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作者： Benjamin Cann University Of Victoria

学位级别：硕士

Potentially valuable information about the underlying data generating process of a dependent variable is often lost when an independent variable is transformed to fit into the same sampling frequency as a dependent variable. With the mixed data sampling (MIDAS) technique and increasingly available data at high frequencies, the issue of choosing an optimal sampling frequency becomes apparent. We use financial data and the MIDAS technique to estimate thousands of regressions and forecasts in the quarterly, monthly, weekly, and daily sampling frequencies. Model fit and forecast performance measurements are calculated from each estimation and used to generate summary statistics for each sampling frequency so that comparisons can be made between frequencies. Our regression models contain an autoregressive component and five additional independent variables and are estimated with varying lag length specifications that incrementally increase up to five years of lags. Each regression is used to forecast a rolling, one and two-step ahead, static forecast of the quarterly Yen and U. S Dollar spot exchange rate. Our results suggest that it may be favourable to include high frequency variables for closer modeling of the underlying data generating process but not necessarily for increased forecasting performance.

关键词： mixed data sampling forecasting model selection criteria time-series yen dollar exchange rate

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Quantitative Easing in Developed Countries and Middle Income Countries' financial markets

Quantitative Easing in Developed Countries and Middle Income...

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作者： Thuthuka Ntuli University of the Witwatersrand

学位级别：硕士

This study examines Quantitative Easing policy programs of developed countries and their potential impact on Middle Income Countries through capital inflows. The study specifically focuses on the United States and European Union Quantitative Easing programs and investigates potential effects through the various transmission channels. An Autoregressive Multifactor MIDAS approach is used to carry out the empirical analysis and the study finds that lagged capital inflows are highly significant across the different models run and that there is evidence of transmission of quantitative easing to capital inflows to Middle Income Countries along the portfolio rebalancing and liquidity channels.

关键词： Quantitative Easing Middle Income Countries Financial/Capital inflows mixed data sampling

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Deep factor asset pricing with policy guidance based on multi-source heterogeneous information

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APPLIED SOFT COMPUTING 2024年 159卷

作者： Wang, Zezhou Xu, Qifa Jiang, Cuixia Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Anhui Peoples R China Minist Educ Engn Res Ctr Intelligent Decis Making Hefei 230009 Peoples R China

We propose a novel latent factor pricing model to extract latent pricing factors and corresponding factor loadings from multi -source heterogeneous information through a deep learning architecture. Notably, we pioneer the extraction of policy pricing factors from China's national strategies ("Five -Year Plans", "Government Work Reports", and "Monetary Policy Reports") using natural language processing and a dynamic topic model. The proposed mixed -frequency deep factor asset pricing (MIDAS-DF) model learns from mixed -frequency heterogeneous data and captures nonlinear joint patterns between inputs and outputs, providing more nuanced insights into asset pricing. The empirical analyses of the Chinese A -share market from January 1, 2003 to July 31, 2022 show that the MIDAS-DF model outperforms competing models in pricing individual stocks, various test portfolios, and investment portfolios. The results also demonstrate that low -frequency policy information anchors long-term pricing trends, while high -frequency market and sentiment information refine short-term pricing accuracy. They work together to enhance the pricing performance.

关键词： Asset pricing Multi-source heterogeneous information Policy guidance mixed data sampling Deep learning Latent factor model

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Does high crude oil dependence influence Chinese military expenditure decision-making?

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ENERGY STRATEGY REVIEWS 2021年 35卷 100653-100653页

作者： Wang, Kai-Hua Su, Chi-Wei Qingdao Univ Sch Econ Qingdao Peoples R China

This paper employs the mixed frequency vector autoregression (MF-VAR) to discuss the causal link between crude oil dependence and military expenditure in China. The empirical results demonstrate that the crude oil dependence Granger causes military expenditure and accounts for 66.8% of the long-run forecast error variance decomposition. That means the MF-VAR model owns stronger explanatory and asymptotic powers than conventional VAR with single frequency data. Besides, the impulse-response analysis finds time varying relationship between crude oil dependence and military expenditure across four quarters within one year period. Therefore, the Chinese People's Liberation Army, especially its Navy, is needed to take responsibility and active activities to assure crude oil transport security. Government, enterprises and institutes should increase investment in military innovation to promote the upgrading of weapons and equipment. The peaceful rise strategy should be reasserted to construct harmonious international relations, which is helpful for crude oil security, foreign energy investment and reduce unnecessary military expenditure.

关键词： Economic growth Military expenditure Crude oil dependence mixed data sampling mixed frequency vector autoregression

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Economic policy uncertainty, jump dynamics, and oil price volatility

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ENERGY ECONOMICS 2023年第1期120卷

作者： Liu, Feng Shao, Shuai Li, Xin Pan, Na Qi, Yu Hubei Univ Econ Inst Adv Studies Finance & Econ Wuhan 430205 Peoples R China East China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Zhejiang Gongshang Univ Sch Econ Hangzhou 310018 Peoples R China Zhongnan Univ Econ & Law Sch publ Finance & Taxat Wuhan 430073 Hubei Peoples R China

Although the underlying forces behind oil price volatility have attracted the attention of scholars, a clear consensus is yet to be achieved on how to quantify the contributions of economic policy uncertainty (EPU) and jump dynamics simultaneously. This paper develops a joint EGARCH-MIDAS-ARJI model to address this issue and break the obstacle of mixed data sampling. To avoid the effect of political and business cycles, all EPU indices are detrended using the Hodrick Prescott (HP) filter. The results show that oil price volatility is adversely associated with EPU. As large EPU values imply that policymakers are trying to make some beneficial efforts, this finding challenges the neoclassical opinion that policy intervention is a source of market fluctuation. We also find that the US policy intervention can reduce only less than 6% of oil price volatility, while the effects of China's economic policy are even weaker. More importantly, jump dynamics still account for a remarkable percentage in oil price volatility, especially during lower volatility periods. Possible explanations are that policy makers only respond to large persistent oil price volatility, while the targets and actions of oil-importing countries and oil-exporting countries are also converse in most cases.

关键词： Oil price volatility Economic policy uncertainty Jump dynamics mixed data sampling China US

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Technology shocks-Gold market connection: Is the effect episodic to business cycle behaviour?

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RESOURCES POLICY 2023年第1期84卷

作者： Ayinde, Taofeek O. Olaniran, Abeeb O. Abolade, Onomeabure C. Ogbonna, Ahamuefula Ephraim Fountain Univ Dept Econ Osogbo Osun Nigeria Ctr Econometr & Appl Res Ibadan Nigeria Cent Bank Nigeria Dept Stat Abuja Nigeria Univ Ibadan Dept Stat Ibadan Nigeria Ctr Econometr & Appl Res Ibadan Nigeria

We explore the connection between technology shocks (TS) and gold return volatility using the various variants of the newly developed TS data and covering several decades from 1950. The consideration for a long range of data affords us to test whether or not the nexus is sensitive to business cycle dynamics. We employ the GARCHMIDAS framework to establish the connection owing to the mixed data frequencies of the variables of interest. Overall, we find that technological innovations drive higher volatilities in the gold market from a long term perspective. Examining the role of business cycles in the nexus, we partition the analyses into expansion and recession periods. Consequently, we show that the role of business cycles is indeed crucial as the TS increase the level of gold volatility during the period of expansion relative to the period of recession. This justifies that improvements in the economy may further stimulate technological innovations, and by extension, enhance the trading activity in the gold market. Finally, we also demonstrate the forecast gains of including the TS in the predictive model of gold volatility rather than subsuming it in the (aggregate) market risk. Our results are robust to multiple out-of-sample forecast horizons and alternative measures of gold returns, and technology shocks.

关键词： Technology shock Business cycle Gold market Volatility mixed data sampling GARCH-MIDAS Forecasting analysis

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Diaspora investments in low & high interest rate environments

RESEARCH IN GLOBALIZATION

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RESEARCH IN GLOBALIZATION 2022年 5卷

作者： Adediran, Idris A. Okunade, Solomon O. Aor, Raymond L. Ctr Econometr & Appl Res Ibadan Nigeria Chrisland Univ Dept Econ Abeokuta Nigeria Benue State Univ Dept Econ Makurdi Nigeria

