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.

Hedging Strategies with the KTB Futures

2002 ◽  
Vol 10 (2) ◽  
pp. 25-56
Author(s):  
Jae Ha Lee ◽  
Han Deog Hui
Keyword(s):  
Garch Model ◽  
Corporate Bond ◽  
Price Sensitivity ◽  
Risk Minimization ◽  
Hedging Strategies ◽  
Daily Data ◽  
Bond Portfolio ◽  
Hedge Ratios ◽  
Out Of Sample ◽  
The Garch Model

This study explores hedging strategies that use the KTB futures to hedge the price risk of the KTB spot portfolio. The study establishes the price sensitivity, risk-minimization, bivariate GARCH (1,1) models as hedging models, and analyzes their hedging performances. The sample period covers from September 29, 1999 to September 18, 2001. Time-matched prices at 11:00 (11:30) of the KTB futures and spot were used in the analysis. The most important findings may be summarized as follows. First, while the average hedge ration of the price sensitivity model is close to one, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios tend to be greater for daily data than for weekly data. Second, for the daily in-sample data, hedging effectiveness is the highest for the GARCH model with time-varying hedge ratios, but the risk-minimization model with constant hedge ratios is not far behind the GARCH model in its hedging performance. In the case of out-of-sample hedging effectiveness, the GARCH model is the best for the KTB spot portfolio, and the risk-minimization model is the best for the corporate bond portfolio. Third, for daily data, the in-sample hedge shows a better performance than the out-of-sample hedge, except for the risk-minimization hedge against the corporate bond portfolio. Fourth, for the weekly in-sample hedges, the price sensitivity model is the worst and the risk-minimization model is the best in hedging the KTB spot portfolio. While the GARCH model is the best against the KTB +corporate bond portfolio, the risk-minimization model is generally as good as the GARCH model. The risk-minimization model performs the best for the weekly out-of-sample data, and the out-of-sample hedges are better than the in-sample hedges. Fifth, while the hedging performance of the risk-minimization model with daily moving window seems somewhat superior to the traditional risk-minimization model when the trading volume increased one year after the inception of the KTB futures, on the average the traditional model is better than the moving-window model. For weekly data, the traditional model exhibits a better performance. Overall, in the Korean bond markets, investors are encouraged to use the simple risk-minimization model to hedge the price risk of the KTB spot and corporate bond portfolios.

scholarly journals The Impact of Liquidity on GARCH Option Pricing Error during Financial Crisis

2017 ◽  
Vol 4 (4) ◽  
pp. 160
Author(s):  
Han Ching Huang ◽  
Yong Chern Su ◽  
Wei-Shen Chen
Keyword(s):  
Financial Crisis ◽  
Option Pricing ◽  
Garch Model ◽  
Black Scholes Model ◽  
Pricing Errors ◽  
Black Scholes ◽  
Out Of Sample ◽  
Pricing Error ◽  
The Impact ◽  
The Relationship

In this paper, we explore the valuation performance of Heston and Nandi GARCH (HN GARCH) model on the pricing of options of financial stocks listed for AMEX during pre and post financial crisis periods. We find that the GARCH pricing model presents better performance than the traditional Black-Scholes model for the out-of-sample option pricing, no matter what the moneyness and the time-to-maturity. Specifically, the models show the effects of liquidity is not significant. Intuitionally, smaller liquidity tends to exhibit more pricing errors, especially for longer mature options. Unfortunately, we cannot get the expected outcomes, which is that the period of post financial crisis tend to have larger pricing errors. In sum, except more computational convenience, the HN GARCH model offers another vision of the relationship between liquidity and its effect on pricing errors.

scholarly journals On Forecasting Taiwanese Stock Index Option Prices: The Role of Implied Volatility Index

2017 ◽  
Vol 9 (9) ◽  
pp. 133 ◽  
Author(s):  
Jying-Nan Wang ◽  
Hung-Chun Liu ◽  
Lu-Jui Chen
Keyword(s):  
Implied Volatility ◽  
Option Price ◽  
Garch Model ◽  
Predictive Performance ◽  
Stock Index ◽  
Additional Information ◽  
Volatility Index ◽  
Volatility Models ◽  
Rolling Window ◽  
The Garch Model

This paper aims to propose four volatility measures: The first is the GARCH model advocated by Bollerslev (1986); the second is the GARCHVIX model which extends the GARCH model by including the volatility index (VIX) as explanatory variable for volatility; the last two are HS20D and HS252D, which represent the historical volatilities generated by traditional rolling window technique with 20- and 252-day historical index returns data, respectively. We examine the price information on VIX to improve the predictive performance of GARCH model for valuing TAIEX stock index call options (TXO) over the period from January 2014 to May 2015. Empirical results firstly indicate that both the GARCH and GARCHVIX models consistently perform better than the historical volatility models for forecasting call value of TXO under different moneynesses. Secondly, the GARCHVIX model significantly outperforms the GARCH model for most cases, indicating that the GARCH-based option price forecasts can be effectively improved with the additional information contained in VIX. Finally, the use of GARCHVIX model can greatly reduce model mispricing especially for out-the-money TXO option case. Thus, volatility index is crucial for option traders to efficiently predict TXO option value with 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.

Detecting the impact of fundamentals and regulatory reforms on the Greek wholesale electricity market using a SARMAX/GARCH model

Energy ◽  
2018 ◽  
Vol 142 ◽  
pp. 1083-1103 ◽  
Author(s):  
George P. Papaioannou ◽  
Christos Dikaiakos ◽  
Athanasios S. Dagoumas ◽  
Anargyros Dramountanis ◽  
Panagiotis G. Papaioannou
Keyword(s):  
Electricity Market ◽  
Garch Model ◽  
Wholesale Electricity Market ◽  
The Impact ◽  
Regulatory Reforms

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

Forecasting multivariate VaR and ES using MC-GARCH-Copula model

2020 ◽  
Vol 21 (5) ◽  
pp. 493-516 ◽  
Author(s):  
Hemant Kumar Badaye ◽  
Jason Narsoo
Keyword(s):  
Value At Risk ◽  
Garch Model ◽  
Dependence Structure ◽  
Copula Model ◽  
Copula Models ◽  
Content Type ◽  
Significance Level ◽  
Foreign Currencies ◽  
Out Of Sample ◽  
Weighted Portfolio

Purpose This study aims to use a novel methodology to investigate the performance of several multivariate value at risk (VaR) and expected shortfall (ES) models implemented to assess the risk of an equally weighted portfolio consisting of high-frequency (1-min) observations for five foreign currencies, namely, EUR/USD, GBP/USD, EUR/JPY, USD/JPY and GBP/JPY. Design/methodology/approach By applying the multiplicative component generalised autoregressive conditional heteroskedasticity (MC-GARCH) model on each return series and by modelling the dependence structure using copulas, the 95 per cent intraday portfolio VaR and ES are forecasted for an out-of-sample set using Monte Carlo simulation. Findings In terms of VaR forecasting performance, the backtesting results indicated that four out of the five models implemented could not be rejected at 5 per cent level of significance. However, when the models were further evaluated for their ES forecasting power, only the Student’s t and Clayton models could not be rejected. The fact that some ES models were rejected at 5 per cent significance level highlights the importance of selecting an appropriate copula model for the dependence structure. Originality/value To the best of the authors’ knowledge, this is the first study to use the MC-GARCH and copula models to forecast, for the next 1 min, the VaR and ES of an equally weighted portfolio of foreign currencies. It is also the first study to analyse the performance of the MC-GARCH model under seven distributional assumptions for the innovation term.

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.  

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