Price Discovery Analysis Research Articles

An Empirical Analysis of Price Discovery and Causal Relationship Between Futures and Spot Market in India

The derivatives market has witnessed many positive experiences over the cash market, and it is leading to the price discovery process. It signifies that the price movements in the derivative segment strongly influence the cash market and that the cash market follows the derivative market for price determination. The price discovery happens in the derivative segment which is one of the functions of the derivative market. Both the futures and spot markets try to maintain equilibrium whenever disequilibrium occurs between the spot and futures market. Nifty IT futures and spot prices exhibit short-run relationship; whenever disequilibrium occurs in the co-integrated series, it is the futures market that makes greater adjustments in order to re-establish the equilibrium.

Price Discovery and Market Efficiency in India's Financial Futures Market within the Derivatives Landscape: An Empirical Analysis

This study offers a robust, long-term analysis of price discovery and the persisting lead-lag relationship between India’s equity futures and spot markets. Utilising monthly data from April 2005 to December 2022, it filters transient noise typically associated with high-frequency data. Information Share and Common Factor Weight methodologies within the Vector Error Correction (VEC) framework consistently reveal the dominance of futures markets in the price discovery process. The insights remain consistent across three distinct periods spanning the global financial crisis and the COVID-19 pandemic. The volatility dynamics and asymmetry effects in the Indian equity futures and spot markets using the Vector Error Correction - Exponential General Autoregressive Conditional Heteroskedastic (VEC-EGARCH) approach uncover notable asymmetry effects, signifying a strong market sensitivity to negative news. This highlights the necessity for comprehensive risk management strategies and stringent regulatory supervision, especially in light of the significant growth and systemic risks in the Indian derivatives market.

An Econometric Analysis of Volatility Discovery

We investigate information processing in the stochastic process driving stock’s volatility (volatility discovery). We apply fractionally cointegration techniques to decompose the estimates of the market-specific integrated variances into an estimate of the common integrated variance of the efficient price and a transitory component. The market weights on the common integrated variance of the efficient price are the volatility discovery measures. We relate the volatility discovery measure to the price discovery framework and formally show their roles on the identification of the integrated variance of the efficient price. We establish the limiting distribution of the volatility discovery measures by resorting to both long span and in-fill asymptotics. The empirical application is in line with our theoretical results, as it reveals that trading venues incorporate new information into the stochastic volatility process in an individual manner and that the volatility discovery analysis identifies a distinct information process than that based on the price discovery analysis.

Panel data measures of price discovery

This paper considers disaggregated price data that are observed not only for multiple markets over extended periods of time, but also for a large number of assets. The previous literature has argued that in such data rich environments, which arise frequently in applied work, the analysis of price discovery can be made more precise by accounting for the panel structure of the data. Moreover, since the individual assets are not that interesting anyways, little is lost by taking the overall panel perspective. These arguments are, however, mainly based on empirical observations, and there is little in terms of econometric support. The purpose of the present study is to fill this gap in the literature. This is done by offering a full-blown econometric analysis of panel analogs of the information share and permanent–transitory measures of price discovery, which are the workhorses of the time series literature. Both measures are shown to be consistent and they support standard normal inference, which is in contrast to the time series case, where such inference is only possible for the permanent–transitory measure.

Gold Exchange Traded Fund - Price Discovery and Performance Analysis

Abstract The paper aims to examine the price discovery process and the performance of Gold Exchange Traded Funds especially with respect to two Gold ETFs, namely, Goldman Sachs Gold Exchange Traded Scheme (GoldBeEs) and SBI Gold Exchange Traded Scheme (SBIGETS), for the period 2009 – 2016. The study has employed Johansen cointegration and Johansen’s Vector Error Correction Model (VECM) for the price discovery analysis. The results of VECM reveal that the spot prices lead the Gold ETFs price during the study period. Tracking Error analysis shows that Gold ETFs have neither outperformed nor underperformed the spot price. Price Deviation analysis indicates that Gold ETFs are trading on an average lower than the spot price of gold. The entire analysis reveals that although the price discovery takes place in the spot market, Gold ETFs have performed as well as physical gold and the slight difference in price with that of Gold is only because of certain fees, which are applicable in the management of Gold ETFs.

