scholarly journals Multistep quantile forecasts for supply chain and logistics operations: bootstrapping, the GARCH model and quantile regression based approaches

2018 ◽  
Vol 28 (1) ◽  
pp. 309-336 ◽  
Author(s):  
Joanna Bruzda
Keyword(s):  
Supply Chain ◽  
Quantile Regression ◽  
Garch Model ◽  
The Garch Model

scholarly journals Asymmetry and Leverage with News Impact Curve Perspective in Australian Stock Returns’ Volatility during COVID-19

2021 ◽  
Vol 14 (7) ◽  
pp. 314
Author(s):  
Najam Iqbal ◽  
Muhammad Saqib Manzoor ◽  
Muhammad Ishaq Bhatti
Keyword(s):  
Stock Returns ◽  
Garch Model ◽  
Bad News ◽  
Good News ◽  
Fiscal Policies ◽  
News Shocks ◽  
Australian Health ◽  
Egarch Model ◽  
Negative Effect ◽  
The Garch Model

This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH (EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on the Australian health system and its economy as bad news, and on the other hand, measures taken by government economic stimulus packages through their monetary and fiscal policies as good news. The S&P ASX200 (ASX-200) index is used as a proxy to the Australian stock market, and we use value-weighted returns of the stocks listed on ASX-200 for the period 27 January 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative (positive) shocks on the conditional volatility compared to its variant with the news.

scholarly journals Ramadan Effect and Volatility Risk by Garch Model: Evidence in Indonesia Stock Market

2021 ◽  
pp. 73-82
Author(s):  
Dery Westryananda Putra ◽  
Sri Hasnawati ◽  
Muslimin Muslimin
Keyword(s):  
Stock Market ◽  
Sampling Method ◽  
Stock Exchange ◽  
Garch Model ◽  
Analytical Tool ◽  
Volatility Risk ◽  
Purposive Sampling ◽  
Model Evidence ◽  
The Garch Model

This study aims to analyze the effect of the Ramadan effect and volatility risk on the Indonesian stock market using the GARCH model. The population in this study are companies listed on the LQ45 index on the Indonesia Stock Exchange during 2019. There are 42 companies used as samples in this study. The research sample was taken using purposive sampling method. This study uses the GARCH model as an analytical tool. The results of this study indicate that there is no Ramadan effect on the LQ45 index, but the volatility in the month of Ramadan affects the volatility in the LQ45 index. Keywords: Ramadan Effect, Volatility Risk, GARCH Model Abstrak Penelitian ini bertujuan untuk menganalisis pengaruh Ramadhan effect dan risiko volatilitas terhadap pasar saham Indonesia dengan menggunakan model GARCH. Populasi dalam penelitian ini adalah perusahaan yang terdaftar pada indeks LQ45 di Bursa Efek Indonesia selama tahun 2019. Terdapat 42 perusahaan yang dijadikan sampel dalam penelitian ini. Sampel penelitian diambil dengan menggunakan metode purposive sampling. Penelitian ini menggunakan model GARCH sebagai alat analisis. Hasil penelitian ini menunjukkan bahwa tidak ada pengaruh Ramadhan terhadap indeks LQ45, namun volatilitas pada bulan Ramadhan berpengaruh terhadap volatilitas pada indeks LQ45. Kata Kunci: Ramadhan Effect, Risiko Volatilitas, Model GARCH

scholarly journals Bad Friday, Monday Effect and Political Issue: Application of ARCH-GARCH Model to Analyze Seasonal Pattern of Stock Return

2018 ◽  
Vol 7 (3.30) ◽  
pp. 38
Author(s):  
Maria Rio Rita ◽  
Sugeng Wahyudi ◽  
Harjum Muharam
Keyword(s):  
Stock Prices ◽  
Trading Volume ◽  
Stock Return ◽  
Seasonal Pattern ◽  
Garch Model ◽  
Statistical Characteristics ◽  
Political Issue ◽  
Political Issues ◽  
Monday Effect ◽  
The Garch Model

At the end of 2016, Indonesia was shaken by a demonstration of the election of the Governor of Jakarta Capital Special Region and political issues related to religious defamation. Does this condition have an impact on stock prices and returns? The aim of this study is to test the week day pattern in IDX using LQ-45 stocks during selected observation period of August 2016-January 2017. Then a GARCH model is used to investigate the presence of week day pattern in the stock market. Therefore, the GARCH model is able to describe observed statistical characteristics of many time series of financial assets return. The test results show that there is a difference in average stock return during the trading day. The lowest and the highest return are observed on Monday and Wednesday, respectively. Meanwhile, the average negative return on Friday is not proven to significantly drive the occurrence of Monday effect. Return on Monday is influenced by the frequency of trading, not by trading volume. Is there anything to do with the psychological aspect of investors solely in assessing risk acceptance to stocks? Research agenda related to this is very relevant to do in the future.  

