How Do Housing Returns in Emerging Countries Respond to Oil Shocks? A MIDAS Touch

Salisu, Afees A.; Gupta, Rangan

doi:10.1080/1540496x.2020.1807322

Cited by 7 publications

(2 citation statements)

References 54 publications

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“…It has been increasingly applied to economics, finance and other business areas (e.g. Yang and Zhang, 2014; Bangwayo-Skeete and Skeete, 2015; Zhang and Wang, 2019; Fang et al , 2020; Salisu and Gupta 2021).…”

Section: Notesmentioning

confidence: 99%

Does the conventional money market overnight rate influence the investment rate of Islamic deposits? Evidence from Malaysia

Liu

Shrestha

2022

IMEFM

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Purpose The paper aims to examine whether the daily conventional money market overnight rate influences the monthly investment rate of Islamic deposits in Malaysia. The traditional approach, which averages the high-frequency data to match the low-frequency data, results in information loss for the high-frequency data. Design/methodology/approach The paper uses the mixed data sampling (MIDAS) model to study the relationship between Islamic banking and conventional banking. The Malaysian data are used for the analysis as Malaysia has one of the most developed Islamic financial industries in the world, and it is well-known for its dual banking system. Findings The evidence shows that the conventional overnight rate has a positive effect on the Islamic deposit rate. The results are consistent for Islamic deposit rates with different maturities. The positive aggregate effect holds when the lag length of the daily conventional overnight rate goes up to 90 days. Additional evidence shows that the daily conventional overnight rate has a similar effect on the conventional deposit rate. Originality/value This paper documents that the relationship between Islamic banking and conventional banking is not monotonous. When high-frequency data is averaged with low-frequency data, the non-linear relationship will be masked. It highlights the importance of using high-frequency data to get a detailed picture.

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Section: Notesmentioning

confidence: 99%

Does the conventional money market overnight rate influence the investment rate of Islamic deposits? Evidence from Malaysia

Liu

Shrestha

2022

IMEFM

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“…To the best of our knowledge, this is the first attempt to forecast housing returns in China using aggregated and disaggregated proxies of investor sentiment and other macroeconomic and financial variables, based on a wide array of machine learning methods. The two papers that we could find which have produced out-of-sample forecasting of housing returns for China is that of Wei and Cao (2017) and Salisu and Gupta (2021). While the latter paper shows that monthly disaggregated oil shocks, that is, supply, global economic activity, oil-specific demand, and oil inventory demand, can be used to forecast quarterly housing returns of China based on a mixed-frequency model, the former paper highlights the role of a Google search index (associated with city name plus house price), instead of fundamental macroeconomic or monetary indicators, based on a dynamic model averaging (DMA) framework.…”

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The role of investor sentiment in forecasting housing returns in China: A machine learning approach

Çepni

Gupta

Onay

2022

Journal of Forecasting

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This paper analyzes the predictive ability of aggregate and dis-aggregate proxies of investor sentiment, over and above standard macroeconomic predictors, in forecasting housing returns in China, using an array of machine learning models. Using a monthly out-of-sample period of 2011:01 to 2018:12, given an in-sample of 2006:01-2010:12, we find that indeed the new aligned investor sentiment index proposed in this paper has greater predictive power for housing returns than the a principal component analysis (PCA)-based sentiment index, used earlier in the literature. Moreover, shrinkage models utilizing the dis-aggregate sentiment proxies do not result in forecast improvement indicating that aligned sentiment index optimally exploits information in the dis-aggregate proxies of investor sentiment. Furthermore, when we let the machine learning models to choose from all key control variables and the aligned sentiment index, the forecasting accuracy is improved at all forecasting horizons, rather than just the short-run as witnessed under standard predictive regressions. This result suggests that machine learning methods are flexible enough to capture both structural change and time-varying information in a set of predictors simultaneously to forecast housing returns of China in a precise manner. Given the role of the real estate market in China's economic growth, our result of accurate forecasting of housing returns, based on investor sentiment and macroeconomic variables using state-of-the-art machine learning methods, has important implications for both investors and policymakers.

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Stock markets and exchange rate behavior of the BRICS

Salisu

Cuñado

Isah

et al. 2021

Journal of Forecasting

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Relying on the uncovered equity parity (UEP), we formulate a predictive model that links movements in exchange rate to stock return differential between the domestic market and the foreign (US) market. We also test for any probable asymmetric relationship between the two variables while also accounting for the role of observed common (global) factor such as oil price. We find a positive relationship between stock return differential and exchange rate return for three of the BRICS countries namely Brazil, India and South Africa, thus validating the UEP hypothesis, whereas a contrasting evidence is observed for China and Russia. We further establish the out‐of‐sample predictability of stock return differential for exchange rates of the BRICS while accounting for the role of observed common (global) factor, and asymmetry may further improve the forecast accuracy. The implications of our findings for portfolio diversification and foreign exchange management are highlighted.

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How Do Housing Returns in Emerging Countries Respond to Oil Shocks? A MIDAS Touch

Cited by 7 publications

References 54 publications

Does the conventional money market overnight rate influence the investment rate of Islamic deposits? Evidence from Malaysia

Does the conventional money market overnight rate influence the investment rate of Islamic deposits? Evidence from Malaysia

The role of investor sentiment in forecasting housing returns in China: A machine learning approach

Stock markets and exchange rate behavior of the BRICS

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