Price Spillovers Research Articles

Dynamics of spillover network among oil and leading Asian oil trading countries’ stock markets

Abstract Asia as the largest continent which has the highest consumption of energy, so analyzing the impact of spillover and energy crisis is crucial for this region. In this article the volatility spillovers connections among crude oil, leading Asian oil-exporting and oil-importing countries’ stock exchanges are scrutinized. This study applies the VAR-DCC-GARCH model to examine the spillover network structure with the nine years of daily data from September 01, 2009 to August 31, 2018. The results of these analyses have been displayed the network connections using complex networking technique. Overall strong spillover relations were found among oil-exporting as well as with oil prices. The strong interconnection is alarming for these economies, it is recommended to diversify their dependence on other sectors of the economy too. On the other side, oil-importing countries documented low as well as fewer interconnections and also with oil prices. It means these economies are more self-dependent, they also need to make long term planning to deal with future crisis shocks. Further, the findings of this study will provide an insight to researchers and policymakers regarding the spillover networking among oil and leading Asian trading countries’ stock markets. The networking of the oil price spillover can be extended for the Asian oil trade and subsequent to the whole world.

The Ripple Effect and Spatiotemporal Dynamics of Intra-Urban Housing Prices at the Submarket Level in Shanghai, China

The ripple effect of housing price movements between cities has been extensively investigated, but there are relatively few studies on this topic within a metropolitan context, especially at the submarket level. This paper describes the use of ripple effect theory to examine the diffusion process and convergence of intra-urban housing prices at the submarket level in Shanghai, an emerging global city in China. The analysis is based on directed acyclic graphs, local indicators of spatial association time-paths, and a recently developed convergence test. The empirical results of grouping analysis identify 25 submarkets in Shanghai, and the diffusion of housing prices between these submarkets is found to be caused by both geographical and economic proximities. There is also a complex recursive process of price spillovers from high- to low-priced submarkets, and vice versa, which contributes to the spiraling local housing prices. Housing prices diverge across all submarkets, and the whole market can be divided into three convergence clubs. Finally, these convergence clubs have a circular structure with a degree of continuity. This study broadens our knowledge of the price interrelationship among housing submarkets at the intra-urban level. These findings have profound implications for urban planners, policy makers, and local residents.

Housing market shocks in italy: A GVAR approach

In this paper, we use a Global Vector Autoregression (GVAR) model to assess the spatio-temporal mechanism of house price spillovers, also known as “ripple effect”, among 93 Italian provincial housing markets, over the period 2004−2016. In order to better capture the local housing market dynamics, we use data not only on house prices but also on transaction volumes. In particular, we focus on estimating, to what extent, exogenous shocks, interpreted as negative housing demand shocks, arising from 10 Italian regional capitals, impact on their house prices and sales and how these shocks spill over to neighbours housing markets. The negative housing market demand shock hitting the GVAR model is identified by using theory-driven sign restrictions. The spatio-temporal analysis carried through impulse response functions shows that there is evidence of a “ripple effect” mainly occurring through transaction volumes.

Real Exchange Rates, Oil Price Spillover Effects, and Tripolarity

ABSTRACT The real exchange rates of the currencies of non-Eurozone countries are determined to a large extent by the behavior of the euro, because intra-EU trade accounts for a large proportion of the countries’ balances of payments. Given that their currencies and the euro are also influenced by the EUR/USD exchange rate and oil price changes, the tripolar model appears to provide an appropriate analytical framework. The empirical results of this study support the hypothesis that in the long run the euro-dollar and Polish zloty-euro exchange rates are driven by crude oil prices, and that the parities of the real risk-free interest rates determine the euro-dollar exchange rate in the short-run and the zloty-euro exchange rate in the long-run. They also show that in recent years the euro-dollar exchange rate was long influenced by the ECB’s asset purchase program. The impulse response analysis clearly indicates that in addition to shocks from credit default risk premiums and interest rates innovations oil price shocks too are a significant and pervasive source of the nonstationary dynamics of the exchange rates.

Spillovers between US real estate and financial assets in time and frequency domains

PurposeAssessing the strength and time variation of spillovers between returns on residential real estate, real estate investment trusts (REITs), stocks and bonds in the United States. Spillovers reduce the benefits of portfolio diversification, especially in crisis times, when asset returns tend to be more correlated.Design/methodology/approachThe Diebold–Yilmaz approach in the time domain and the Baruník–Krehlík methodology in the frequency domain are used. The latter allows distinguishing spillovers generating only short-lived volatility from those with a more persistent effect.FindingsOn average, spillovers between housing, stock and bond returns are relatively modest and shocks to stock and bond markets affect housing returns more than the other way round, even though with variations over time. Spillovers in both directions are much stronger between REITs and stocks than between REITs and housing. Shocks originating in the housing market are most persistent, particularly in the aftermath of the subprime crisis.Practical implicationsHousing provides a hedge against volatility in financial (including REITs) markets. However, hedging strategies involving housing need to take into account potential tail events such as the GFC and the investment horizon.Originality/valueTo the best of the knowledge of the authors, this paper is the first to apply the Baruník–Krehlík methodology to real estate price spillovers. Although the Diebold–Yilmaz methodology has been used in several studies on spillovers between residential real estate and financial asset returns, this paper covers a new set of variables and time span.

Optimal price regulations in international pharmaceutical markets with generic competition

In a two-country (home and foreign) model in which the home producer of a branded pharmaceutical product faces generic competition in each market, we analyze home's optimal policy choices regarding two major types of price regulations: external reference pricing (ERP) and direct price controls. Home's nationally optimal ERP policy lowers domestic price while maintaining the firm's export incentive. This ERP policy results in a negative international price spillover that the foreign country can (partly) offset via a local price control. Generic competition in either market reduces home's welfare gain from instituting an ERP policy. Weaker competition abroad or a greater weight on firm profits relative to consumer surplus in home's welfare function makes it more likely that home prefers an ERP policy to a price control. While international integration of national generic markets can improve welfare, such is not the case if it causes home to relax its ERP policy.

Spillover Effects of Fraud Allegations and Investor Sentiment

ABSTRACTWe examine whether a stock price spillover effect spreads through the method of listing or country of origin and whether this spillover effect changes when investor sentiment shifts. Using a sample of fraud allegations against Chinese companies that became public through Chinese reverse mergers (CRMs), we investigate whether firms that experienced negative spillover effects on their stock prices are those from the same country and/or with the same method of listing as the firms accused of fraud. We first show that the negative spillover effect channeled through the firm's country of origin becomes stronger when investor sentiment about Chinese companies becomes pessimistic, as evinced by significant declines in the stock prices of non‐fraudulent Chinese companies, including both CRMs and Chinese IPOs. Second, we show that the negative spillover effects on CRMs are stronger than those on Chinese IPOs and non‐Chinese reverse mergers, suggesting that both country and listing method are applicable to CRMs. Our findings indicate that (i) investor sentiment plays an important role in the spillover process involving fraud allegations and (ii) while the two channels could coexist, negative spillover effects that spread through the country of origin play a more prominent role than those that spread through the method of listing.

Price spillovers between rare earth stocks and financial markets

We study price transmission between rare earth stocks and the base metals, gold, clean energy, oil and global MSCI stock markets. Using a Markov switching vector autoregressive model that accounts for different volatility regimes, our empirical findings reveal that price connectedness between rare earth (RE) and other stock markets differs across volatility regimes. Specifically, in a low-volatility regime: (a) RE stocks are linked to the base metals market, receiving and transmitting sizeable price spillovers, and (b) RE stocks are weakly connected with clean energy, gold, oil and general stock markets. In contrast, in a high-volatility regime, there is increased RE price co-movement with price oscillations in the clean energy, oil and general stock markets. These findings have implications for hedging risk associated with positions in RE companies using other financial assets.

Cross‐city spillovers in Chinese housing markets: From a city network perspective

AbstractCross‐city spillovers among housing markets are usually modelled by the classical spatial autoregressive models, which usually suffer from identification problems in practice. This paper investigates the cross‐city house price spillovers arising from city network externalities wherein a city's connections with other cities in the urban network create the external house price premium through productivity and amenity gains. Using a cross‐sectional data set for an urban system in eastern China, we present significant evidence for positive network spillovers by the application of spatial lag of X model and spatial Durbin error model. Besides, common shocks are also proved to be responsible for cross‐city dependence of house prices.

The Impact of International Oil Prices on the Stock Price Fluctuations of China’s Renewable Energy Enterprises

In order to address a series of issues, including energy security, global warming, and environmental protection, China has ranked first in global renewable investment for the seventh consecutive year. However, developing a renewable energy industry requires a significant capital investment. Also, the international oil price fluctuations have an important impact on the stock prices of renewable energy firms. Thus, in order to provide implications for market investment as well as policy recommendations, this paper studied the spillover effect of international oil prices on the stock prices of China’s renewable energy listed companies. We used a Vector Autoregressive (VAR) model with innovations using a Factor-GARCH (Generalized Autoregressive Conditional Heteroskedasticity) process to evaluate the impact of market co-movements and time-varying volatility and correlation between the international oil price and China’s renewable energy market. The results show that the international oil price has a significant price spillover effect on the stock prices of China’s renewable energy listed companies. Moreover, the fluctuations of international oil prices have an influence on the stock price variations of Chinese renewable energy listed companies.

