scholarly journals Investigating seasonality, policy intervention and forecasting in the Indian gold futures market: a comparison based on modeling non-constant variance using two different methods

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rupel Nargunam ◽  
William W. S. Wei ◽  
N. Anuradha
Keyword(s):  
Transformation Method ◽  
Futures Market ◽  
Forecast Performance ◽  
Constant Variance ◽  
Variance Stabilization ◽  
Modelling Method ◽  
Standard Variance ◽  
Physical Form ◽  
Gold Futures ◽  
Derivative Market

AbstractThis study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physical form. The trade of gold futures relates to seasons, festivity, and government policy. So, the paper will discuss seasonality and intervention in the analysis. Due to non-constant variance, we will also use the standard variance stabilization transformation method and the ARIMA/GARCH modelling method to compare the forecast performance on the gold futures prices. The results from the analysis show that while the standard variance transformation method may provide better point forecast values, the ARIMA/GARCH modelling method provides much shorter forecast intervals. The empirical results of this study which rationalise the effect of seasonality in the Indian bullion derivative market have not been reported in literature.

Does Commodity Derivatives Function Effectively? A lengthy Discussion

2021 ◽  
pp. 227797522098574
Author(s):  
Bhabani Sankar Rout ◽  
Nupur Moni Das ◽  
K. Chandrasekhara Rao
Keyword(s):  
Price Discovery ◽  
Spot Market ◽  
Futures Market ◽  
The Other ◽  
Volatility Spillover ◽  
Agricultural Commodities ◽  
Commodity Derivatives ◽  
Derivative Market ◽  
Negative Shock ◽  
Lengthy Discussion

The present work has been designed to intensely investigate the capability of the commodity futures market in achieving the aim of price discovery. Further, the downside of the cash and futures market and transfer of the risk to other markets has also been studied using VaR, and Bivariate EGARCH. The findings of the work point that the metal commodity derivative market helps in the efficient discovery of price in the spot market except for nickel. But, in the case of the agricultural commodities, the spot is found to be leading and thus there is no price discovery except turmeric. On the other hand, the volatility spillover is bidirectional for both agri and metal commodities except copper, where volatility spills only from futures to spot. Further, the effect of negative shock informational bias differs from commodity to commodity, irrespective of metal or agriculture.

scholarly journals Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?

2021 ◽  
Vol 9 ◽  
Author(s):  
Yinpeng Zhang ◽  
Panpan Zhu ◽  
Yingying Xu
Keyword(s):  
Futures Markets ◽  
Vector Autoregression ◽  
Spot Market ◽  
Futures Market ◽  
Conditional Heteroskedasticity ◽  
Volatility Spillover ◽  
Empirical Results ◽  
Dynamic Correlation ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Gold Futures

The Bitcoin market has become a research hotspot after the outbreak of Covid-19. In this paper, we focus on the relationships between the Bitcoin spot and futures. Specifically, we adopt the vector autoregression-dynamic correlation coefficient-generalized autoregressive conditional heteroskedasticity (VAR-DCC-GARCH) model and vector autoregression-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroskedasticity (VAR-BEKK-GARCH) models and calculate the hedging effectiveness (HE) value to investigate the dynamic correlation and volatility spillover and assess the risk reduction of the Bitcoin futures to spot. The empirical results show that the Bitcoin spot and futures markets are highly connected; second, there exists a bi-directional volatility spillover between the spot and futures market; third, the HE value is equal to 0.6446, which indicates that Bitcoin futures can indeed hedge the risks in the Bitcoin spot market. Furthermore, we update the data to the post-Covid-19 period to do the robustness checks. The results do not change our conclusion that Bitcoin futures can hedge the risks in the Bitcoin spot market, and besides, the post-Covid-19 results indicate that the hedging ability of Bitcoin futures increased. Finally, we test whether the gold futures can be used as a Bitcoin spot market hedge, and we further control other cryptocurrencies to illustrate the hedging ability of the Bitcoin futures to the Bitcoin spot. Overall, the empirical results in this paper will surely benefit the related investors in the Bitcoin market.

scholarly journals Wavelet Transform as an Alternative to Power Transformation in Time Series Analysis

2016 ◽  
Vol 17 ◽  
pp. 57-74
Author(s):  
Chukwudi Justin Ogbonna ◽  
C. Jeol Nweke ◽  
Eleazer C. Nwogu ◽  
Iheanyi Sylvester Iwueze
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Goodness Of Fit ◽  
Discrete Wavelet ◽  
Power Transformation ◽  
Rate Data ◽  
Forecast Performance ◽  
Constant Variance

This study examines the discrete wavelet transform as a transformation technique in the analysis of non-stationary time series while comparing it with power transformation. A test for constant variance and choice of appropriate transformation is made using Bartlett’s test for constant variance while the Daubechies 4 (D4) Maximal Overlap Discrete Wavelet Transform (DWT) is used for wavelet transform. The stationarity of the transformed (power and wavelet) series is examined with Augmented Dickey-Fuller Unit Root Test (ADF). The stationary series is modeled with Autoregressive Moving Average (ARMA) Model technique. The model precision in terms of goodness of fit is ascertained using information criteria (AIC, BIC and SBC) while the forecast performance is evaluated with RMSE, MAD, and MAPE. The study data are the Nigeria Exchange Rate (2004-2014) and the Nigeria External Reserve (1995-2010). The results of the analysis show that the power transformed series of the exchange rate data admits a random walk (ARIMA (0, 1, 0)) model while its wavelet equivalent is adequately fitted to ARIMA (1,1,0). Similarly, the power transformed version of the External Reserve is adequately fitted to ARIMA (3, 1, 0) while its wavelet transform equivalent is adequately fitted to ARIMA (0, 1, 3). In terms of model precision (goodness - of - fit), the model for the power transformed series is found to have better fit for exchange rate data while model for wavelet transformed series is found to have better fit for external reserve data. In forecast performance, the model for wavelet transformed series outperformed the model for power transformed series. Therefore, we recommend that wavelet transform be used when time series data is non-stationary in variance and our interest is majorly on forecast.

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