Market Price Series Research Articles

MARKET INTEGRATION OF SWEET LIME BETWEEN DOMESTIC AND INTERNATIONAL MARKETS

The study focused on the integration of domestic and international markets for sweet limes during 2021. The co-integration of commodity prices was considered to be necessary for the implementation of a successful marketing reform for which both domestic and international market prices are required. Due to the lack of information regarding international prices, export price information was gathered from thewebsite of the Agricultural and Processed Food Products Export Development Authority (APEDA), and secondary data regarding domestic prices of sweet lime was gathered from the website of the Agricultural Marketing Information Network (AGMARKNET). The annual average prices data for the chosen markets were gathered, and the Pearson correlation coefficient and Johansen’s co-integration analysis approaches were used to analyse the relationship between the chosen market pair. Both markets were found to be correlated through correlation analysis, and the findings of the Johansen Co-integration rank, trace test, and eigen value statistics test were used to analyse the integration among the markets. The study’s findings on market integration reveal how closely related the selected market places’ pricing are to one another. Additionally, this research demonstrated that the model’s variables exhibited an equilibrium longrun co-movement with the market price series.

Evaluation of European Union carbon and energy networks: Evidence from market efficiency

This article investigates dynamic networks of the informational efficiency between European Union carbon and energy markets in a quantile-based framework. The market (in)efficiency degree is gauged by capturing the potentially fractional integration order of the market price series using the Feasible Exact Local Whittle estimator on a rolling basis. Rather than relying on a mean-based method, we employ the causality-in-quantiles approach, through which the cross-market linkage of the inefficiency degree is uncovered in a full-distributional characterization. The dynamic linkage is identified through the perspectives of data in return and volatility, respectively. We find that both carbon and energy markets are inefficient, while the inefficiency degree of the former is slightly higher. The inefficiency degree of the two types of markets is featured by asymmetry and non-linearity over the data distribution, while its cross-market linkage is found to be bi-directional. The spillover degree of the dynamic linkage remains the highest at around the median quantile of the “information giver,” while such the degree weakens at the extreme quantiles. Our findings possess insightful implications for effective operations of the emission trading toward a low carbon transition.

Price Volatility Spillovers Among Major Wheat Markets in the World

This research determined price volatility spillovers among major wheat markets in the world using time series data (1966-2018) of six major wheat producing countries in the world. The data were sourced from FAO and UNCTAD databanks and were analyzed using descriptive statistics, multiple regression, unit root test and GARCH models. The findings showed that there is low and high persistence in the wheat prices of Canada and USA; and, Australia and India, respectively. Thus, it was established that the prices in the former markets were characterized by short memory; the effect of shock is temporary as the prices return to the attractor level within a short period. However, bad news on the prices of the latter markets has pronounced effect and takes a longer period for the price series to normalize. On the other hand, French and Chinese market price series exhibited an explosive pattern; the price series have infinite memory and the effect of innovation is permanent as price series will not normalize. Therefore, it can be concluded that the future trade of wheat is useful in the market given the persistence behavior of the prices as their price trends are tailored towards a rational expectation rather than a naïve expectation. However, for the market prices that are explosive, the market participants should focus on rational market expectation as a trade barometer.

Relative cluster entropy for power-law correlated sequences

We propose an information-theoretical measure, the relative cluster entropy \mathcal{D_C}[P\|Q]𝒟𝒞[P∥Q], to discriminate among cluster partitions characterised by probability distribution functions P and Q. The measure is illustrated with the clusters generated by pairs of fractional Brownian motions with Hurst exponents H_1H1 and H_2H2 respectively. For subdiffusive, normal and superdiffusive sequences, the relative entropy sensibly depends on the difference between H_1H1 and H_2H2. By using the minimum relative entropy principle, cluster sequences characterized by different correlation degrees are distinguished and the optimal Hurst exponent is selected. As a case study, real-world cluster partitions of market price series are compared to those obtained from fully uncorrelated sequences (simple Browniam motions) assumed as a model. The minimum relative cluster entropy yields optimal Hurst exponents H_1H1=0.55, H_1H1=0.57, and H_1H1=0.63 respectively for the prices of DJIA, S and P500, NASDAQ: a clear indication of non-markovianity. Finally, we derive the analytical expression of the relative cluster entropy and the outcomes are discussed for arbitrary pairs of power-laws probability distribution functions of continuous random variables.