Interdependence Between Renewable-Energy and Low-Carbon Stock Prices

In the transition to a low-carbon economy, climate-resilient investors may be inclined to buy renewable-energy or other low-carbon assets. As the diversification benefits of investment positions in those assets depend on interdependence between their market prices, we explore that interdependence in the European and USA stock markets. We model the dependence structure using bivariate copula functions and evaluate price spillovers between those markets using a conditional quantile dependence approach that accounts for the reciprocal effects of price movements in those markets under normal and extreme market scenarios. Our empirical evidence for the period 2010–2019 indicates that European renewable-energy and low-carbon stocks co-move; upward and downward movements in low-carbon asset prices have sizeable effects on renewable-energy asset prices, and vice versa, although effects are smaller. In contrast, for the USA we find evidence of non-interdependence, with no significant upward or downward price spillover effects between renewable-energy and low-carbon stocks. Our empirical findings provide useful insights for the design of carbon-resilient portfolios and risk management strategies, and also for implementation of public funding policies to support the transition to a low-carbon economy.

Price connectedness between green bond and financial markets

We study price connectedness between the green bond and financial markets using a structural vector autoregressive (VAR) model that captures direct and indirect transmission of financial shocks across markets. Using heteroskedasticity to identify the structural VAR model parameters, our empirical findings reveal that the green bond market is closely linked to the fixed-income and currency markets, receiving sizeable price spillovers from those markets and transmitting negligible reverse effects. We also show that, in contrast, the green bond market is weakly tied to the stock, energy and high-yield corporate bond markets. These findings have implications in terms of portfolio and risk management decisions for environmentally aware investors holding positions in green bonds.

The role of the world’s major steel markets in price spillover networks: an analysis based on complex network motifs

In recent years, the international steel market has shown increasingly strong cross-regional correlation. To better understand the price trends of various markets, it is necessary to identify their inherent price spillovers. This paper combines a generalized autoregressive conditional heteroskedasticity Baba–Engle–Kraft–Kroner (GARCH-BEKK) model and complex network motifs to explore the price fluctuations among international steel markets. The study selects steel markets in 12 countries and regions and uses daily data on import and export prices from January 2009 to September 2017 to analyze eight steel products. The results show that spillovers are associated with geographical location, market development, product type and status. Spillovers mostly occur between buyer’s markets; additionally, the Asian market, especially the East Asian market, is in most cases the recipient of spillover, whereas the European Union (EU) market is in most cases the sender of spillover effects. Developed markets have clear spillover effects on emerging markets, sheet steel products have clear spillover effects on profile steel products, and the prices of midstream and downstream products in the industrial chain are the most influenced. This paper examines international steel market relationships from the perspective of price transmission, and the results can help manage and prevent large-scale economic risks.

Estimating Demand with Substitution and Intraline Price Spillovers

Problem definition: Standard choice models used to optimize product line prices are effective at capturing the effects of prices on the affordability of products but do not incorporate intraline price spillovers, or the effects of prices on consumer preferences for branded product lines. To date, no approach exists to measure such spillovers, and thus their existence and characteristics are subject to controversy. Academic/practical relevance: Different authors have proposed that different product line items, here referred to as known value items (KVIs), generate price spillovers. Yet product line prices and quality levels are typically highly correlated, and thus it is not clear which spillovers should be modeled. No empirical approach exists to test for multiple price spillovers simultaneously while controlling for quality spillovers. Methodology: This study proposes an empirical approach that includes a choice model to abstract rich demand patterns and an estimation algorithm that addresses the endogeneity and collinearity of KVI prices. An empirical application based on vehicle choice data illustrates how the approach can be implemented in practice and demonstrates the importance of modeling spillovers while yielding interesting managerial implications. Results: The results establish that different KVIs generate separate spillovers and illustrate their distinct roles. KVI price spillovers can be economically important, inducing large cross-price elasticities that range from −1.31 to 0.72. Furthermore, neglecting spillovers can bias own-price elasticity estimates by up to 15% and cross-price elasticities by more than 100%. Managerial implications: The empirical results generate KVI-specific insights that managers in the automobile industry can use to improve their pricing strategies. For other industries, the paper proposes an approach and an implementation algorithm that managers can use to estimate spillovers and optimize product line prices. The empirical application illustrates the implementation of the approach and shows how managers can model price spillovers to increase profits by almost 3%.

