scholarly journals Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 12 ◽  
Author(s):  
David E. Allen ◽  
Michael McAleer
Keyword(s):  
Stochastic Volatility ◽  
Ordinary Least Squares ◽  
Conditional Heteroscedasticity ◽  
Daily Data ◽  
Volatility Models ◽  
Tail Behaviour ◽  
Comparable Performance ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Daily Returns ◽  
Better Than

The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken from the Oxford Man Realised Library. Both models demonstrated comparable performance and were correlated to a similar extent with RV estimates when measured by ordinary least squares (OLS). However, a crude variant of Corsi’s (2009) Heterogeneous Autoregressive (HAR) model, applied to squared demeaned daily returns on FTSE, appeared to predict the daily RV of FTSE better than either of the two models. Quantile regressions suggest that all three methods capture tail behaviour similarly and adequately. This leads to the question of whether we need either of the two standard volatility models if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at least in the context of the sample.

scholarly journals Stochastic Volatility and GARCH: Do Squared End-of-Day Returns Provide Similar Information?

2020 ◽  
Vol 13 (9) ◽  
pp. 202
Author(s):  
David Edmund Allen
Keyword(s):  
Stochastic Volatility ◽  
Explanatory Power ◽  
Second Moments ◽  
Daily Data ◽  
Volatility Models ◽  
Comparable Performance ◽  
Auto Regressive ◽  
Marginal Improvement ◽  
Daily Returns ◽  
Better Than

The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and STOXX50 indices, sampled at 5-minute intervals, taken from the Oxford Man Realised Library. Both models demonstrate comparable performance and are correlated to a similar extent with the RV estimates, when measured by OLS. However, a crude variant of Corsi’s (2009) Heterogenous Auto-Regressive (HAR) model, applied to squared demeaned daily returns on the indices, appears to predict the daily RV of the series, better than either of the two base models. The base SV model was then enhanced by adding a regression matrix including the first and second moments of the demeaned return series. Similarly, the GARCH(1,1) model was augmented by adding a vector of demeaned squared returns to the mean equation. The augmented SV model showed a marginal improvement in explanatory power. This leads to the question of whether we need either of the two standard volatility models, if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at least in the context of the indices in the sample. The paper thus explores whether simple rules of thumb match the volatility forecasting capabilities of more sophisticated models.

scholarly journals Modeling of Stochastic Volatility to Validate IDR Anchor Currency

2018 ◽  
Vol 20 (2) ◽  
pp. 165 ◽  
Author(s):  
Didit Budi Nugroho ◽  
Tundjung Mahatma ◽  
Yulius Pratomo
Keyword(s):  
Stochastic Volatility ◽  
Foreign Currency ◽  
Foreign Exchange Rate ◽  
Stochastic Volatility Models ◽  
Daily Data ◽  
Volatility Models ◽  
Error Distributions ◽  
T Distribution ◽  
Negative Relationships ◽  
Daily Returns

This study aims to assess the performance of stochastic volatility models for their estimation of foreign exchange rate returns' volatility using daily data from Bank Indonesia (BI). The model is then applied to validate the anchor currency of Indonesian rupiah (IDR). Two stylized facts are incorporated into the models: A correlation between the previous returns and their conditional variance, and return errors following four different error distributions namely Normal, Student-t, non-central Student-t, and generalized hyperbolic skew Student-t. The analysis is based on the application of daily returns data from nine foreign currency selling rates to IDR from 2010 to 2015, including the AUD, CHF, CNY, EUR, GBP, JPY, MYR, SGD, and USD. The main results are: (1) Mixed evidence of positive and negative relationships between the return and its variance were found, especially significant correlations being found for the IDR/AUD, IDR/CHF, IDR/JPY, IDR/SGD, and IDR/USD returns series; (2) the model with the generalized hyperbolic skew Student's t-distribution specification for the returns error provides the best performance; and (3) anchoring the IDR to established hard currencies is more appropriate than anchoring it to other currencies.

scholarly journals Modelling the Efficiency of TGARCH Model in Nigeria Inflation Rate

2021 ◽  
Vol 3 (2) ◽  
pp. 20-35
Author(s):  
Michael Sunday Olayemi ◽  
Adenike Oluwafunmilola Olubiyi ◽  
Oluwamayowa Opeyimika Olajide ◽  
Omolola Felicia Ajayi
Keyword(s):  
Inflation Rate ◽  
Random Variable ◽  
Mean Value ◽  
Volatility Models ◽  
Downward Spiral ◽  
The Garch Model ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Best Fit ◽  
Lower Production ◽  
Better Than

In general, volatility is known and referred to as variance and it is a degree of spread of a random variable from its mean value. Two volatility models were considered in this paperwork. Nigeria's inflation rate was modeled by applying the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Threshold GARCH models. Symmetric and asymmetric models captured the most commonly stylized facts about the rate of inflation in Nigeria like leverage effects and irregularities in clustering and were studied. These models are GARCH (1,1) and TGARCH (1,1). This work estimated the comparison of volatility models in term of best fit and forecasting. The result showed that TGARCH (1,1) model outperformed GARCH (1,1) models in term of best fit, because it has the least AIC of 2.590438. We forecasted to see the level of volatility using Theils Inequality Coefficient and the result shows that TGARCH has the highest Theils Inequality Coefficient of 0.065075 which makes the TGARCH model better than the GARCH model in this research. From the initial and modified sample static forecast, it was discovered that the return on inflation is stable and shows that volatility slows towards the end of the month, we can see a downward spiral, which means price reaction to economic crisis led to lower production, lower wages, decreased demand, and still lower prices.

