Modeling Inflation in Bangladesh

Rahman, Mohammad Lutfor; Islam, Mynul; Roy, Manik

doi:10.4236/ojs.2020.106056

Cited by 2 publications

(3 citation statements)

References 21 publications

(29 reference statements)

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“…The VECM framework is particularly valuable as it captures both short-term dynamics and term equilibrium relationships making it highly suitable for studying the dynamic interplay between Bitcoin and inflation (Rahman et al, 2020). Therefore, conducting research that utilizes VECM would fill the gap by offering valuable insights into the potential role of Bitcoin as a hedge against inflation in diverse economies.…”

Section: Model Identification and Assumptions Testingmentioning

confidence: 99%

An Empirical Investigation of Bitcoin Hedging Capabilities against Inflation using VECM: The Case of United States, Eurozone, Philippines, Ukraine, Canada, India, and Nigeria

Gbolahan

2023

IJEFI

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This study examines Bitcoin's potential as an inflation hedge in different countries, including the United States, the Eurozone, the Philippines, Ukraine, Canada, India, and Nigeria. The study reveals varying results across countries using the Vector Error Correlation Model (VECM) with secondary monthly data from January 2012 to June 2023 for Bitcoin prices and inflation rates. Bitcoin exhibits an insignificant short-term relationship in the United States but a significant long-term negative correlation, suggesting it may not be a reliable inflation hedge. Similarly, no significant relationship was found in the Eurozone, the Philippines, Ukraine and Nigeria, indicating Bitcoin's limited effectiveness as an inflation hedge. Contrastingly, the study identifies a significant positive relationship between Bitcoin and inflation in Canada and India, indicating potential hedging against inflation within these economies. Therefore, investors, portfolio managers, and policymakers should consider these country-specific findings when evaluating Bitcoin's role as an inflation hedge. Furthermore, this study contributes valuable insights into cryptocurrencies and their potential in financial risk management.

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Section: Model Identification and Assumptions Testingmentioning

confidence: 99%

An Empirical Investigation of Bitcoin Hedging Capabilities against Inflation using VECM: The Case of United States, Eurozone, Philippines, Ukraine, Canada, India, and Nigeria

Gbolahan

2023

IJEFI

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“…Inflation can be stated to be short-term and long-term, hence, it necessitates both anticipation and action to avert extreme highs and lows. The key to maintaining inflation within manageable bounds lies in the formulation of economic policies aimed at inflation control (Rahman et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…The key to maintaining inflation within manageable bounds lies in the formulation of economic policies aimed at inflation control (Rahman et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

Arif,

Herlinawati,

Devianto

et al. 2024

Front. Big Data

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Inflation is capable of significantly impacting monetary policy, thereby emphasizing the need for accurate forecasts to guide decisions aimed at stabilizing inflation rates. Given the significant relationship between inflation and monetary, it becomes feasible to detect long-memory patterns within the data. To capture these long-memory patterns, Autoregressive Fractionally Moving Average (ARFIMA) was developed as a valuable tool in data mining. Due to the challenges posed in residual assumptions, time series model has to be developed to address heteroscedasticity. Consequently, the implementation of a suitable model was imperative to rectify this effect within the residual ARFIMA. In this context, a novel hybrid model was proposed, with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) being replaced by Long Short-Term Memory (LSTM) neural network. The network was used as iterative model to address this issue and achieve optimal parameters. Through a sensitivity analysis using mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), the performance of ARFIMA, ARFIMA-GARCH, and ARFIMA-LSTM models was assessed. The results showed that ARFIMA-LSTM excelled in simulating the inflation rate. This provided further evidence that inflation data showed characteristics of long memory, and the accuracy of the model was improved by integrating LSTM neural network.

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Modeling Inflation in Bangladesh

Cited by 2 publications

References 21 publications

An Empirical Investigation of Bitcoin Hedging Capabilities against Inflation using VECM: The Case of United States, Eurozone, Philippines, Ukraine, Canada, India, and Nigeria

An Empirical Investigation of Bitcoin Hedging Capabilities against Inflation using VECM: The Case of United States, Eurozone, Philippines, Ukraine, Canada, India, and Nigeria

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

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