IOC lets contracts for units and catalysts at Indian refineries to McDermott Int’l

Transmission of price shocks among related markets is a necessary condition for economic efficiency. Earlier empirical studies have investigated price spillovers in green coffee markets across physical space, quality space, and/or time through highly restrictive models. The present work examines price transmission “at the extremes” using penalized quantile regressions, an econometric tool that allows one to analyze complex market networks. The analysis relies on daily green coffee prices from two geographically separated markets (USA and EU), four coffee varieties, and two futures markets. The empirical results suggest: (a) price risk spillovers are stronger when quality differentiation is low and when green coffee varieties are traded within the same spatial market; (b) higher quality varieties are net-transmitters of price risk to lower quality ones while futures markets are net-receivers of price risk from spot markets; and (c) asymmetric price spillovers are more likely to occur for coffee beans traded in different spatial markets.

Connectedness among crude oil prices, stock index and metal prices: An application of network approach in the USA

We investigate the connectedness among crude oil prices, stock index and metal prices covering the period of 1990M1-2017M3 for US economy applying time domain Spillover Index framework by Diebold and Yilmaz (2012). We contribute to the literature by developing network-based approach generated from time domain spill-over methods. It is evident that the stock index is neither price spillover receiver nor contributor. Our investigation demonstrates that palladium, gold, platinum and silver are net contributors of volatility spillover whereas crude oil, titanium, steel, and silver are net receivers of volatility spillover. The identification of the net recipient and receiver of volatility spillover appears challenging measured by the magnitudes of net volatility spillovers index in the presence of economic crises including global financial crisis 2008–2009 and European Sovereign Debt Crisis period 2010–2012.

The Determinants of Retail Fuel Prices in the EU Evidence from the Spatial Panel Quantile Regression

We propose a novel spatial panel quantile regression method to investigate the impact of crude oil and carbon prices and neighboring fuel prices on regional retail fuel prices in the EU markets. This approach captures the changing price shock propagation and cross-market dependency of retail fuel markets at different levels of quantiles. Empirically, we show that the impact of these determinants on retail fuel prices are heterogeneous and asymmetric across quantiles. Specifically, we find a positive price spill-over from neighboring countries’ retail fuel markets to the local retail fuel markets (spatial effects) at different quantiles. The impact of carbon futures prices on the regional retail fuel markets is positive with low crude oil prices and increases across quantile levels. Crude oil prices have a positive influence on retail fuel prices, especially at low quantiles. Our fresh results hold critical implications for energy risk management and policy-making processes.

Circuit Breakers on the London Stock Exchange: Do They Improve Subsequent Market Quality?

This paper uses transaction data to estimate the effect of single stock circuit breakers on other stocks that remain in continuous trading (the “spillover effect”). We find that circuit breakers lead to significant trading, volatility and price spillovers for stocks that remain in continuous trading and that this is driven primarily by traders hedging against further mark-to-market losses in the suspended stock. The effect is stronger when market-wide volatility is relatively high, however when stock prices are highly correlated, circuit breakers play a more beneficial role.

Surge Pricing on a Service Platform Under Spatial Spillovers: Evidence From Uber

Ride-sharing platforms employ surge pricing to match anticipated capacity spillover with demand. We develop an optimization model to characterize the relationship between surge price and spillover. We test predicted relationships using a spatial panel model on a dataset from Uber’s operation. Results reveal that Uber’s pricing accounts for both capacity and price spillover. There is a debate in the management community on the efficacy of labor welfare mechanisms associated with shared capacity. We conduct counterfactual analysis to provide guidance in regards to the debate, for managing congestion, while accounting for consumer and labor welfare through this online platform.

The role of price spillovers in the American housing boom

One of the striking features of the last U.S. housing boom was the heterogeneity in the timing of its onset across local markets. In this paper, we exploit this heterogeneity to estimate the extent to which the boom was spread via spatial spillovers from one market to another. Our analysis focuses on spillovers that occur around the time that a local market enters its boom, which we identify using sharp structural breaks in house price growth rates. On the extensive margin, there is evidence that the likelihood of a market booming increases substantially if nearby neighbors boom. On the intensive margin, we also find statistically significant but economically modest effects of the size of a neighbor's boom on subsequent price growth in nearby markets. These affects appear to be unrelated to local market fundamentals, suggesting a potential role for non-rational factors.