Impact of the Corporate Structure and Sharia-Compliant Status to Average Degree of IPO Underpricing in Malaysia Market

Bakar, Nashirah Binti Abu; Rosbi, Sofian

doi:10.1166/asl.2017.9965

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We chose ARMA (10, 10) as the best model to anticipate the Bitcoin return among the various ARMA models based on the ACF and PACF plots, and this model was also chosen based on the Correlogram plot. The ARMA model estimation result shows that all the co-efficients are statistically significant as the p-value is less than 0.05 (Bakar & Rosbi, 2017). So we can predict the return of Bitcoin with the help of this model.…”

Section: Resultsmentioning

confidence: 93%

“…For fitting an ARMA model it is necessary to determine the number of AR or MA terms. Thus, the ACF and PACF plots of the Bitcoin return series provides information regarding the sequence of AR and MA terms necessary to fit a model (Bakar & Rosbi, 2017). The sample ACF from the series (figure 4) reveals that the most dominant spike at lag 10 is statistically significant for both ACF and PACF.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Repercussion of Covid 19 Upsurge: An Analysis on the Efficaciousness of Autoregressive Models in Volatility and Return Estimation of Bitcoin

M P

2021

CBR

View full text Add to dashboard Cite

Cryptocurrencies, especially Bitcoin, is a hot commodity today. High volatility is a common feature of almost all the cryptocurrencies in the world. A systematic exploration and examination of the volatility of cryptos enables the investor to earn more on their investments. After the outbreak of the COVID-19 pandemic, the crypto market witnessed a highly volatile situation with a huge increase in price. The pandemic also affected the volatility and return of Bitcoin. This research aims to analyse and compare the risk and volatility characteristics of Bitcoin after the outbreak of the COVID-19 pandemic. The study further tests the capacity of several autoregressive models, such as ARMA, GARCH, EGARCH, and TARCH in estimating and evaluating the return and volatility associated with Bitcoin. Identified models were tested and compared with the help of Akaike information criteria (AIC) and Schwarz information criteria (SIC). For this article, the data of daily adjusted closing price of Bitcoin INR (BTC-INR) were collected from Yahoo Finance during the period January, 2017 to December, 2021. We witnessed a huge change in the daily average return of Bitcoin after the COVID-19 outbreak. Also, we identified TARCH (1, 1) as the best model in the ARCH family for evaluating and estimating volatility and ARMA (10, 10) as the best model for predicting the return of Bitcoin.

show abstract

Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%