Nexus of Gold Price-Exchange rate-interest rate-Oil Price: Lessons for Monetary Policy in Pakistan

Iqbal, Zahid; Akbar, Muhammad; Amjad, Warda

doi:10.52015/nijbm.v16i1.50

“…In addition to its use as an asset, gold has also been extensively addressed in literature as a safeguard or shelter against inflation and currency changes (Dee et al, 2013) [18].An asset is considered to be an inflation hedge when it moves in perfect agreement with inflation. As an alternative definition, a hedge against another asset is considered to be an asset that frequently has a negative or zero correlation to it, while an asset that exhibits those characteristics during times of market stress is viewed as a safe haven (Baur and McDerrmot, 2010) [19].According the Chinese, gold market acts as a hedge, not as a haven for stock and inflation(Dee et al, 2013) [4]. Other researches demonstrate the importance of investments in gold markets as a hedge against changes in a variety of macroeconomic factors including crude oil prices, currency exchange rates, inflation, financial markets like stock exchange and bonds (Beckmann et al, 2015 and Reboredo, 2013) [22? ].…”

Section: Gold As Financial Asset: Hedge Against Inflationmentioning

confidence: 99%

Quantitative Analysis of Long-term Dynamic Relationships between Crude Oil, gold, exchange rates, and stock prices: An Auto Regressive Distributive Lag Approach in Pakistan

Shah,

Channar,

Dahri

et al. 2023

VFAST trans. math.

0

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Since crude oil is Pakistan's main source of imports, the current study empirically investigates the long-term dynamic relationships between changes in international price of crude oil and gold on macroeconomic indicators, particularly the exchange rate and stock market index. Therefore, international oil prices, Brent oil prices are used as a proxy which were obtained from Federal Reserve Economic Data (FRED) database, gold spot prices data were acquired from World Gold Council website, stock market index, KSE-100 index was used as a proxy and the data was collected from Investing.com website, and the data for exchange rate for Pakistani rupee against US Dollar (PKR/USD) was obtained from Investing.com. The sample period included in this study is from the year 2000 to 2021. The paper employs the Auto regressive Distributive Lag (ARDL) technique to analyze the dynamic relationships among the variables. Findings suggest ARDL has been useful in capturing the long-run relationship i.e., crude oil and gold international prices are significant variables and have impact on stock market and exchange rate.

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Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Gur

2024

DSFE

0

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<p>This study aims to apply advanced machine-learning models and hybrid approaches to improve the forecasting accuracy of the US Consumer Price Index (CPI). The study examined the performance of LSTM, MARS, XGBoost, LSTM-MARS, and LSTM-XGBoost models using a large time-series data from January 1974 to October 2023. The data were combined with key economic indicators of the US, and the hyperparameters of the forecasting models were optimized using genetic algorithm and Bayesian optimization methods. According to the VAR model results, variables such as past values of CPI, oil prices (OP), and gross domestic product (GDP) have strong and significant effects on CPI. In particular, the LSTM-XGBoost model provided superior accuracy in CPI forecasts compared with other models and was found to perform the best by establishing strong relationships with variables such as the federal funds rate (FFER) and GDP. These results suggest that hybrid approaches can significantly improve economic forecasts and provide valuable insights for policymakers, investors, and market analysts.</p>

show abstract

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Gur

2024

DSFE

0

View full text Add to dashboard Cite

<p>This study aims to apply advanced machine-learning models and hybrid approaches to improve the forecasting accuracy of the US Consumer Price Index (CPI). The study examined the performance of LSTM, MARS, XGBoost, LSTM-MARS, and LSTM-XGBoost models using a large time-series data from January 1974 to October 2023. The data were combined with key economic indicators of the US, and the hyperparameters of the forecasting models were optimized using genetic algorithm and Bayesian optimization methods. According to the VAR model results, variables such as past values of CPI, oil prices (OP), and gross domestic product (GDP) have strong and significant effects on CPI. In particular, the LSTM-XGBoost model provided superior accuracy in CPI forecasts compared with other models and was found to perform the best by establishing strong relationships with variables such as the federal funds rate (FFER) and GDP. These results suggest that hybrid approaches can significantly improve economic forecasts and provide valuable insights for policymakers, investors, and market analysts.</p>

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Nexus of Gold Price-Exchange rate-interest rate-Oil Price: Lessons for Monetary Policy in Pakistan

Cited by 4 publications

References 39 publications

Quantitative Analysis of Long-term Dynamic Relationships between Crude Oil, gold, exchange rates, and stock prices: An Auto Regressive Distributive Lag Approach in Pakistan

Quantitative Analysis of Long-term Dynamic Relationships between Crude Oil, gold, exchange rates, and stock prices: An Auto Regressive Distributive Lag Approach in Pakistan

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

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