyzed. The empirical results demonstrate that the use of asymmetry in the GARCH models and the assumption of fat-tail distributions for the innovations improve the volatility forecasts. Overall, EGARCH fits the best while the GJR model, with both normal and non-normal innovations, seems to provide superior forecasting ability over short and long horizons.

APA, Harvard, Vancouver, ISO, and other styles

41

Setiawan, Eri, Netti Herawati, and Khoirin Nisa. "Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion." INSIST 3, no. 2 (October 20, 2018): 160. http://dx.doi.org/10.23960/ins.v3i2.160.

Full text

Abstract:

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used in time series forecasting especially with asymmetric volatility data. As the generalization of autoregressive conditional heteroscedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH models were introduced in literatures, among them are Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Asymmetric Power GARCH (APGARCH) models. This paper aims to compare the performance of the three enhancements of the asymmetric volatility models by means of applying the three models to estimate real daily stock return volatility data. The presence of leverage effects in empirical series is investigated. Based on the value of Akaike information and Schwarz criterions, the result showed that the best forecasting model for our daily stock return data is the APARCH model.

APA, Harvard, Vancouver, ISO, and other styles

42

Ceretta, Paulo Sérgio, Fernanda Galvão De Barba, Kelmara Mendes Vieira, and Fernando Casarin. "Previsão da volatilidade intradiária: análise das distribuições alternativas." Brazilian Review of Finance 9, no. 2 (July 8, 2011): 209. http://dx.doi.org/10.12660/rbfin.v9n2.2011.2586.

Full text

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 estimate volatility. 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.

APA, Harvard, Vancouver, ISO, and other styles

43

Zeghdoudi, Halim, and Madjda Amrani. "On Mixture GARCH Models: Long, Short Memory and Application in Finance." Journal of Mathematics and Statistics Studies 2, no. 2 (June 24, 2021): 01–07. http://dx.doi.org/10.32996/jmss.2021.2.2.1.

Full text

Abstract:

In this work, we study the famous model of volatility; called model of conditional heteroscedastic autoregressive with mixed memory MMGARCH for modeling nonlinear time series. The MMGARCH model has two mixing components, one is a GARCH short memory and the other is GARCH long memory. the main objective of this search for finds the best model between mixtures of the models we made (long memory with long memory, short memory with short memory and short memory with long memory) Also, the existence of its stationary solution is discussed. The Monte Carlo experiments demonstrate we discovered theoretical. In addition, the empirical application of the MMGARCH model (1, 1) to the daily index DOW and NASDAQ illustrates its capabilities; we find that for the mixture between APARCH and EGARCH is superior to any other model tested because it produces the smallest errors.

APA, Harvard, Vancouver, ISO, and other styles

44

Díaz Pérez, Adolfo Alejandro. "Estudio experimental sobre estrategias didácticas innovadoras y tradicionales en la enseñanza de Estudios Sociales." Revista Electrónica de Conocimientos, Saberes y Prácticas 2, no. 1 (June 30, 2019): 21–35. http://dx.doi.org/10.5377/recsp.v2i1.8164.

Full text

Abstract:

En los distintos espacios educativos es común hacer referencia a las metodologías innovadoras y a las metodologías tradicionales, máxime en el contexto actual en donde el flamante magisterio está siendo portavoz de cambios en las metodologías didácticas que desde antaño han estado bien enraizadas, particularmente, en la enseñanza de las ciencias sociales. Sin embargo, mientras la lucha entre lo innovador y lo tradicional se agudiza en los espacios de debates educativos, las prácticas pedagógicas tradicionales en el aula de clase permanecen inalterables y, por otra parte, los estudiantes –ahora al margen de los nuevos recursos que brinda la sociedad- aparcan expectantes por otras formas de aprender. A partir de esto, la presente investigación se propuso indagar la opinión del estudiantado acerca de las estrategias didácticas innovadoras y tradicionales, para ello se utilizó un diseño experimental exploratorio y se realizó una intervención didáctica de diez sesiones de clase, a fin de generar un espacio de reflexión entre el profesorado respecto a los retos de la innovación educativa.

APA, Harvard, Vancouver, ISO, and other styles

45

CHANG, CHIA-LIN, MICHAEL McALEER, and ROENGCHAI TANSUCHAT. "MODELLING LONG MEMORY VOLATILITY IN AGRICULTURAL COMMODITY FUTURES RETURNS." Annals of Financial Economics 07, no. 02 (December 2012): 1250010. http://dx.doi.org/10.1142/s2010495212500108.

Full text

Abstract:

This paper estimates a long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGARCH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1, d, 1) and FIEGARCH (1, d, 1) models are found to outperform their GARCH (1, 1) and EGARCH (1, 1) counterparts.

APA, Harvard, Vancouver, ISO, and other styles

46

Al Rahahleh, Naseem, and Robert Kao. "Forecasting Volatility: Evidence from the Saudi Stock Market." Journal of Risk and Financial Management 11, no. 4 (November 28, 2018): 84. http://dx.doi.org/10.3390/jrfm11040084.

Full text

Abstract:

The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized autoregressive conditional heteroskedasticity (GARCH)–class models in terms of their in-sample and out-of-sample forecasting accuracy for the Tadawul All Share Index (TASI) and the Tadawul Industrial Petrochemical Industries Share Index (TIPISI) for petrochemical industries. We use the daily price data of the TASI and the TIPISI for the period of 10 September 2007 to 26 February 2015. The results suggest that the Asymmetric Power of ARCH (APARCH) model is the most accurate model in the GARCH class for forecasting the volatility of both the TASI and the TIPISI in the context of petrochemical industries, as this model outperforms the other models in model estimation and daily out-of-sample volatility forecasting of the two indices. This study is useful for the dataset examined, because the results provide a basis for traders, policy-makers, and international investors to make decisions using this model to forecast the risks associated with investing in the Saudi stock market, within certain limitations.