Price graphs: Utilizing the structural information of financial time series for stock prediction

Great research efforts have been devoted to exploiting deep neural networks in stock prediction. However, long-term dependencies and chaotic properties are still two major issues that lower the performance of state-of-the-art deep learning models in forecasting future price trends. In this study, we propose a novel framework to address both issues. Specifically, in terms of transforming time series into complex networks, we convert market price series into graphs. Then, structural information, referring to temporal point associations and node weights, is extracted from the mapped graphs to resolve the problems regarding long-term dependencies and chaotic properties. We take graph embeddings to represent the associations among temporal points as the prediction model inputs. Node weights are used as a priori knowledge to enhance the learning of temporal attention. The effectiveness of our proposed framework is validated using real-world stock data, and our approach obtains the best performance among several state-of-the-art benchmarks. Moreover, in the conducted trading simulations, our framework further obtains the highest cumulative profits. Our results supplement the existing applications of complex network methods in the financial realm and provide insightful implications for investment applications regarding decision support in financial markets.

Measuring volatility persistence in leveraged loan markets in the presence of structural breaks

This paper examines volatility persistence in leverage loan market price series for Australia, Canada, Europe, Japan, Singapore, UK and USA in the presence of structural breaks. To the best of our knowledge, this is the first empirical study to examine volatility persistence in the leveraged loan markets. To this end, using fractional integration methods, the results indicate that both absolute and squared returns display long memory features, with orders of integration confirming the long memory hypothesis. However, after accounting for structural breaks, we find a reduction in the degree of persistence in the leveraged loan market. The evidence of persistence in volatility implies that market participants who want to make gains across trading scales need to factor the persistence properties of leveraged loan price series in their valuation and forecasting models since that will help improve long-term volatility market forecasts and optimal hedging decisions.

Patterns of cross-correlation in time series: A case study of gait trails**Project supported by the National Natural Science Foundation of China (Grant Nos. 11805128, 11875042, 11505114, and 10975099), the Program for Professor of Special Appointment (Orientational Scholar) at Shanghai Institutions of Higher Learning, China (Grant Nos. D-USST02 and QD2015016), and the Shanghai Project for Construction of Top Disciplines, China (Grant

A complex system contains generally many elements that are networked by their couplings. The time series of output records of the system’s dynamical process is subsequently a cooperative result of the couplings. Discovering the coupling structure stored in the time series is an essential task in time series analysis. However, in the currently used methods for time series analysis the structural information is merged completely by the procedure of statistical average. We propose a concept called mode network to preserve the structural information. Firstly, a time series is decomposed into intrinsic mode functions and residue by means of the empirical mode decomposition solution. The mode functions are employed to represent the contributions from different elements of the system. Each mode function is regarded as a mono-variate time series. All the mode functions form a multivariate time series. Secondly, the co-occurrences between all the mode functions are then used to construct a threshold network (mode network) to display the coupling structure. This method is illustrated by investigating gait time series. It is found that a walk trial can be separated into three stages. In the beginning stage, the residue component dominates the series, which is replaced by the mode function numbered M14 with peaks covering ∼680 strides (∼12 min) in the second stage. In the final stage more and more mode functions join into the backbone. The changes of coupling structure are mainly induced by the co-occurrent strengths of the mode functions numbered as M11, M12, M13, and M14, with peaks covering 200–700 strides. Hence, the mode network can display the rich and dynamical patterns of the coupling structure. This approach can be extended to investigate other complex systems such as the oil price and the stock market price series.

Crop price insurance in China: pricing and hedging using futures market

PurposeThe purpose of this paper is to demonstrate how crop price insurance premium can be calculated using an option pricing model and how insurers can transfer underwriting risks in the futures market.Design/methodology/approachBased on data from spot and futures market in China, this paper develops an improved B-S model for the calculation of crop price insurance premium and tests the possibility of hedging underwriting risks by insurance firms in the futures market.FindingsThe authors find that spot price of crops in China can be estimated with agricultural commodity futures prices, and can be taken as the insured price for crop price insurance. The authors also find that improved B-S model yields better estimation of crop price insurance premium than traditional B-S model when spot price does not follow geometric Brownian motion. Finally, the authors find that hedging can be one good alternative for insurance firms to manage underwriting risks.Originality/valueThis paper develops an improved B-S model that is data-driven in nature. Insured price of the crop price insurance, or the exercise price used in the B-S model, is estimated from a co-integration model built on spot and futures market price series. Meanwhile, distributional patterns of spot price series, one important factor determining the applicability of B-S model, is factored into the improved B-S model so that the latter is more robust and friendly to data with varied distributions. This paper also verifies the possibility of hedging of underwriting risks by insurance firms in the futures market.

