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

RESOURCES POLICY 2023年 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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Modeling and forecasting commodity market volatility with long-term economic and financial variables

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JOURNAL OF FORECASTING 2020年第2期39卷 126-142页

作者： Duc Khuong Nguyen Walther, Thomas IPAG Business Sch IPAG Lab 184 Blvd St Germain F-75006 Paris France Indiana Univ Sch Publ & Environm Affairs 107 S Indiana Ave Bloomington IN 47405 USA Univ St Gallen Inst Operat Res & Computat Finance CH-9000 St Gallen Switzerland Tech Univ Dresden Fac Business & Econ D-01062 Dresden Germany

This paper investigates the time-varying volatility patterns of some major commodities as well as the potential factors that drive their long-term volatility component. For this purpose, we make use of a recently proposed generalized autoregressive conditional heteroskedasticity-mixed data sampling approach, which typically allows us to examine the role of economic and financial variables of different frequencies. Using commodity futures for Crude Oil (WTI and Brent), Gold, Silver and Platinum, as well as a commodity index, our results show the necessity for disentangling the short-term and long-term components in modeling and forecasting commodity volatility. They also indicate that the long-term volatility of most commodity futures is significantly driven by the level of global real economic activity as well as changes in consumer sentiment, industrial production, and economic policy uncertainty. However, the forecasting results are not alike across commodity futures as no single model fits all commodities.

关键词： commodity futures GARCH long-term volatility macroeconomic effects mixed data sampling

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The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach

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JOURNAL OF FORECASTING 2013年第7期32卷 600-612页

作者： Asgharian, Hossein Hou, Ai Jun Javed, Farrukh Lund Univ Dept Econ S-22007 Lund Sweden Knut Wicksell Ctr Financial Studies S-22007 Lund Sweden Southern Denmark Univ Dept Econ & Business Odense Denmark Lund Univ Dept Stat S-22007 Lund Sweden

This paper applies the GARCH-MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low-frequency macroeconomic information in the GARCH-MIDAS model improves the prediction ability of the model, particularly for the long-term variance component. Moreover, the GARCH-MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： mixed data sampling long-term variance component macroeconomic variables principal component variance prediction

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Conditionally Efficient Estimation of Long-Run Relationships Using mixed-Frequency Time Series

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ECONOMETRIC REVIEWS 2016年第6期35卷 1142-1171页

作者： Miller, J. Isaac Univ Missouri Dept Econ 118 Profess Bldg Columbia MO 65211 USA

I analyze efficient estimation of a cointegrating vector when the regressand and regressor are observed at different frequencies. Previous authors have examined the effects of specific temporal aggregation or sampling schemes, finding conventionally efficient techniques to be efficient only when both the regressand and the regressors are average sampled. Using an alternative method for analyzing aggregation under more general weighting schemes, I derive an efficiency bound that is conditional on the type of aggregation used on the low-frequency series and differs from the unconditional bound defined by the full-information high-frequency data-generating process, which is infeasible due to aggregation of at least one series. I modify a conventional estimator, canonical cointegrating regression (CCR), to accommodate cases in which the aggregation weights are known. The correlation structure may be utilized to offset the potential information loss from aggregation, resulting in a conditionally efficient estimator. In the case of unknown weights, the correlation structure of the error term generally confounds identification of conditionally efficient weights. Efficiency is illustrated using a simulation study and an application to estimating a gasoline demand equation.

关键词： Canonical cointegrating regression Cointegration mixed data sampling mixed-frequency series Price elasticity of gasoline demand Temporal aggregation C13 C22 Q41

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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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Predicting Financial Distress Using a MIDAS Hazard Model: Evidence from Listed Companies in China

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EMERGING MARKETS FINANCE AND TRADE 2023年第4期60卷 678-687页

作者： Li, Xiangrong Zhang, Maojun Nan, Jiangxia Yang, Qingyuan Guangxi Univ Sch Econ Nanning Peoples R China Suzhou Univ Sci & Technol Sch Business Suzhou Peoples R China Nanning Coll Vocat Technol Sch Finance & Econ Nanning Peoples R China Suzhou Univ Sci & Technol Sch Business Xuefu Rd 99 Suzhou 215009 Peoples R China

This study aims to predict financial distress in an emerging country using data on ST listed companies in China from 2001 to 2021. A new Aalen hazard model with mixed data sampling (MIDAS) is adopted to investigate the impact of monthly macroeconomic variables and quarterly financial variables on financial distress. The empirical results show that the current ratio, operating profit ratio, current capital ratio, retention ratio, profit ratio and income ratio of listed companies have a significant impact on the time-varying intensity of financial distress. The consumer price index has a negative relation with the intensity of financial distress, while the production price index and credit spreads have a positive influence. Finally, the results of the robustness tests are consistent with those with different lag orders.

关键词： Financial distress Aalen model mixed data sampling special treatment C52 G32 G33

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mixed frequency evidence of the tourism growth relationship in small Island developing states: a case study of Tonga

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ASIA PACIFIC JOURNAL OF TOURISM RESEARCH 2021年第3期26卷 294-307页

作者： Kumar, Nikeel Nishkar Chandra, Ravinay Amit Patel, Arvind Univ South Pacific Fac Business & Econ Sch Accounting & Finance Suva Fiji Univ Fiji Sch Business & Econ Dept Management Saweni Fiji

In this study, we examine the effect of tourism on economic growth in Tonga. We apply Ghysels Beta-MIDAS approach to estimate the elasticity of tourism, identify breaks with the multiple break test, and test for causality using the mixed frequency VAR approach. We utilize a sample from 1995 quarter 1 to 2018 quarter 4. The results indicate that the long-run effect of tourism is 0.06. Two structural breaks are identified, and both have negative growth effects. Unidirectional causality from tourism to real per-capita GDP is noted. The findings indicate that Tonga's economic growth can be improved by promoting tourism.

关键词： Tourism led growth hypothesis tourism development seasonality and quarterly data mixed data sampling mixed frequency cointegration and causality airline and cruise-ship arrivals structural breaks small island developing states Asia Pacific region Tonga

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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

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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