scholarly journals Análise da Efetividade de Políticas de Hedge no Mercado de Dólar Futuro no Brasil

2011 ◽  
Vol 9 (3) ◽  
pp. 365
Author(s):  
Marcelo Cabus Klotzle ◽  
Antonio Carlos Figueiredo Pinto ◽  
Mario Domingues Simões ◽  
Leonardo Lima Gomes
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
Fast Growth ◽  
Future Market ◽  
Conditional Heteroscedasticity ◽  
Hedge Ratio ◽  
Hedge Ratios ◽  
Dollar Price ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Naive Approach

In recent years, one could observe a very definite surge in dollar prices in Brazil. Many Brazilian Companies, especially those with large amounts of dollar denominated debt incurred substantial losses due to the strong and fast growth of the dollar. The subsequent dollar price collapse from 2002 to 2008 caused great losses to exporters. In the context of hedge being a form of protection against currency oscillations, this paper aimed to study its effectiveness using the dollar future market in the BM&FBovespa. Specifically, four alternatives for calculating the optimum hedge ratio were compared: a) the so called naïve approach, where opposite positions are taken in the spot and future markets; b) OLS – Ordinary Least Squares c) symmetric bi-variate GARCH (Generalized Autoregressive Conditional Heteroscedasticity); d) asymmetric bi-variate GARCH. The results showed that both GARCH supported hedge ratios presented higher effectiveness when compared to OLS, with in turn surpassed the naïve one.

Stochastic Volatility Models for Asset Returns with Leverage, Skewness and Heavy-Tails via Scale Mixture

2014 ◽  
Vol 04 (03) ◽  
pp. 1450011
Author(s):  
Jing-Zhi Huang ◽  
Li Xu
Keyword(s):  
Stochastic Volatility ◽  
Heavy Tails ◽  
Skew Normal Distribution ◽  
New Class ◽  
Volatility Models ◽  
Special Cases ◽  
Error Distributions ◽  
Heavy Tailed ◽  
Equity Index ◽  
Daily Returns

We propose and estimate a new class of equity return models that incorporate scale mixtures of the skew-normal distribution for the error distribution into the standard stochastic volatility framework. The main advantage of our models is that they can simultaneously accommodate the skewness, heavy-tailedness, and leverage effect of equity index returns observed in the data. The proposed models are flexible and parsimonious, and include many asymmetrically heavy-tailed error distributions — such as skew-t and skew-slash distributions — as special cases. We estimate a variety of specifications of our models using the Bayesian Markov Chain Monte Carlo method, with data on daily returns of the S&P 500 index over 1987–2009. We find that the proposed models outperform existing ones of index returns.

Comparison of linear and non-linear GARCH models for forecasting volatility of select emerging countries

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudhi Sharma ◽  
Vaibhav Aggarwal ◽  
Miklesh Prasad Yadav
Keyword(s):  
Global Financial Crisis ◽  
Emerging Countries ◽  
Expected Returns ◽  
Garch Models ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Volatility Models ◽  
Non Linear ◽  
Forecasting Volatility ◽  
Autoregressive Conditional Heteroscedasticity

PurposeSeveral empirical studies have proven that emerging countries are attractive destinations for Foreign Institutional Investors (FIIs) because of high expected returns, weak market efficiency and high growth that make them attractive destination for diversification of funds. But higher expected returns come coupled with high risk arising from political and economic instability. This study aims to compare the linear (symmetric) and non-linear (asymmetric) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in forecasting the volatility of top five major emerging countries among E7, that is, China, India, Indonesia, Brazil and Mexico.Design/methodology/approachThe volatility of financial markets of five major emerging countries has been empirically investigated for a period of two decades from January 2000 to December 2019 using univariate volatility models including GARCH 1, 1, Exponential Generalized Autoregressive Conditional Heteroscedasticity (E-GARCH 1, 1) and Threshold Generalized Autoregressive Conditional Heteroscedasticity (T-GARCH-1, 1) models. Further, to examine time-varying volatility, the distinctions of structural break have been captured in view of the global financial crisis of 2008. Thus, the period under the study has been segregated into pre- and post-crisis, that is, January 2001–December 2008 and January 2009–December 2019, respectively.FindingsThe findings indicate that GARCH (1, 1) model is superior to non-linear GARCH models for forecasting volatility because the effect of leverage is insignificant. China has been considered as most volatile, whereas India is volatile but positively skewed and Indonesia is the least volatile country. The results can help investors in better international diversification of their portfolio and identifying best suitable hedging opportunities.Practical implicationsThis study can help investors to construct a more risk-adjusted returns international portfolio. Further, it adds to the scant literature available on the inconclusive debate on the choice of linear versus non-linear models to forecast market volatility.Originality/valueEarlier studies related to univariate volatility models are mostly applications of the models. Only few studies have considered the robustness while applying the models. However, none of the studies to the best of the authors’ searches have considered these models for identifying the diversification opportunity among the emerging countries. Hence, this study is able to derive diversification and hedging opportunities by applying wide ranges of the statistical applications and models, that is, descriptive, correlations and univariate volatility models. It makes the study more rigorous and unique compared to the previous literature.

scholarly journals Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion

INSIST ◽  
2018 ◽  
Vol 3 (2) ◽  
pp. 160
Author(s):  
Eri Setiawan ◽  
Netti Herawati ◽  
Khoirin Nisa
Keyword(s):  
Stock Return ◽  
Information Criterion ◽  
Performance Comparison ◽  
Conditional Heteroscedasticity ◽  
Stock Return Volatility ◽  
Asymmetric Volatility ◽  
Volatility Models ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Return Data

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.

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