Much ado about making money: the impact of disclosure, news and rumors on the formation of security market prices over time

This article develops an agent-based model of security market pricing process, capable to capture main stylised facts. It features collective market pricing mechanisms based upon evolving heterogenous expectations that incorporate signals of security issuer fundamental performance over time. Distinctive signaling sources on this performance correspond to institutional mechanisms of information diffusion. These sources differ by duration effect (temporary, persistent, and permanent), confidence, and diffusion degree among investors over space and time. Under full and immediate diffusion and balanced reaction by all the investors, the value content of these sources is expected to be consistently and timely integrated by the market price process, implying efficient pricing. By relaxing these quite heroic conditions, we assess the impact of distinctive information sources over market price dynamics, through financial systemic properties such as market price volatility, exuberance and errancy, as well as market liquidity. Our simulation analysis shows that transient information shocks can have permanent effects through mismatching reactions and self-reinforcing feedbacks, involving mispricing in both value and timing relative to the efficient market price series. This mispricing depends on both the information diffusion process and the ongoing information confidence mood among investors over space and time. We illustrate our results through paradigmatic cases of stochastic news, before generalising them to autocorrelated news. Our results are further corroborated by robustness checks over the parameter space and across several market trading mechanisms.

MULTIFRACTALS IN WESTERN MAJOR STOCK MARKETS HISTORICAL VOLATILITIES IN TIMES OF FINANCIAL CRISIS

In this paper, the generalized Hurst exponent is used to investigate multifractal properties of historical volatility (CHV) in stock market price and return series before, during and after 2008 financial crisis. Empirical results from NASDAQ, S&P500, TSE, CAC40, DAX, and FTSE stock market data show that there is strong evidence of multifractal patterns in HV of both price and return series. In addition, financial crisis deeply affected the behavior and degree of multifractality in volatility of Western financial markets at price and return levels.

Much Ado About Making Money: The Impact of Disclosure, News and Rumors Over the Formation of Security Market Prices Over Time

This article develops an agent-based model of security market pricing process, capable to capture main stylised facts. It features a collective market pricing mechanism based upon evolving heterogenous expectations that incorporate signals of security issuer fundamental performance over time. Distinctive signaling sources on this performance correspond to institutional mechanisms of information diffusion. These sources differ by duration (temporary, persistent, and permanent), confidence, and diffusion degree among investors over space and time. Under full and immediate diffusion and balanced reaction by all the investors, the value of these sources should be consistently and timely integrated by the market price process, implying efficient pricing. By relaxing these quite heroic conditions, we assess the impact of distinctive information sources over market price dynamics, through financial systemic properties such as market price volatility, exuberance and errancy, as well as market liquidity. Our simulation analysis shows that transient information shocks can have permanent effects through mismatching reactions and self-reinforcing feedbacks, involving mispricing in both value and timing relative to the efficient market price series. This mispricing depends on both the information diffusion process and the ongoing information confidence mood among investors over space and time. We illustrate our results through paradigmatic cases of stochastic news, before generalising them to autocorrelated news. Our results are further corroborated by robustness checks over the parameter space.

Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis

The purpose of this study is to investigate long-range dependence in trend and short variation of stock market price and return series before, during, and after 2008 financial crisis. Variational mode decomposition (VMD), a newly introduced technique for signal processing, is adopted to decompose stock market data into a finite set of modes so as to obtain long term trends and short term movements of stock market data. Then, the detrended fluctuation analysis (DFA) and range scale (R/S) analysis are used to estimate Hurst exponent in each variational mode obtained from VMD. For both price and return series, the empirical results from twelve international stock markets show evidence that long term trends are persistent, whilst short term variations are anti-persistent before, during, and after 2008 financial crisis.

