Lead-lag Relationship Research Articles

PRICE TRANSMISSION OF MELONS (CUCUMIS MELO AND CITRULLUS LANATUS) IN MYANMAR – CHINA BORDER TRADE

Border trade is the main export channel for agricultural products from Myanmar (formerly Burma). With an increasing quantity and value of exports during the last few decades, melons have become one of the major crops in the national export strategy of Myanmar. In this paper, the word “melons” is used to represent both watermelon (Citrullus lanatus) and muskmelon (Cucumis melo) cultivars. No previous research exists on price transmission between these countries, especially for the horticultural produce market. This study aims to measure the magnitude and speed of spatial price adjustment in melons exported from Myanmar to China. Price transmission in the melon export market in Myanmar is analyzed by studying the lead-lag relationship among prices in the border market in Myanmar and the selected wholesale markets in Beijing, China, using a vector autoregressive model (VAR). Prices are transmitted quickly from Myanmar to the selected wholesale markets in Beijing, China. The wholesale market price in Beijing, China lags only one day behind the Myanmar price. In the Myanmar border market, the melon price is affected only by its own lagged price. The adjustment of melon prices in one day suggests that market information is getting passed up the market channel, and there does not appear to be a need for a policy to improve market efficiency.

Joint dynamic topic model for recognition of lead-lag relationship in two text corpora

Joint dynamic topic model for recognition of lead-lag relationship in two text corpora

Dynamics between Direct Industrial Real Estate and the Macroeconomy: An Empirical Study of Hong Kong

Pricing of direct industrial real estate (DIRE) has long been under-researched due to the paucity of analysable data. Compared to other types of real estate, DIRE has often been regarded as more inefficient because of information asymmetry amongst market players stemming from a lack of market transparency. Therefore, pricing of DIRE usually does not follow a random walk and should be more predictable than other types of real estate. Along this line of reasoning, this study empirically investigates the causal relationships between the price-to-rent ratio of DIRE and macroeconomic attributes using cointegration and causality techniques. More specifically, we employ data on the market of Hong Kong to investigate the lead-lag relationships between the price-to-rent ratio of DIRE and a wide spectrum of macroeconomic and financial indicators, including inflation, money supply, national income, exchange rates, performance of housing market and other economic indicators specific to the industrial sector. The results of our statistical tests reveal significant evidence that DIRE is generally moving in syncs with other segments of the economy over time in terms of long-term cointegration. Further, DIRE tends to lag behind the overall macroeconomy in terms of Granger causation with the price-to-rent ratio exhibiting varying lengths of time lag with the macroeconomic determinants. The findings of the study carry important implications for informing property valuation practices and industrial land policy, particularly in designing urban revitalization programmes aimed at optimising industrial land use.

VIX and major agricultural future markets: dynamic linkage and time-frequency relations around the COVID-19 outbreak

PurposeThe purpose of this paper is to examine the volatility spillover and lead-lag relationship between the Chicago Board Options Exchange volatility index (VIX) and the major agricultural future markets before and during the Coronavirus disease 2019 (COVID-19) outbreak.Design/methodology/approachThe methods used were the vector autoregression-Baba, Engle, Kraft and Kroner-generalized autoregressive conditional heteroskedasticity method, the Wald test and wavelet transform method.FindingsThe findings indicate that prior to the COVID-19 outbreak, there was a two-way volatility spillover impact between the majority of the sample markets. In comparison, volatility transmission between the VIX index and the agricultural future market was significantly lower following the COVID-19 outbreak, the authors observed greater coherence at higher frequencies than at lower frequencies, implying that the interdependence between the two VIX indices and the agricultural future market was stronger over a longer time-frequency domain and the VIX’s signalling effect on various agricultural future prices after the COVID-19 outbreak was significantly lower.Originality/valueThe authors conducted the first comprehensive investigation of the VIX’s correlation with major agricultural futures, especially during COVID-19. The findings contribute to a better understanding of the risk transmission mechanism between the VIX and major agricultural commodities futures contracts. And our findings have significant implications for investors and portfolio managers, as well as for policymakers who are concerned about the price of agricultural futures.

Effect of COVID-19 lockdowns on city-center and suburban housing markets: Evidence from Hangzhou, China

In 2020, governments worldwide enforced lockdowns to contain the spread of COVID-19, severely impeding aspects of daily life such as work, school, and tourism. Consequently, numerous economic activities were affected. Before the COVID-19 outbreak, city-center housing markets in areas surrounding popular tourist attractions performed better than did suburban housing markets because of the output of the tourism industry. This study examines the changes in the performance of city-center and suburban housing markets in regions with popular tourist attractions after the lockdown. Specifically, the dynamics of city-center and suburban housing markets in Hangzhou, where West Lake is located, and the changes in the information transfer between these housing markets after the lockdown are explored. Transaction data from January 1, 2019 to September 30, 2020 are used to perform analysis, in which adjusted housing prices and asking prices are employed to measure market performance and sellers’ pricing strategies, and transaction volume and time on the market are used to measure market liquidity and transaction frequency. The results reveal that the effects of lockdowns differ between city-center and suburban housing markets. After the lockdown, a substantial structural change is observed in the suburban housing market; the volatility risk of housing prices decreases substantially, causing an increase in transaction premiums. Housing prices and transaction volume increase in the city-center housing market after the lockdown; this is possibly because of the influence from the overall housing market booms. In addition, because sellers raise their asking prices and the transaction time is extended, the sellers in the city-center housing market are particularly influenced by the disposition effect. This leads to a reversal in the lead–lag relationship between the city center and suburban housing markets in terms of informativeness. Specifically, before the lockdown, the city-center market transfers information to the suburban market, but after the lockdown, the suburban market transfers information to the city-center market. The COVID-19 pandemic has changed the world in many aspects; this paper finds that it will also change the development pattern of the real estate market in different locations.

Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships.

This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility.

Can precious metals hedge geopolitical risk? Fresh sight using wavelet coherence analysis

Based on wavelet coherence analysis, this paper explores whether precious metals can be used to hedge geopolitical risk (GPR) and whether there is an asymmetry in the hedging effect when GPR rises or falls in time and frequency domains. In addition, we obtain the lead-lag relationship between precious metals and GPR under different time-frequency effects. The main findings are summarized as follows. First, gold and silver are good assets to hedge against GPR in the short and medium term. Second, the relationship between GPR and precious metals shows a significant asymmetry when GPR rises and falls. Third, all four precious metals can hedge both GPR and the negative change of GPR in the medium term. Finally, it is worth noting that both gold and silver are leaders in GPR changes. These new findings in this paper provide an essential reference for investors and policymakers from different time scale perspectives.

Lead-Lag Relationship between the Price-to-Rent Ratio and the Macroeconomy: An Empirical Study of the Residential Market of Hong Kong

The price-to-rent (PtR) ratio is one of the most commonly used indicators to assess housing market conditions by policy makers and real estate practitioners. It is often employed as an economic barometer to detect whether a housing bubble exists and determine whether the property market has become unaffordable relative to historical trends. Despite a plethora of research studies on the PtR ratio in the housing literature, relatively little is known about its long-term dynamics with macroeconomic and financial determinants. By utilising time series data on the Hong Kong residential property market, this study examines the cointegration and causal relationships between a wide spectrum of macroeconomic indicators and the PtR ratios of housing segments of different tiers which comprise different socioeconomic groups of homebuyers and investors. The results point towards market compartmentalisation, in the sense that the PtR ratios of the housing submarkets respond to changes in macroeconomic fundamentals in a differential manner. For instance, the PtR ratios of housing segments with a greater proportion of owner-occupiers are statistically less y correlated with investment-related macroeconomic attributes, such as foreign direct investment and equity market performance. On the other hand, the pricing of large-sized housing units in prime locations, generally favoured by investors from mainland China, are found to be Granger-caused by the exchange rate of the Chinese Yuan to the Hong Kong dollar.

Time-frequency analysis of financial stress and global commodities prices: Insights from wavelet-based approaches

We examine the time-frequency lead–lag relationships and the degree of integration between the US financial stress index and global commodity prices (i.e., oil, gold, silver, and cocoa) with data covering over 47 decades (January 1975 to December 2021). For this purpose, we resort to the bi- and multiple wavelet econometric approaches. Findings from the bivariate wavelet analysis evidence the significant influence of the US financial stress in driving the price-generating process in commodities markets. Our findings support the hedging abilities of commodities across the time-frequency space. Findings from the multiple correlations explicate that the interrelation between the commodities and financial stress is attributable to their interdependence in the long term during financial market meltdowns. The dynamic and nonhomogeneous lead/lag relations underscored by our findings highlight the importance of cross-commodity investments. As such, by acknowledging the response of different commodities to financial stress, asset allocation should factor in commodities that offer opposing responses to a financial stress to hedge downside risks associated with portfolios. Our findings are of interest to regulators, risk managers, investors, and commodities producers.

Assessing government policies' impact on the COVID-19 pandemic and elderly deaths in East Asia.

This study assesses governments' long-term non-pharmaceutical interventions upon the coronavirus disease 2019 (COVID-19) pandemic in East Asia. It advances the literature towards a better understanding of when and which control measures are effective. We (1) provide time-varying case fatality ratios and focus on the elderly's mortality and case fatality ratios, (2) measure the correlations between daily new cases (daily new deaths) and each index based on multiple domestic pandemic waves and (3) examine the lead-lag relationship between daily new cases (daily new deaths) and each index via the cross-correlation functions on the pre-whitened series. Our results show that the interventions reduce COVID-19 infections for some periods before the period of the Omicron variant. Moreover, there is no COVID-19 policy lag in Taiwan between daily new confirmed cases and each index. As of March 2022, the case fatality ratios of the elderly group in Japan, Hong Kong and South Korea are 4.69%, 4.72% and 1.48%, respectively, while the case fatality ratio of the elderly group in Taiwan is 25.01%. A government's COVID-19 vaccination distribution and prioritisation policies are pivotal for the elderly group to reduce the number of deaths. Immunising this specific group as best as possible should undoubtedly be a top priority.

Does bitcoin hedge against the economic policy uncertainty: based on the continuous wavelet analysis

ABSTRACT This article aims to test a causal nexus between bitcoin market and economic policy uncertainty. We use the continuous wavelet analysis to investigate lead-lag relationship between bitcoin market and economic policy uncertainty in different time-frequency domains. Our findings show the negative relationship between bitcoin returns and economic policy uncertainty around the period of bitcoin’s currency recognition and COVIC-19 pandemic crisis both daily and monthly time series test. Furthermore, we find that the causality relationship between bitcoin and economic policy uncertainty is relatively indistinct around the period of bitcoin’s currency recognition, while bitcoin returns are leading economic policy uncertainty changes during COVID-19 pandemic crisis, indicating the economic policy uncertainty fluctuation trend can refer to the fluctuation of bitcoin, bitcoin can be viewed as a leading indicator, but it could not be employed as a safe-haven asset hedge against uncertainty during the period of COVID-19 pandemic.

Multiresolutional statistical machine learning for testing interdependence of power markets: A Variational Mode Decomposition-based approach

In the increasingly interconnected and digitized world, the field of electricity price forecasting has benefited from growing research especially due to the market liberalization and the connectedness between electrical systems. This study defines a novel multiscaled forecasting model based upon the Variational Mode Decomposition (VMD) to quantify multiscaled cross-correlation between two important European markets during COVID-19 pandemic. The VMD is known to be a strong information processing tool which is localized in both frequency and time, and is especially used for capturing nonstationary and nonlinear behaviors of time series. The set of new VMD techniques is applied on hourly electricity spot prices from the Nord Pool and MIBEL energy exchanges for the period ranging from January 2019 to March 2020. The sampled time series include a period of specific recession in the financial system, coinciding with the Brexit and COVID-19 event, which was accompanied by a significant collapse in the world’s economic sphere. The empirical results reveal a significant dependence between electricity markets across long- and medium-run investment time horizons, with evidence for dynamic lead–lag relationships at some frequency sub-bands. However, over the short-term (daily and intra-daily intervals), we notice a kind of independence between markets, especially in times of crisis, which offers investors different investment diversification opportunities. On the other hand, the accuracy of generated forecasts prove the interest of a conjoint modeling and the reliability of this new tool, in particular when the approach is adequately coupled with feedforward neural networks.

Time–frequency return co-movement among asset classes around the COVID-19 outbreak: portfolio implications

This study explores the time–frequency return connectedness of the four most relevant asset classes namely, equity, digital assets, commodity, and fixed income. To do so, we use the novel proxies of the S&P500 Index for equity, the S&P Cryptocurrency MegaCAP Index for digital assets, the S&P Goldman Sachs Commodity Index for commodity, and the S&P Global Developed Sovereign Bond Index for fixed income, and also employ the wavelet analysis for daily data over the period 2017: M02 to 2021: M09. In contrast to the pre-COVID-19 period, our findings indicate that the interdependence between the selected asset classes has intensified across all time scales and frequency bands during the COVID-19 crisis, proving the lack of hedging opportunities. Besides, the findings reveal that there is a significant lead-lag relationship between time series at medium and low frequencies during the research period, and the directional connectedness among asset classes is sensitive to frequencies. Especially, the co-movements among the pairs are pronounced during the COVID-19 outbreak. Remarkably, the wavelet-based Granger causality test corroborates the wavelet results and underscores there is a significant causal link between the variables during COVID compared to pre-COVID. Moreover, the results of the portfolio risk analysis by employing the value at risk (VaR) measure indicate that portfolio diversity advantages vary among frequency and across time. The results of the present study provide insight and might help foreign portfolio investors diversify their portfolios across different asset classes.

Investigating long-run cointegration and lead lag relationship between spot and future markets of energy commodities

Investigating long-run cointegration and lead lag relationship between spot and future markets of energy commodities

Multiscale Price Lead-Lag Relationship between Steel Materials and Industry Chain Products Based on Network Analysis.

As two main steelmaking materials, iron ore and scrap steel have different price lead-lag relationships (PLRs) on midstream and downstream steel products in China. The relationships also differ as the time scale varies. In this study, we compare the price influences of two important steel materials on midstream and downstream steel products at different time scales. First, we utilize the maximal overlap discrete wavelet transform (MODWT) method to decompose the original steel materials and products price series into short-term, midterm, and long-term time scale series. Then, we introduce the cross-correlation and Podobnik test method to calculate and test the price lead-lag relationships (PLRs) between two steel materials and 16 steel products. Finally, we construct 12 price lead-lag relationship networks and choose network indicators to present the price influence of the two materials at different time scales. We find that first, most scrap steel and steel products prices fluctuate at the same time lag order, while iron ore leads most steel products price for one day. Second, products that exist in the downstream industry chain usually lead to iron ore. Third, as the time scale becomes longer, the lead relationships from steel materials to steel products become closer.

Time-varying causality between stock prices and macroeconomic fundamentals: Connection or disconnection?

This study investigates the connection/disconnection between the stock market and macroeconomic fundamentals in the United States from January 1960 to December 2021. Using a recent time-varying Granger causality framework, tests revealed asymmetric bidirectionnal causality. The lead-lag relationships between stock prices and key macroeconomic indicators are more prevalent during recession phases. However, the significance and intensity of the causal relationships during the Covid crisis varied greatly; which could indicate a possible disconnection.

How does economic policy uncertainty comove with stock markets: New evidence from symmetric thermal optimal path method

We revisit the dynamic relationship between domestic economic policy uncertainty and stock markets using the symmetric thermal optimal path (TOPS) method. We observe different interaction patterns in emerging and developed markets. Economic policy uncertainty drives the stock market in China, while stock markets play a leading role in the UK and the US. Meanwhile, the lead–lag relationships of the three countries react significantly to extreme events. Our findings have important implications for investors and policy makers.

Financial development and tourism: a century of evidence from Germany

ABSTRACT This article presents findings from the first study to examine the direct effects of financial development on tourism. Using a unique historical dataset for Germany covering 1870 to 2016, we apply an autoregressive distributional lag (ARDL) model with structural breaks. To identify the lead–lag relationship between financial development and tourism, we adopt the wavelet coherence method and the most recently developed Shi, Hurn, and Phillips (2020) time-varying causality test. The ARDL results suggest that, on average, financial development is associated with an increase in tourist arrivals. The wavelet coherence results unveil a significant positive correlation between financial development and tourism in both short- and medium-terms, and financial development leads to tourism growth in Germany. Moreover, the causality results indicate that the positive effect of financial development on tourism is most evident from 2009 onward. Our study provides important implications for policymakers.

The time-varying lead-lag relationship between index futures and the cash index and its factors

This paper investigates the time-varying lead-lag relationship between CSI 300 index futures and the cash index at intraday and daily frequencies under different market conditions, which is crucial in the price discovery research but rarely examined by the literature. Using a new method that is based on dynamic time warping and can capture the dynamic lead-lag relationship up to the intraday level, we find that index futures tend to lead the cash index by 0–5 minutes but it occasionally lags the cash index, and this relationship is variably affected by factors according to market conditions. Specifically, at both of the intraday and daily frequencies, the lead of index futures decreases with market volatility and the relative intensity of trading activity of index futures. The results also unveil the asymmetric effects of overnight information from the cash market on the lead times of both index futures and the cash index at a daily frequency. Moreover, the synchronization of trading hours strengthened the link between the two markets. These results have significant implications for price discovery in these markets.

The Simulated Atmospheric Response to Western North Pacific Sea Surface Temperature Anomalies

Abstract Observations reveal two distinct patterns of atmospheric variability associated with wintertime variations in midlatitude sea surface temperatures (SSTs) in the North Pacific sector: 1) a pattern of atmospheric circulation anomalies that peaks 2–3 weeks prior to large SST anomalies in the western North Pacific that is consistent with “atmospheric forcing” of the SST field, and 2) a pattern that lags SST anomalies in the western North Pacific by several weeks that is consistent with the “atmospheric response” to the SST field. Here we explore analogous lead–lag relations between the atmospheric circulation and western North Pacific SST anomalies in two sets of simulations run on the NCAR Community Earth System Model version 1 (CESM1): 1) a simulation run on a fully coupled version of CESM1 and 2) a simulation forced with prescribed, time-evolving SST anomalies over the western North Pacific region. Together, the simulations support the interpretation that the observed lead–lag relationships between western North Pacific SST anomalies and the atmospheric circulation reveal the patterns of atmospheric variability that both force and respond to midlatitude SST anomalies. The results provide numerical evidence that SST variability over the western North Pacific has a demonstrable effect on the large-scale atmospheric circulation throughout the North Pacific sector.