Do simple traders' rules perform better than the GARCH model? Evidence from currency options in India

2018 ◽  
Vol 6 (3) ◽  
pp. 183
Author(s):  
Aparna Bhat
Keyword(s):  
Garch Model ◽  
Currency Options ◽  
Model Evidence ◽  
The Garch Model ◽  
Better Than

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 Pricing of foreign currency options in the Serbian market

2009 ◽  
Vol 54 (180) ◽  
pp. 91-115
Author(s):  
Irena Jankovic
Keyword(s):  
Financial Market ◽  
Foreign Currency ◽  
Garch Model ◽  
Main Idea ◽  
Market Volatility ◽  
Local Market ◽  
Currency Options ◽  
Volatility Analysis ◽  
The Garch Model

The main idea of this paper is to find the most suitable approach to the valuation of foreign currency options in the Serbian financial market. Volatility analysis included the application of the GARCH model which resulted in the marginal volatility measure, which was used in the pricing of basic foreign currency options in the local market. The analysis is completed with an overview of the implementation of FX derivatives in the Serbian financial market.

scholarly journals GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies

2021 ◽  
Vol 14 (9) ◽  
pp. 421
Author(s):  
Fahad Mostafa ◽  
Pritam Saha ◽  
Mohammad Rafiqul Islam ◽  
Nguyet Nguyen
Keyword(s):  
Value At Risk ◽  
Garch Model ◽  
Graphical Analysis ◽  
Learning Approaches ◽  
Market Prices ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
The Garch Model ◽  
Mean Square Errors ◽  
Better Than

Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (ANN) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (MSEs) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.

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.

Are Shocks Asymmetric to Volatility of Chinese Stock Markets?

2004 ◽  
Vol 07 (03) ◽  
pp. 379-395 ◽  
Author(s):  
Wei-Chiao Huang ◽  
Yuanlei Zhu
Keyword(s):  
Stock Returns ◽  
Stock Markets ◽  
Garch Model ◽  
Leverage Effect ◽  
Arch Models ◽  
Before And After ◽  
Chinese Markets ◽  
Chinese Stock Markets ◽  
Better Than ◽  
A Share

This paper uses ARCH models to examine if there is a leverage effect and also to test if A- and B-share holdings have different risks in Chinese stock markets before and after B-share markets open to domestic investors in February 2001. The empirical results suggest that leverage effect was not present and shocks have symmetric impact on the volatility of Chinese B-share stock returns in both periods and A-share returns in Period I. Thus GARCH model would be a better model to fit the Chinese B-share stock returns than EGARCH or GJR-GARCH model. But EGARCH or GJR-GARCH model fits recent (Period II) A-share markets data better than GARCH model. Another finding of this paper is that holding A- or B-share bears different risk in returns in the two Chinese markets. Furthermore, news or shocks have a larger impact on volatility of B-share returns in Period I than in Period II.

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.

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