APA, Harvard, Vancouver, ISO, and other styles

47

Diaz, John Francis T. "Do Scarce Precious Metals Equate to Safe Harbor Investments? The Case of Platinum and Palladium." Economics Research International 2016 (January 10, 2016): 1–7. http://dx.doi.org/10.1155/2016/2361954.

Full text

Abstract:

This research establishes the predictability and safe harbor properties of two scarce precious metals, namely, platinum and palladium. Utilizing their spot prices, the study concludes intermediate memory in the return structures of both precious metals, which implies the instability of platinum and palladium returns’ persistency in the long run. However, both the ARFIMA-FIGARCH and the ARFIMA-FIAPARCH models confirm long-memory properties in the volatility of the two spot prices. The leverage effects phenomenon is not also present based on the ARFIMA-APARCH and ARFIMA-FIAPARCH models, which may possibly conclude the resilience of both precious metals against increased volatility. However, further tests proved that only platinum has a symmetric volatility response to shocks with the presence of negative gamma parameter, which proves that only platinum can be considered a safe harbor investment, because negative and positive shocks have equal effects on their returns and volatilities. Comparing the four models utilized in this study, the combined ARFIMA-FIAPARCH models are the best fitting model to characterize both precious metals’ spot prices.

APA, Harvard, Vancouver, ISO, and other styles

48

Wulandari, Aulia Yulianti, Noer Azam Achsani, and Lukytawati Anggraeni. "Respon Return Pasar Modal Indonesia terhadap Kebijakan Moneter Domestik dan Asing." JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN 7, no. 1 (August 21, 2018): 1–20. http://dx.doi.org/10.29244/jekp.7.1.1-20.

Full text

Abstract:

Understanding the impact of external shocks on the stock market return and volatility is crucial for market participants as volatility is synonymous with risk. This paper provides comprehensive evidence on the spillover effects of the change of monetary policies from inside country and foreign origins on Indonesia stock market in the period of the time from November 2, 2012 to May 15, 2017. Used symmetric (IGARCH) and asymmetric (EGARCH and APARCH) GARCH model analysis to evaluate the impact of surprise and anticipated changes of monetary policies from inside country and foreign policies (from another ASEAN countries and leading economies, in this paper are United States, Europe, and United Kingdom). Surprise change of monetary policy is proxied by one day change in 3 months interbank offered rate, while anticipated change of monetary policy is proxied by one day change in target interest rate or policy rate. The result shows that information of the monetary policy news and Indonesia stock return is asymmetric. Indonesia stock market is only affected by foreign monetary policies. Keywords: ASEAN stock market, GARCH, Monetary policy JEL classification: C01, C50, E50

APA, Harvard, Vancouver, ISO, and other styles

49

Wulandari, Aulia Yulianti, Noer Azam Achsani, and Lukytawati Anggraeni. "Respon Return Pasar Modal Indonesia terhadap Kebijakan Moneter Domestik dan Asing." JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN 7, no. 1 (August 21, 2018): 1–20. http://dx.doi.org/10.29244/jekp.7.1.2018.1-20.

Full text

Abstract:

Understanding the impact of external shocks on the stock market return and volatility is crucial for market participants as volatility is synonymous with risk. This paper provides comprehensive evidence on the spillover effects of the change of monetary policies from inside country and foreign origins on Indonesia stock market in the period of the time from November 2, 2012 to May 15, 2017. Used symmetric (IGARCH) and asymmetric (EGARCH and APARCH) GARCH model analysis to evaluate the impact of surprise and anticipated changes of monetary policies from inside country and foreign policies (from another ASEAN countries and leading economies, in this paper are United States, Europe, and United Kingdom). Surprise change of monetary policy is proxied by one day change in 3 months interbank offered rate, while anticipated change of monetary policy is proxied by one day change in target interest rate or policy rate. The result shows that information of the monetary policy news and Indonesia stock return is asymmetric. Indonesia stock market is only affected by foreign monetary policies. Keywords: ASEAN stock market, GARCH, Monetary policy JEL classification: C01, C50, E50

APA, Harvard, Vancouver, ISO, and other styles

50

López Villa, Jorge, and Miriam Sosa Castro. "Contagio en la volatilidad entre los mercados de capital y de divisas en México y Brasil (2000-2020)." Revista Mexicana de Economía y Finanzas 16, TNEA (September 8, 2021): 1–28. http://dx.doi.org/10.21919/remef.v16i0.701.

Full text

Abstract:

Se analiza el contagio en volatilidad entre los mercados cambiarios y de valores en México y Brasil de enero/2000 a noviembre/2020. La metodología incluye modelos GARCH univariados: GARCH, APARCH, EGARCH y TARCH para el análisis de la volatilidad de las series y modelos multivariados GARCH: DCC y ADCC, para medir los co-movimientos de la volatilidad condicional del mercado de capitales y cambiario, permitiendo determinar la existencia de contagio. Se observa que, en el mercado brasileño, la correlación es más fuerte y estable que en el mercado mexicano, confirmando, al menos un periodo de contagio en cada economía. Las recomendaciones que se desprenden es que, durante periodos de inestabilidad se deben realizar estrategias de cobertura cambiaria o, mantener las posiciones hasta que haya recuperación en los mercados. Las limitaciones es que únicamente se incluyen dos economías latinoamericanas, por lo que, no se analiza el efecto regional. La originalidad radica en la propuesta empírica, el estudio de economías emergentes, que han sido escasamente analizadas, así como, en el aporte de información crucial para las estrategias de diversificación y cobertura de riesgos.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Aparchai'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles