Lead-lag Relationship Research Articles

Relations among Bitcoin Futures, Bitcoin Spot, Investor Attention, and Sentiment

This research investigates the function of price discovery between the Bitcoin futures and the spot markets while also analyzing the impact of investor sentiment and attention on these markets. This study utilizes various statistical models to examine the short-term and long-term relations between these variables, including the bivariate Granger causality model, the ARDL and NARDL models, and the Johansen cointegration procedure with a vector error correction mechanism. The results suggest that there is no statistical evidence of price discovery between the Bitcoin spot price and futures, and the term structure of the Bitcoin futures neither enriches nor impairs this lead lag relation. However, the study finds robust evidence of a long-run cointegrating relation between the two markets and the presence of asymmetry in them. Moreover, this research indicates that investor sentiment exhibits a lead lag relation with both the Bitcoin futures and the spot markets, while investor attention only leads to the Bitcoin spot market, without showing any lead lag relation with the Bitcoin futures. These findings highlight the crucial role of investor behavior in affecting both Bitcoin futures and spot prices.

The profitability of lead–lag arbitrage at high frequency

Any lead–lag effect in an asset pair implies that future returns on the lagging asset have the potential to be predicted from past and present prices of the leader, thus creating statistical arbitrage opportunities. We utilize robust lead–lag indicators to uncover the origin of price discovery, and we propose an econometric model exploiting that effect with level 1 data of limit order books (LOBs). We also develop a high-frequency trading strategy based on the model predictions to capture arbitrage opportunities. The framework is then evaluated on six months of DAX 30 cross-listed stocks’ LOB data obtained from three European exchanges in 2013: Xetra, Chi-X, and BATS. We show that a high-frequency trader can profit from lead–lag relationships because of predictability, even when trading costs, latency, and execution-related risks are considered.

Tourist arrival forecasting using feed search information

ABSTRACT The feed index is a weighted sum of the number of reactions (i.e. reading, comments, retweets, likes and dislikes, and so on.) that the content engine actively recommends and distributes to the users. It provides valuable information from big data on the Internet and high marketing value to Destination Marketing Organization as the content can be customized. A large-scale empirical study on the impact of the feed index on tourist arrival forecasting accuracy has been conducted, with a new approach proposed to incorporate the feed index into the tourist arrival forecasting model with higher forecasting accuracy. Firstly, the empirical results suggest that the feed index for different keywords reflects varying tourist preferences and has different impacts on tourist arrival movements, with variant lead-lag relationships. Secondly, the study shows that keywords need to be carefully selected based on theoretical analysis plus new methods such as entropy analysis. Therefore, it is proposed that entropy is employed to select the keywords and time lags, thus helping improve forecasting accuracy.

A SST-constructed Ocean Heat Content index in crossing ENSO spring persistence barrier

The El Niño-Southern Oscillation (ENSO) spring persistence barrier (SPB) describes the feature in which the predictive skills of ENSO decrease significantly in the boreal spring. This paper investigates an index constructed using sea surface temperature (SST), namely SSTH, which is based on tropical Pacific Ocean Heat Content (OHC) in crossing ENSO SPB. Inspired by the dynamical relationship between the tropical Pacific OHC and eastern Pacific SST anomalies, SSTH is constructed by SST anomalies (i.e., Niño3.4 index) to represent OHC. We show that this index leads ENSO SST anomalies by about 10 months, making it effective in crossing ENSO SPB. Particularly, among the 50 ENSO events from 1950 to 2022, 27 years were identified to be caused by SSTH signals. Compared with warm water volume (WWV) or the west of WWV (WWVw), this index is more stable and effective after the 21st century because the effective region of subsurface OHC changed dramatically afterward. However, SSTH avoids this problem as it is constructed by SST anomalies alone. Finally, as SST data is reliable before 1980, SSTH is utilized to study the interdecadal lead-lag relationship between subsurface OHC and ENSO SST.

Global lead-lag changes between climate variability series coincide with major phase shifts in the Pacific decadal oscillation

We show that there are distinct periods when three ocean variability series in the Atlantic and the Pacific Oceans persistently lead or lag each other, as well as distinct periods when ocean variability series lead the rate of changes in global temperature anomaly (∆GTA) and in atmospheric CO2 concentration (1880–2019). The superimposed lead-lag (LL) relations that can be formed from the five climate series (three ocean series, GTA and CO2), ΣLL(10), change directions or weaken synchronously at 6 years: 1900, 1926, 1965, 1977, 1997, and 2013. During the same years, the Pacific decadal oscillation (PDO) changes between positive ( +) and negative (-) phases, but with an additional phase shift in 1947/48. We find bi-decadal oscillations in the rate of change in global temperature, ∆GTA, during the same years. Since the hiatus periods are closely related to the cold phase (-) in PDO, the hiatus periods may also be related to global changes in ocean interactions.

Conditional effects of local and global risk factors on the co-movements between economic growth and inflation: Insights into G8 economies

World economies have experienced rise in uncertainties which has caused misalignments in the already existing nexus between inflation and economic growth. In addition to this, the presence of nonlinearities, asymmetry, heterogeneity, and structural shocks in time series data concerning substantial fluctuations that span systemic crises have rendered time and/or frequency connectedness worthy of investigation. Due to limited studies in this regard, the authors investigated the risk synchronisation among Gross Domestic Product (GDP), Consumer Price Index (CPI), Economic Policy Uncertainty (EPU) and Geopolitical Risk with insights from G8 countries. To achieve the study's purpose, estimation techniques employed included the wavelet approaches (bi-wavelet and partial wavelet), and the wavelet multiple as well as the DCC-GARCH Connectedness approach as robustness. A sample period from January 1997 to August 2021 restricted by consistent data availability was considered. It was discovered that most G8 nations have a comparable relationship between their GDP and CPI. Additionally, significant co-movements between the G8 nations' GDP and CPI straddle crises. Furthermore, the relationship between Russia's GDP and CPI was significantly conditionally influenced by geopolitical risk factors. Own country economic policy uncertainty was the main source of shocks for nations like Canada, France, and the US, whereas, in Germany, Italy, and the UK, Global EPU was a crucial conduit for reducing the lead-lag relationship between GDP and CPI. Outcomes from this study imply that uncertainties pose a more persistent and dynamic challenge to the G8 countries' efforts to achieve sustained economic growth, lessen the negative effects of inflation and deflation, and improve national and regional economic integration.

Intraday price discovery and volatility transmission between the dual-listed stock index futures and spot markets – new evidence from India

PurposeThis study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.Design/methodology/approachThis study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.FindingsThis study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.Practical implicationsThese findings can potentially help market participants, policymakers, stock exchanges and regulators.Originality/valueUnlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

Inflation surge of 2021-2023 and monetary policy in Eastern European countries

This article provides an in-depth analysis of the current global economic landscape, with a particular focus on Moldova, Ukraine, Romania, and Poland. The scope of this article is to examine central banks’ responses to the challenges posed by 2021-2023 inflation. The research employs a combination of scientific methods, including empirical analysis and quantitative research. It utilizes data from reputable sources like the World Bank, National Banks, and National Bureaus of Statistics to examine various economic indicators. Statistical software R is used for data analysis, including Cross-Correlation Function (CCF) analysis. In response to rising inflation, Moldova and Ukraine raised interest rates significantly. Moldova later eased rates gradually, while Ukraine maintained a high rate to safeguard its currency. Romania and Poland, with stronger economies, saw milder rate hikes and less inflation increase compared to other Eastern European nations, adopting a cautious and delayed approach and still not reducing their base rates. A critical aspect of the research is the implementation of cross-correlation function (CCF) analysis, which is used to explore the time-lagged correlations between different economic variables, such as the Base policy rate, Commodity price index and IPC. The analyses conducted have revealed two significant patterns: a deficiency in the proactive adjustment of base rates by central banks in response to inflation and the lead-lag relationship between ascending commodity prices and inflation. These findings underscore the cautious approach taken by central banks and their delayed response to the impact of external factors on inflation dynamics.

The asymmetric relationships between the Bitcoin futures’ return, volatility, and trading volume

This study investigated the asymmetric contemporaneous and lead-lag relationships between return and trading volume as well as return volatility and volume on the Bitcoin futures by analyzing the threshold and smooth transition effects based on the logistic smooth transition regression (LSTR) model with the logistic smooth transition GJR-GARCH framework. The main findings demonstrated that the asymmetric and smooth transition effects existed in both the contemporaneous and lead-lag return-volume and volatility-volume relationships. There were also a one-trading-day to three-trading-day delayed effects from volume to return and volatility. The implication of the findings suggests that there are arbitrage opportunities for investors in the Bitcoin futures market. Furthermore, we confirm that the Mixture of Distribution Hypothesis (MDH) was a better theory than the Sequential Information Arrival Hypothesis (SIAH) in explaining the volatility-volume relationships on the Bitcoin futures. Our findings also showed that the speed of information transmission increased as it got closer to the current trading day. This evidence supported the MDH in the Bitcoin futures market.

Can geopolitical risk stably predict crude oil prices? A multi-dimensional perspective

This study aims to examine the stability of the lead-lag relationship between geopolitical risk and crude oil prices. To this end, a Granger causality test is conducted, using a lag-augmented vector autoregression model and monthly data from 1997 to 2022. The results show a significant full-sample bilateral Granger causality. The recursive expanding window method and the asymmetric decomposition technique are also used to find that the relationship between geopolitical risk and crude oil prices is complex and time-varying, with asymmetry. The wavelet decomposition analysis shows that this complexity is mainly due to short-term structural changes. When these fluctuations are excluded, the impact of geopolitical risk on crude oil prices (and vice versa) no longer shows time-varying characteristics or asymmetry. This stable relationship provides a new perspective for predicting crude oil prices in the face of geopolitical risk. Given the importance of both geopolitical risk and crude oil prices in the fields of economy, finance, and politics, this research and the accompanying results have implications for informed decision-making by investors and policy makers.

Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?

The financial asset return volatility and information field have continued to compare both hypotheses: sequential information arrival hypothesis (SIAH) and the mixture of distribution hypothesis (MDH). However, numerous former studies have not found an appropriate information indicator but just used trading volume as an indirect proxy. The study examines the relationship between Bitcoin return volatility and information flow instead of the trading volume. We apply a text and web mining to get all related 24,316 news items for Bitcoin from 64 news websites. Next, we apply a sentiment analysis of natural language processing (NLP) to generate information flow data to replace the traditional trading volume. Finally, we appropriate vector autoregressive (VAR) models to catch the lead-lag relationship and Spearman Correlation to test contemporaneous nexus. The study results show that Bitcoin return volatility is affected by the negative information flow and parallels SIAH; the positive information flow impacts Bitcoin return volatility and matches MDH. The empirical result benefits investors in making proper investment decisions in Bitcoin, and the gist of the paper fills the gap in academic literature because the aspect of information is still absent in academia.

How do rare earth prices respond to economic and geopolitical factors?

This paper investigates the correlation between rare earth prices, geopolitical risk and global economic activity. The wavelet decomposition technique is used to capture the variables' dynamic correlations in the time and frequency domains. The empirical results show that rare earth price leads the geopolitical risk index positively when rare earth markets encounter supply shocks. However, the lead-lag relationship was reversed after the global pandemic in 2020, suggesting that these two variables may enjoy a mutual relationship in the short run. When geopolitical risk is high, it positively affects the rare earth price, highlighting the strategic and hedging value of rare earth during periods of turmoil caused by geopolitical events. In addition, the wavelet results show that the price of rare earth rises when economic activity is flourishing and decreases when the economy is gloomy. Conversely, the increase in rare earth prices does not inhibit global economic activities due to its broad application prospects in the energy transition. Therefore, policy-makers should establish risk early warning mechanisms to ensure the stable price of rare earth to prevent the adverse impact of geopolitical risks on rare earth security and economic activities.

Leading indicators for the US housing market: New empirical evidence and thoughts about implications for risk managers and ESG investors

We argue that financial risk managers should focus more strongly on developing forward-looking early warning indicator systems for the North American real estate market. Based on time series data from the US housing market that focuses on the subprime crisis and the period directly after this event, we discuss possible information that such early warning indicator systems could be based on and analyze the presence of a lead-lag relationship between US housing starts and the Architectural Billings Index. We find evidence for such a relation using two different approaches, namely Granger causality tests and transfer entropy analyses. We then discuss the implications of our findings for financial risk managers as well as for ESG investors.

The lead–lag relation between VIX futures and SPX futures

We analyze the lead–lag relation between VIX futures and SPX futures. The two futures markets are weakly connected when market volatility is low. By contrast, when volatility is high, their prices are highly negatively correlated, with VIX futures leading SPX futures. However, the tightness of the lead–lag relation prevents the formation of profitable trading strategies in a setup that includes transaction costs. An analysis of the time variation in the lead–lag relation finds that an improvement in the relative liquidity of one market strengthens the lead of that market. Moreover, the hedging activities of market makers influence the lead–lag relation.

Forecasting European stock volatility: The role of the UK

This study investigates the lead–lag relationships of volatility among European stock markets. Using weakly realized variance measures, we examine volatility spillover dynamics between the UK and other major stock markets in Europe, thereby identifying a long-run leading role for the UK market portfolio. Lagged UK volatility can significantly predict volatilities in non-UK countries, whereas lagged non-UK volatility has a limited association with UK volatility. Moreover, pairwise Granger causality estimations, predictive regression specifications, and out-of-sample validations reveal that volatility shocks in the UK are gradually reflected in market fluctuations across Europe with varying market-specific delays. Our findings support the limited attention explanation for the volatility predictability of the lagged UK equity index.

Regional spillover of housing (un)affordability: an empirical study on the residential housing markets for first-time buyers in the U.K

ABSTRACT This study examines the lead–lag relationships between the levels of housing affordability of different regions of the U.K. By utilizing government data, a number of housing affordability indicators are constructed to explore whether spatial diffusion exists between different regional submarkets of first-time homebuyers over the period of 2000 – 2021. The results reveal that during periods of economic expansion, housing unaffordability tended to diffuse from regions of slower economic growth to regions of higher economic development. It is further evident that in the aftermath of the GFC, the London housing market Granger-caused other regional markets in terms of housing (un)affordability. Lastly, the U.K’.s decision to leave the EU in 2016 seems to have led to more divergence between the submarket regions, which in a Granger causal sense, have become less causally correlated in terms of pricing. We conjecture that the causal interactions between the different regional housing submarkets exist with the lead–lag relationships governed primarily by their underlying macroeconomic fundamentals.

Astronomical forcing shaped the timing of early Pleistocene glacial cycles

Glacial cycles during the early Pleistocene are characterised by a dominant 41,000-year periodicity and amplitudes smaller than those of glacial cycles with ~100,000-year periodicity during the late Pleistocene. However, it remains unclear how the 41,000-year glacial cycles during the early Pleistocene respond to Earth’s astronomical forcings. Here we employ a three-dimensional ice-sheet model to simulate the glacial cycles at ~1.6–1.2 million years before present and analyse the phase angle of precession and obliquity at deglaciations. We show that each deglaciation occurs at every other precession minimum, and when obliquity is large. The lead-lag relationship between precession and obliquity controls the length of interglacial periods, the shape of the glacial cycle, and the glacial ice-sheet geometry. The large amplitudes of obliquity and eccentricity during this period helped to establish robust 41,000-year glacial cycles. This behaviour is explained by the threshold mechanism determined by ice-sheet size and astronomical forcings.

Commodity network and predictable returns

AbstractWe investigate the lead–lag relation in the cross‐section of commodity returns. We estimate dynamic and directional networks for 32 commodities and then construct a new predictor termed commodity network momentum, exploring cross‐commodity information spillover. Network momentum positively and significantly predicts future commodity returns, controlling for existing commodity characteristics. Unlike previous lead–lag studies, the predictive relation is consistent with overreaction rather than underreaction. The relation is stronger for attention‐grabbing commodities and commodities with lottery‐like properties and with higher limits to arbitrage. Extrapolation from connected commodities contributes to this predictive relation. Overall, our paper highlights the role of information spillover in commodity return predictability.

Powering Ghana’s future: unraveling the dynamics of electricity generation and the path to sustainable energy

This study investigates powering Ghana’s future: unraveling the dynamics of electricity generation and the path to sustainable energy by estimating endogenous parameters and employing an unrestricted Vector Autoregression (VAR) model. The model examines the linear lead–lag relationships between variables in the Ghanaian electricity sector and power consumption, using data from 2002 to 2021. The results reveal structural long-and-short-run headwinds for the unrestricted models and indicate that the growth rate of the Gross Domestic Product (GDP) and electricity from fossil fuels are directly correlated. Granger causality analysis highlights a feedback relationship between GDP growth rate and electricity from fossil fuel sources. The impulse response function reveals that the GDP growth rate is sensitive to exogenous shocks with lasting effects. Variance decomposition results show that renewable energy without hydropower explains a minimal variance due to shocks, while total global greenhouse emissions account for a significant proportion of the variance due to headwinds. Electricity from fossil fuel sources explains a substantial part of the variance due to headwinds, suggesting Ghana’s overreliance on conventional energy sources. The study forecasts that installed renewable energy capacity will experience considerable growth by 2036, accounting for most of the energy mix. To promote a sustainable energy future, the study recommends implementing fiscal instruments that incentivize renewable energy consumption, gradually diversifying the energy mix towards natural gas as a medium-term transition fuel for grid electricity generation and shifting entirely to renewables in the long time. This research contributes valuable insights into the dynamics of electricity generation in Ghana and provides policy recommendations for sustainable energy development.

Time-Frequency Analysis of COVID-19 Shocks and Energy Commodities

In a time-frequency biwavelet framework, we analysed the short-, medium-, and long-term impacts of COVID-19-related shocks on ten energy commodities (i.e., Brent, crude oil, coal, heating oil, natural gas, gasoline, ethanol, naphtha, propane, and uranium) from January 2020 to April 2022. We document intervals of high and low coherence between COVID-19 cases and the returns on energy commodities across the short-, medium-, and long-term horizons. Low coherence at high frequencies indicated weak correlation and signified diversification, hedging, and safe-haven potentials in the short term of the pandemic. Our findings suggest that energy markets’ dynamics were highly driven by the pandemic, causing significant changes in market returns, particularly across the medium- and low-frequency bands. Furthermore, the empirical results indicate dynamic lead-lag relationships between COVID-19 cases and energy returns between the medium- and long-term horizons, signifying that diversification could be sought through crossinvestment in different energy commodities. The results have significant implications for market participants, regulators, and practitioners.