Diaspora investment flows measured as foreign direct investments represent one of the major outcomes of the activities of diaspora investors, entrepreneurs, and venture capitalists in the economy. This paper contributes to the literature with distinct analysis of diaspora investment flows in low interest rate environments (Canada, Denmark, Euro area, Japan, Korea Republic, Sweden and the US) and high interest rate environments (Brazil, China, Colombia, India, Indonesia, Mexico and Turkey). First, we employ the Bounds cointegration analysis to investigate whether diaspora investment integrates either of the two groups of economies. Second, we apply the Toda-Yamamoto causality approach to examine whether the interest rate environment causes diaspora investment inflows. Third, we employ the Autoregressive Distributed Lag-mixed data sampling (ADL-MIDAS) technique to evaluate the role of macroeconomic performance for attracting diaspora investments. We find proof of financial integration of diaspora investments in all the low interest rate economies, whereas the evidence is limited to three countries in the high interest rate environment. We also find that the low interest rate environment (more than the high interest rate environment) engenders diaspora investment inflows and also enhances the positive impact of macroeconomic performance in attracting diaspora investments. We highlight some insightful investment and policy implications from the findings.

关键词： Diaspora investment Interest rate environment Cointegration mixed data sampling

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A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection

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NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE 2020年 51卷 101074-101074页

作者： Jiang, Cuixia Ding, Xiaoyi Xu, Qifa Tong, Yongbo Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei Peoples R China Chainfin Informat Technol Beijing Co Ltd Beijing Peoples R China

This paper develops a novel time-varying multivariate Copula-MIDAS-GARCH (TVM-Copula-MIDAS-GARCH) model with exogenous explanatory variables to model the joint distribution of returns. The model accounts for mixed frequency factors that affect the time-varying dependence structure of financial assets. Furthermore, we examine the effectiveness of the proposed model in VaR-based portfolio selection. We conduct an empirical analysis on estimating the 90%, 95%, 99% VaRs of the portfolio constituted of the Shanghai Composite Index, Shanghai SE Fund Index, and Shanghai SE Treasury Bond Index. The empirical results show that the proposed TVM-Copula-MIDAS-GARCH model is effective to investigate the nonlinear time-varying dependence among those three indices and performs better in portfolio selection.

关键词： Time-varying multivariate copula mixed data sampling MIDAS-Copula GARCH Portfolio VaR

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Economic drivers of volatility and correlation in precious metal markets

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JOURNAL OF COMMODITY MARKETS 2022年 28卷

作者： Dinh, Theu Goutte, Stephane Nguyen, Duc Khuong Walther, Thomas Natl Econ Univ Hanoi Vietnam Univ Paris Saclay UMI SOURCE F-78280 Guyancourt France Paris Sch Business PSB Paris France IPAG Business Sch Paris France Vietnam Natl Univ Int Sch Hanoi Vietnam Univ Utrecht Utrecht Sch Econ Utrecht Netherlands Tech Univ Dresden Fac Business & Econ Dresden Germany

We investigate the time-varying dynamics of the precious metal markets. We employ a mixed data sampling technique to identify the impact of macroeconomic and financial drivers from G7 and BRICS countries on the daily volatility and pairwise correlation of Gold, Silver, Platinum, and Palladium. We find that the U.S. and Chinese economies in particular influence the precious metal markets, but in opposite directions. The stock markets and trade balance of both G7 and BRICS countries, as well as the consumer confidence of G7 countries, are the key drivers for the volatility of precious metals. The most influential drivers for correlation are stock markets, money supply, and the inflation rate. Surprisingly, the economic policy uncertainty does not affect the dynamics as much as expected. Lastly, the global financial crisis in 2008 affected the direction of most of the macroeconomic and financial drivers.

关键词： Precious metals Long-term volatility Long-term correlation Macroeconomic drivers Financial drivers Economic policy uncertainty mixed data sampling

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Economic policy uncertainty and stock market volatility in China: Evidence from SV-MIDAS-t model

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INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS 2024年 92卷

作者： Wang, Nianling Yin, Jiyuan Li, Yong Capital Univ Econ & Business Sch Finance Beijing Peoples R China Renmin Univ China Sch Econ Beijing Peoples R China

This study combines the stochastic volatility (SV) model with a mixed data sampling (MIDAS) structure under tdistribution to investigate the effect of economic policy uncertainty (EPU) on Chinese stock market volatility. Furthermore, we compare eight volatility models regarding using "GARCH or SV" model, "with or without" the MIDAS structure, under "normal or t" distributions. The model comparison results show that SV-MIDAS-t model is the best in terms of data fitting, AIC, BIC, and various loss function criteria. Based on the SV-MIDAS-t model, we find that a rise in EPU index significantly increases the long-term component of volatility, and this impact has a time lag effect.

关键词： Economic policy uncertainty Stochastic volatility mixed data sampling Efficient importance sampling Model comparison

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