담합관련 손해배상 소송의 경제분석에서 고려해야 할 이론 및 실증적 쟁점: 수송용 연료시장에의 적용

본 연구에서는 담합의 경제분석에서 고려해야 할 쟁점들을 논의한 후, 이러한 쟁점들을 반영한 방법론을 특정 수송용 연료시장의 분석에 적용하였다. 가상 경쟁가격과 과잉징수를 산정하는 5가지 방법 중 표준시장비교방법에 기반한 회귀분석방법이 최선이다. 수송용 연료시장에서와 같이 국제가격과 환율이 국내가격에 영향을 미치는 제품의 실거래가격을 분석하는 경우 논리에 부합하면서 유연한 함수형태는 로그-로그 함수형태이다. 경제분석의 대상이 되는 자료가 시계열자료인 경우에 ARDL 모형을 시장별 회귀분석모형의 근간으로 채택하는 것이 필요하며, 표준시장비교방법에 기반한 회귀분석방법에서는 구성 회귀식 간에 모수제약이 포함된 ARDL 회귀식 체계를 구축하고 system FGLS로 추정하여야 한다. Friedman 동질성 검정을 통하여 표준시장 여부를 판별할 수 있다. 통계적 유의성은 불확실성 하에서 입증하고자 하는 명제를 확립하는데 요구되는 최소의 요건이다. 담합관련 소송의 경제분석에서는 민감도 분석은 그다지 유용성이 없으며, 최적모형 선별과정이 더 중요한 절차이다. 위 방법론을 특정 수송용 연료시장의 분석에 적용한 결과, 해당 시장에서는 담합에 기인하는 손해액이 없다는 귀무가설을 기각할 수 없었다. We present key issues to consider in estimating damages from price-fixing cases and then apply the procedure addressing those issues to a transportation fuel market. Among the five methods of overcharge calculation, the regression analysis incorporating the yardstick method is the best. If the price equation relates the domestic price to the foreign price and the exchange rate as in the transportation fuel market, the functional form satisfying both logical consistency and modeling flexibility is the log-log functional form. If the data under analysis is of time series in nature, then the ARDL model should be the base model for each market and the regression analysis incorporating the yardstick method combines these ARDL equations to account for inter-market correlation and arrange constant terms and collusion-period dummies across component equations appropriately so as to identify the overcharge parameter. We propose a two-step test for the benchmarked market: (a) conduct market-by-market Spearman or Kendall test for randomness of the individual market price series first and (b) then conduct across-market Friedman test for homogeneity of the market price series. Statistical significance is the minimal requirement to establish the alleged proposition in the world of uncertainty. Between the sensitivity analysis and the model selection process for the best fitting model, the latter is far more important in the economic analysis of damage in price-fixing litigation. We applied our framework to a transportation fuel market and could not reject the null hypothesis of no overcharge.

Price Formation of Coffee Beans: New Evidence from the Japanese Market

This study tested for Granger causality among international market prices of coffee beans, import prices, retail prices, and other luxury beverage retail prices in Japan. Granger causality was found from the international market price series to the import price series. This indicates that Japan procures coffee beans at a price consistent with the international market price. Granger causality between import prices and retail prices was not observed. This reveals that the import price is not easily passed through to the retail price. In the retail market, Granger causality was detected from instant coffee prices to both coffee bean prices and green tea prices. Green tea prices were observed to Granger-cause black tea prices. This demonstrates that instant coffee is the only substitute good for coffee beans and the price maker in the luxury beverage market.

OPEC “Fair Price” Pronouncements and the Market Price of Crude Oil

OPEC producers, individually or collectively, often make statements regarding the “fair price” of crude oil. In some cases, the officials commenting are merely affirming the market price prevailing at the time. In many cases, however, we document that they explicitly disagree with contemporaneous oil futures prices. A natural question is whether these “fair price” pronouncements contain information not already reflected in the market price of crude oil. To find the answer, we collect “fair price” statements made from 2000 through 2010 by officials from OPEC or OPEC member countries. Visually, the “fairprice” series looks like a sampling discretely drawn (with a lag) from the daily futures market price series. Formally, we use two primary methodologies to establish that “fair price” pronouncements have little influence on the market price of crude oil and provide little or no new news to oil futures market participants.

Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping

Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due to their nonlinear modeling capabilities. However, it is well known that in general, traditional training methods for ANNs such as back-propagation (BP) approach are normally slow and it could be trapped into local optima. In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks. The new approach also has improved price intervals forecast accuracy by incorporating bootstrapping method for uncertainty estimations. Case studies based on chaos time series and Australian National Electricity Market price series show that the proposed method can effectively capture the nonlinearity from the highly volatile price data series with much less computation time compared with other methods. The results show the great potential of this proposed approach for online accurate price forecasting for the spot market prices analysis.

Nonlinearity and nonstationarity in international art market prices: evidence from Markov-switching ADF unit root tests

This study investigates the presence (or lack thereof) of nonlinear dynamics and nonstationarity in international art market prices using quarterly data for the period 1990–2011. We first test whether art market price indices follow stochastic trends or whether they are stationary by means of linear unit root tests. Next, we estimate the Markov regime-switching ADF model and test whether the linear or the nonlinear regime-switching model provides a better characterization of the global art market price series. We find that all art market price indices (except for Drawings) exhibit nonlinearity. To our knowledge, our study is the first one in the literature to suggest that a nonlinear (Markov regime-switching) model provides a better characterization of the behavior of price dynamics in international art markets. In particular, our findings indicate that the market for the overall global art market, paintings, old masters, sculptures, photographs, prints, and contemporary art might indeed be stationary while exhibiting nonlinear regime-switching properties. On the other hand, the market for drawings and the Nineteenth century art are found to be nonstationary. Overall, despite the common ground of a regime-switching framework, we still find that the sub-segments of the art market have their own inner regime switching dynamics and hence they can evolve differently overtime.

The multiple-mean-reversion jump-diffusion model for Nordic electricity spot prices

Electricity spot market prices are notoriously difficult to model, let alone predict, because of their extreme volatility. Such volatility is reflected in so-called price spikes that may increase the spot price by an order of magnitude in a matter of hours. Spot market price series are also subject to many other types of phenomena, such as periodicities at different scales, and to mean reversion. We introduce a model for electricity spot market prices that includes both spikes and mean reversion. The model is based on a jump-diffusion process that is superimposed on a mean-reverting Ornstein-Uhlenbeck model. Mean reversion takes place at several different time and price scales, so as to reproduce the observed behavior of spot market prices correctly. The parameters of the model are calibrated with the Nord Pool spot market hourly price series using a maximum likelihood approach. The simulated price series obtained in this way very closely follows the statistical characteristics of the real price series.

Are the Tokyo Grain Exchange non‐genetically modified organism (non‐GMO) and conventional soybean futures markets integrated?

PurposeThe purpose of this paper is to test the market linkage and the strength of the linkage between the identity‐preserved (IP) non‐genetically modified organism (non‐GMO) and non‐IP conventional soybean futures markets at the Tokyo Grain Exchange (TGE) when effects from structural breaks are considered.Design/methodology/approachThe Johansen method is used for testing the market linkage and the law of one price (LOP) test is performed to find out the strength of the linkage. The Bai‐Perron test is conducted on the price series to identify the structural breaks. After the breaks are statistically determined by the Bai‐Perron test the Johansen tests are conducted for every break period.FindingsMarket linkage exists between the non‐GMO and conventional soybean futures markets but the LOP test suggests that the two markets are not fully linked and the relative price of the two soybeans is not constant. The breaks identified in the study affected the market linkage; the price relationship is lost during the break periods.Practical implicationsThe results of this study help explain the value of other new markets where the product traded is a close substitute for an existing market.Originality/valueThis is the first study to apply the market integration theory to an IP market. The TGE non‐GMO soybean contract is the first market price series with sufficient information to appropriately model a price integration linkage for an IP market. The result is valuable in the future when more markets for IP commodities are developed.

Market Efficiency Test in the VIX Futures Market

This paper tests the random walk hypothesis and weak form market efficiency in the VIX futures market using a variety of tests. A unit root in the aggregated market price series suggests that the VIX futures market is efficient. For the individual VIX futures price series, 51 of 54 futures contracts meet the sufficient condition for an efficient market: the prices are found to follow a random walk either because there is a unit root or because the increments are not correlated. Overall, the market for VIX futures has been efficient since the first day of trading.