Valuation Method of Equity Incentives of Listed Companies Based on the Black-Scholes Model

2020 ◽  
Vol 12 (2) ◽  
pp. 36-49
Author(s):  
Zhongwen Liu ◽  
Yifei Chen
Keyword(s):  
Stock Price ◽  
Turnover Rate ◽  
Garch Model ◽  
Listed Companies ◽  
Valuation Method ◽  
Black Scholes Model ◽  
Black Scholes ◽  
S Model ◽  
The Garch Model ◽  
Equity Incentive

This article applies the classic Black-Scholes model (i.e. B-S model) and turnover rate adapted B-S model (revised B-S model) to equity incentive valuation of listed companies. Unlike other studies on equity incentive valuation which generally adopt historical volatility, this article applies the GARCH model to equity incentive valuation. The volatility of stock price is estimated by the GARCH model to improve the accuracy of equity incentive valuation. The turnover rate has an important impact on the equity incentive valuation of listed companies. Considering the turnover rate can improve the accuracy of the equity incentive valuation and reduce the error of equity incentive valuation. Through the case study of the equity incentive valuation of Infinova, the practicality of the equity incentive valuation method is further verified.

scholarly journals A Novel Fuzzy Linear Regression Sliding Window GARCH Model for Time-Series Forecasting

2020 ◽  
Vol 10 (6) ◽  
pp. 1949
Author(s):  
Amiratul L. Mohamad Hanapi ◽  
Mahmod Othman ◽  
Rajalingam Sokkalingam ◽  
Nazirah Ramli ◽  
Abdullah Husin ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Garch Model ◽  
Sliding Window ◽  
Time Series Forecasting ◽  
Likelihood Method ◽  
Fuzzy Linear Regression ◽  
Specific Distribution ◽  
Proposed Model ◽  
The Garch Model

Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most popular models for time-series forecasting. The GARCH model uses a maximum likelihood method for parameter estimation. For the likelihood method to work, there should be a known and specific distribution. However, due to uncertainties in time-series data, a specific distribution is indeterminable. The GARCH model is also unable to capture the influence of each variance in the observation because the calculation of the long-run average variance only considers the series in its entirety, hence the information on different effects of the variances in each observation is disregarded. Therefore, in this study, a novel forecasting model dubbed a fuzzy linear regression sliding window GARCH (FLR-FSWGARCH) model was proposed; a fuzzy linear regression was combined in GARCH to estimate parameters and a fuzzy sliding window variance was developed to estimate the weight of a forecast. The proposed model promotes consistency and symmetry in the parameter estimation and forecasting, which in turn increases the accuracy of forecasts. Two datasets were used for evaluation purposes and the result of the proposed model produced forecasts that were almost similar to the actual data and outperformed existing models. The proposed model was significantly fitted and reliable for time-series forecasting.

Persistence in Variance, Structural Change, and the GARCH Model

1990 ◽  
Vol 8 (2) ◽  
pp. 225 ◽  
Author(s):  
Christopher G. Lamoureux ◽  
William D. Lastrapes
Keyword(s):  
Structural Change ◽  
Garch Model ◽  
The Garch Model

scholarly journals Empirical study of the GARCH model with rational errors

2013 ◽  
Vol 454 ◽  
pp. 012040
Author(s):  
Ting Ting Chen ◽  
Tetsuya Takaishi
Keyword(s):  
Empirical Study ◽  
Garch Model ◽  
The Garch Model

Foreign Exchange Volatility Shifts and Futures Hedging: An ICSS-GARCH Approach

2007 ◽  
Vol 10 (03) ◽  
pp. 349-388 ◽  
Author(s):  
Iqbal Mansur ◽  
Steven J. Cochran ◽  
David Shaffer
Keyword(s):  
Garch Model ◽  
Volatility Persistence ◽  
Futures Hedging ◽  
Volatility Models ◽  
Out Of Sample ◽  
Competing Models ◽  
Foreign Exchange Volatility ◽  
The Garch Model ◽  
The Impact ◽  
Cumulative Sums

In this study, the impact of volatility regime shifts on volatility persistence and hedge ratio estimation is determined for four major currencies using an iterated cumulative sums of squares (ICSS)-GARCH model. Employing a standard GARCH (1,1) model as the benchmark, within-sample results demonstrate that the inclusion of volatility shifts substantially reduces volatility persistence and the significance of the ARCH and GARCH coefficients. In terms of hedging effectiveness, the ICSS-GARCH model outperforms the standard GARCH model for all four currencies. In comparison to two constant volatility models, the standard GARCH model yields the lowest performance, whereas the ICSS-GARCH model performs at least as well as these models. In out-of-sample analysis, the GARCH model provides substantial variance reductions relative to the constant volatility models. Moreover, the ICSS-GARCH model yields positive variance reductions relative to all competing models, including the standard GARCH model. The results suggest that in cases where dynamic hedging is important, sudden shifts in volatility should not be ignored.

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