An analysis of price discovery between Bitcoin futures and spot markets

This paper analyzes the Bitcoin price discovery process. We collect data on futures and spot prices for the period December 2017 to May 2018 and compute Hasbrouck’s information share and Gonzalo and Granger’s common factor component to quantify the contribution of each market to the price discovery process. Both measures coincide in suggesting that the Bitcoin futures market dominates the price discovery process. We also find that both prices are driven by a common factor that is given by a weighted combination of the futures and spot market. Finally, we observe that deviations from the equilibrium condition equating the futures and spot log-price have predictive ability for the return on the Bitcoin spot price but not on the futures price.

Price discovery in the Chinese gold market

This study conducts price discovery analysis in the Chinese gold market. Our results indicate that Chinese gold market price discovery occurs predominantly in the futures market. The result is robust to numerous different measures of price discovery, namely, information share, component share, and information leadership share. Partitioning the daily trades into three trading sessions, we find that the dominance of the futures market occurs consistently in all trading sessions. Furthermore, we investigate sequential price discovery within the spot and futures markets; finding that price discovery of both markets occurs more in the night trading session.

Intraday price discovery analysis in the foreign exchange market of an emerging economy: Mexico

We analyze intraday price discovery in the spot and futures markets for an emerging economy’s flexible exchange rate: the U.S. dollar–Mexican peso (USD-MXN) rate. The futures and spot markets are cointegrated and significantly driven by the common factor. Both markets rapidly respond to the disequilibrium between markets in a minute. Overall the spot market moderately leads futures in price discovery. We also look at the impact of Mexican government interventions on price discovery and find interventions have a significant impact on price discovery.

Comparative Analysis of Price Discovery in the KOSPI200 Regular and Mini Futures Markets

Commodity ETF, Cumulative Return, Tracking Error, Information Ratio, Inverse ETF

Analysis of price discovery and non-linear dynamics between volatility index and volatility index futures

ABSTRACTThis study utilises a smooth transition vector error correction model with a Generalised AutoRegressive Conditional Heteroskedasticity GARCH model to investigate the price-discovery and non-linear dynamics at different times when a deviation occurs in the co-movement equilibrium between the spot volatility index (VIX) and futures. Our results show support for relative price-discovery contribution from both VIX spot and VIX futures markets. Moreover, we provide evidence that information traders obtain profits by exhibiting more confidence in exploiting large deviations from the equilibrium in these two markets, whereas noise traders expand their mispricing behaviour. Both VIX spot and VIX futures in turn continue to deviate from equilibrium with the existence of large deviations from the co-movement.

Price Discovery in the Chinese Gold Market

This study conducts price discovery analysis in the Chinese gold market. Our result indicates that the price discovery in Chinese gold market occurs predominantly in the futures market. The result is robust to the different measures of price discovery, namely information share, component share, and information leadership share. Partitioning the daily trades into three trading sessions, we find that the dominance of the futures market occurs in all trading sessions. We further investigate the sequential price discovery within the spot market or futures market. We find that the price discovery of gold spot market and gold futures market occur in the night trading session.

Price discovery in Indian commodity market

The purpose of this study is to quantify the price discovery relationship between spot and futures prices of four Indian commodity indices, namely, combined commodity index, metal index, energy index, and agriculture index by employing common factor models. The sample consists of daily closing prices for the period from Oct 21, 2005 to May 29, 2015. Vector error correction model is estimated after conventional stationarity and cointegration tests. Spot and futures prices of all indices are found to be cointegrated except agriculture index which is therefore dropped for price discovery analysis. Results suggest that price discovery takes place in spot market for combined commodity index and metal index, whereas for energy index, price discovery occurs in futures market. This study will help market participants to draw a conclusion about relative efficiency of spot and futures market. This is the first study, to best of knowledge, which is using common factor models to analyse price discovery process in Indian commodity indices.

Price discovery in Indian commodity market

The purpose of this study is to quantify the price discovery relationship between spot and futures prices of four Indian commodity indices, namely, combined commodity index, metal index, energy index, and agriculture index by employing common factor models. The sample consists of daily closing prices for the period from Oct 21, 2005 to May 29, 2015. Vector error correction model is estimated after conventional stationarity and cointegration tests. Spot and futures prices of all indices are found to be cointegrated except agriculture index which is therefore dropped for price discovery analysis. Results suggest that price discovery takes place in spot market for combined commodity index and metal index, whereas for energy index, price discovery occurs in futures market. This study will help market participants to draw a conclusion about relative efficiency of spot and futures market. This is the first study, to best of knowledge, which is using common factor models to analyse price discovery process in Indian commodity indices.

