The determinants of the benchmark interest rates in China

Shi, Wen

doi:10.1016/j.jpolmod.2018.01.012

Cited by 10 publications

(14 citation statements)

References 20 publications

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“…The 1998 -2002 period is marked by fewer but larger monetary policy shifts with only six loosening adjustments, unlike the post-2002 periods which marked an era of more frequent but small adjustments. This is discussed in Kim and Shi (2018).…”

Section: A Monetary Policy Indexmentioning

confidence: 99%

“…half of the sample in consideration. The Taylor model uses lagged CPI inflation and industrial output for the output gap, which follows the construction method by Kim and Shi (2018). The Taylor model is included as benchmarks due to their importance in the monetary policy literature.…”

Section: Out-of-sample Forecastingmentioning

confidence: 99%

“…Discrete choice models are well suited to modelling the discrete timing and magnitude of central banking decision as shown for the European Central Bank in Gerlach (2007), the Bank of Korea in Kim et al (2016), the Reserve Bank of Australia in Vasnev et al (2013), the US Federal Reserve in Kauppi (2012) and Kim et al (2009) and the People's Bank of China in He and Pauwels (2008). Kim and Shi (2018) conducted out-of-sample forecasts for the benchmark lending and deposit rates using an ordered probit model but do not attempt to forecast an overall PBoC monetary policy stance. Vasnev et al (2013) and Pauwels and Vasnev (2017) demonstrate that forecast combinations of ordered probit models provide greater forecast accuracy than multivariate probit models and do not require a model selection procedure.…”

Section: Introductionmentioning

confidence: 99%

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Predicting China’s Monetary Policy with Forecast Combinations

Pauwels

2019

SSRN Journal

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China's monetary policy is unconventional and constantly evolving as a result of its rapid economic development. This paper proposes to use forecast combinations to predict the People's Bank of China's monetary policy stance with a large set of 73 macroeconomic and financial predictors covering various aspects of China's economy. The multiple instruments utilised by the People's Bank of China are aggregated into a Monetary Policy Index (MPI). The intention is to capture the overall monetary policy stance of the People's Bank of China into a single variable that can be forecasted. Forecast combination assign weights to predictors according to their forecasting performance to produce a consensus forecast. The out-of-sample forecast results demonstrate that optimal forecast combinations are superior in predicting the MPI over other models such as the Taylor rule and simple autoregressive models. The corporate goods price index and the US nominal effective exchange rate are the most important predictors.

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Section: A Monetary Policy Indexmentioning

confidence: 99%

Section: Out-of-sample Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting China’s Monetary Policy with Forecast Combinations

Pauwels

2019

SSRN Journal

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“…Akkaya ( 2018 ) studied the factors influencing TRLIBOR in Turkey between January 2003 and June 2017 using Auto-Regressive Distributed Lag (ARDL) bounding test and defined that real effective FX and change in consumer prices index significantly affect BMR. Moreover, Kim and Shi ( 2018 ) worked on BMR in China between 1987 and 2013 using quarterly data and probit models. They determined that inflation and money growth were the main factors.…”

Section: Literature Reviewmentioning

confidence: 99%

Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms

2021

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Some countries have announced national benchmark rates, while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of 2021. Considering that Turkey announced the Turkish Lira Overnight Reference Interest Rate (TLREF), this study examines the determinants of TLREF. In this context, three global determinants, five country-level macroeconomic determinants, and the COVID-19 pandemic are considered by using daily data between December 28, 2018, and December 31, 2020, by performing machine learning algorithms and Ordinary Least Square. The empirical results show that (1) the most significant determinant is the amount of securities bought by Central Banks; (2) country-level macroeconomic factors have a higher impact whereas global factors are less important, and the pandemic does not have a significant effect; (3) Random Forest is the most accurate prediction model. Taking action by considering the study’s findings can help support economic growth by achieving low-level benchmark rates.

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“…By adopting a dynamic panels, the authors find a significantly negative effects of interest rate on stock returns in developed economies. Similarly, Kim and Shi (2018) examine the determinants of two (2) key benchmark interest rates in China using quarterly data covering the sample period of 1987 to 2013 [46]. By adopting a constrained ordered probit models, the authors estimate a behavioural equation that models its decision making process for revisions of benchmark deposit rate and lending rate.…”

Section: Review Of the Empirical Literaturementioning

confidence: 99%

Macroeconomic Analysis of Interest Rate and Economic Growth in Nigeria: A Time Series Approach

Idris¹

2019

IJFBR

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This study examines the impact of interest rate on economic growth in Nigeria using annual time series data spanning 1980 to 2017. This phenomenon is particularly interesting from a theoretical standpoint as well as for the understanding of financial market mechanisms. The Vector Autoregression (VAR) model and the Granger causality test are employed to estimate the model coefficients and measure the causal relationship among the concerned variables. From the VAR-based impulse response function and its corresponding variance decomposition estimates, result shows the existence of negative relationship between interest rate and economic growth in Nigeria. In addition, the Granger causality test indicates the presence of bi-directional causal relationship between interest rate and economic growth. Consequently, monetary authorities should designed and implement interest rate policies that enhance investment and take into cognisance other elements that retard investment progression. To attain the desired growth level in Nigeria, monetary authorities and policy makers should adopt policy measures that are growth oriented and have the potentials to accelerate the economy to higher productivity and sustainable economic growth.

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The determinants of the benchmark interest rates in China

Cited by 10 publications

References 20 publications

Predicting China’s Monetary Policy with Forecast Combinations

Predicting China’s Monetary Policy with Forecast Combinations

Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms

Macroeconomic Analysis of Interest Rate and Economic Growth in Nigeria: A Time Series Approach

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