The NYSE Korea ETF Market

This study focused on the Korea ETF which is listed and traded in NYSE (New York stock exchange) and analyzed empirically the price discovery effect of NYSE Korea ETF on the Korean stock market. There was almost no related research even though the Korea ETF listed in NYSE did not only show high correlation with the Korean stock market but also was often used as a predictive tool of the Korean stock market by investors. The significance of this study is in conducting a price discovery analysis on the Korea ETF traded in NYSE by using sample data of ‘iShares MSCI South Korea Capped (symbol: EWY)' of the most abundant liquidity among Korea ETF traded in NYSE. Also, the Korea ETF traded at in Korean nighttime is the spot trading, but since the KOSPI200 nighttime futures are the derivatives trading, there is an implication that the price discovery effect between spot market and derivatives market can be compared by comparing each price discovery effect.

Crossing the Tasman

Purpose – The purpose of this paper is to investigate price discovery for cross-listed stocks on the New Zealand Exchange (NZX) and the Australian Stock Exchange (ASX) and find out the determinants of price discovery between the two markets. Design/methodology/approach – Gonzalo Granger Component Shares and Hasbrouck Information Shares were estimated annually for a sample of 19 cross-listed stocks between 1998 and 2012. Then dynamic panel regressions were used to investigate the driving factors behind price discovery between the NZX and ASX. Findings – Strong downward trends were observed in the contribution to price discovery of the NZX, both for New Zealand firms cross-listing on the ASX and Australian firms cross-listing on the NZX. While in the early years in our sample period, price discovery is dominated by the home market, by 2012, 50 per cent of price discovery for New Zealand firms takes place on the ASX, and the NZX acts as a satellite market for Australian firms. It was also observed that the NZX share of trading activity has a strong positive effect on the NZX level of price discovery, while there is a negative relationship with relative bid–ask spreads. Practical implications – Results suggest that the importance of the NZX relative to the ASX with regards to price discovery is decreasing over time. Given the importance of price discovery for exchanges, such a finding is concerning for the NZX. The determinants of price discovery found in the paper, such as relative volume and spreads, do, however, offer some guidance on how the NZX could regain price discovery. Originality/value – This paper offers a longer and broader analysis of price discovery between the NZX and ASX, two highly integrated markets, and extends previous work by exploring the drivers of price discovery in a panel setting.

A Fractionally Cointegrated VAR Analysis of Price Discovery in Commodity Futures Markets

AbstractIn this paper, we apply the recently developed fractionally cointegrated vector autoregressive (FCVAR) model to analyze price discovery in the spot and futures markets for five non‐ferrous metals (aluminum, copper, lead, nickel, and zinc). The FCVAR model allows for long memory (fractional integration) in the equilibrium errors, and, following Figuerola‐Ferretti and Gonzalo (2010), we allow for the existence of long‐run backwardation or contango in the equilibrium as well, that is, a non‐unit cointegration coefficient. Price discovery can be analyzed in the FCVAR model by a relatively straightforward examination of the adjustment coefficients. In our empirical analysis, we use the data from Figuerola‐Ferretti and Gonzalo (2010), who conduct a similar analysis using the usual (non‐fractional) CVAR model. Our first finding is that, for all markets except copper, the fractional integration parameter is highly significant, showing that the usual, non‐fractional model is not appropriate. Next, when allowing for fractional integration in the long‐run equilibrium relations, fewer lags are needed in the autoregressive formulation, further stressing the usefulness of the fractional model. Compared to the results from the non‐fractional model, we find slightly more evidence of price discovery in the spot market. Specifically, using standard likelihood ratio tests, we do not reject the hypothesis that price discovery takes place exclusively in the spot (futures) market for copper, lead, and zinc (aluminum and nickel). © 2014 Wiley Periodicals, Inc. Jrl Fut Mark 35:339–356, 2015

Price discovery analysis of green equity indices using robust asymmetric vector autoregression

Covering the first commitment period of the Kyoto Protocol (2008–2012), we perform a price discovery analysis to determine Granger causality relationships for a range of prominent green equity indices with the broader equity and commodity markets. Three pivotal contributions are made. Firstly, an expanded database is used that gives greater depth to the price discovery analysis relative to previous literature. Prominent global, regional and sectoral green equity indices are considered, as well as a broader set of commodities including crude oil, natural gas and emissions. The inclusion of natural gas recognises its role as the transition fossil fuel to a low carbon economy. In addition to the main European Union Allowance traded under the EU Emissions Trading Scheme, Certified Emissions Reduction (CER) prices are also included in the emissions database to capture activities under the global Clean Development Mechanism. Secondly, a problem with conventional symmetric vector autoregression is that its implementation commonly leads to large occurrences of insignificant parameters. Therefore, as a first layer of robustness, we utilise an asymmetric vector autoregression model to perform the Granger causality testing, which addresses this limitation by means of allowing different lag specifications among the system variables. Thirdly, explicit recognition is made in our study of the multiple comparisons bias inherent in our high-dimensional testing framework, which is the non-negligible likelihood of identifying statistically significant results by pure chance alone. As a second layer of robustness, we utilise a generalised Holm correction method to control this source of bias. At conventional statistical significance levels, we find that the FTSE 100 and FTSE Global Small Cap equity indices have a causal effect on all of the green equity indices, with limited evidence of causality in the opposite direction. Within the green equity markets, we find evidence that the chosen sectoral index has a Granger causal effect on one of the two global indices considered and also the regional index. This price transmission provides modest evidence that the global green economy is becoming ever more integrated. NBP gas is shown to have a causal effect on all of the green equity indices, whereas we find no such evidence for Brent oil. The former observation may reflect the increasing role of gas as the transition fuel to a low carbon economy, playing a key role in decisions on power generation mix and associated capital investment. Finally, we find no evidence that EUA or CER prices have a causal effect on green stocks, consistent with previous findings and likely reflecting the excessively low prices being commanded for compliance permits in the European emissions markets.

An analysis of price discovery from panel data models of CDS and equity returns

We propose a panel data model of price discovery. We find that the stock market contributes to price discovery in most sectors while the Credit Default Swap (CDS) market contributes to price discovery in only a few sectors. We discover that in sectors where both the stock market and the CDS market contribute to price discovery, it is the stock market that dominates the price discovery process. When we consider investment grade stocks, the importance of the CDS market in price discovery improves but the stock market still dominates the price discovery process. The results for different sizes of stocks generally suggest that both markets are important for price discovery but it is the stock market that dominates. We also find that while the price discovery process was affected by the 2007 global financial crisis, the stock market still dominated the price discovery process. Finally, in an economic significance analysis, we show that investors in the CDS market are able to make relatively more profits from a forecasting model that takes into account price discovery compared to a model that simply ignores the role of price discovery.

Financial crises and the transfer of risk between the private and public sectors: Evidence from European financial markets

The recent financial crisis in Europe has been especially interesting, since it started mainly as a private sector (banking) phenomenon but then evolved into a public (sovereign debt) crisis. Given that prices of a financial asset must reflect the risks associated to said asset, we expect the relationship between financial markets related to “private” and “public” assets to have changed fundamentally during the crisis: private markets should have led the incorporation of information during the early years of the crisis whereas the markets for government securities should have attained preeminence during the years of the sovereign debt crisis. We investigate this change in the leading role of information (risk) incorporation by looking at the relationships between the markets for sovereign CDSs, sovereign bonds and equity for thirteen European countries during the period 2008–2012. Our results suggest that during 2008–2009 equity markets led the process of incorporation of new information but during 2010 this leading role was assumed by sovereign CDS markets, thus suggesting a private-to-public risk transfer during the subprime crisis and a reversal to a public-to-private risk transfer during the sovereign debt crisis. In supplementary analyses we show, first, that the role of CDSs with respect to the other two markets is state dependent, i.e., sovereign CDSs play a stronger role in economies with higher perceived credit risk. Second, we perform a price discovery analysis between CDS markets of the different European countries, showing evidence that during the years 2007–2009 the Spanish CDSs led the price discovery process, while the Italian and French CDSs took over in 2011, results which are consistent with trading volume in the CDS markets.

Are All Credit Default Swap Databases Equal?

AbstractWe compare the five major sources of corporate Credit Default Swap prices: GFI, Fenics, Reuters, CMA, and Markit, using the most liquid single name 5‐year CDS in the iTraxx and CDX indexes from 2004 to 2010. Deviations from the common trend among prices in the different databases are not random but are explained by idiosyncratic factors, financing costs, global risk, and other trading factors. The CMA quotes lead the price discovery process. Moreover, we find that there is not a full agreement among databases in the results of the price discovery analysis between stock and CDS returns.