Regulatory Issues and Countermeasures in International Financial Markets

With the deepening of globalization,the development speed of capital markets is constantly accelerating,presenting a trend of globalization.At the same time,the emergence of multiple forms of trading platforms and diversified financial products further highlights the competitive relationship between security exchanges and other trading platforms.While promoting the transformation of security exchange forms in various countries,it also prompts governments to re-examine the financial regulatory system of securities markets.In this situation,it is very important to research the international financial market and financial regulatory system.This article explores the regulatory issues and countermeasures in the international financial market,intending to promote the stability and healthy development of the international financial market.

Copula-EVT Based Tail Dependence Structure of Financial Markets in China

Tail dependence structure model based on Copula theory and extreme value theory （EVT） is constructed to picture the tail correlation of financial time series more exact. The empirical research results show that the Gumbel Copula can fit the upper and lower tail dependence structures of Shanghai A share index and Shenzhen A share index, and correlation of upper tails of both indices is stronger than that of lower-tails.

Will high-frequency trading practices transform the financial markets in the Asia Pacific Region?

High-frequency trading(HFT)practices in the global financial markets involve the use of information and communication technologies(ICT),especially the capabilities of high-speed networks,rapid computation,and algorithmic detection of changing information and prices that create opportunities for computers to effect low-latency trades that can be accomplished in milliseconds.HFT practices exist because a variety of new technologies have made them possible,and because financial market infrastructure capabilities have also been changing so rapidly.The U.S.markets,such as the National Association for Securities Dealers Automated Quote(NASDAQ)market and the New York Stock Exchange(NYSE),have maintained relevance and centrality in financial intermediation in financial markets settings that have changed so much in the past 20 years that they are hardly recognizable.In this article,we explore the technological,institutional and market developments in leading financial markets around the world that have embraced HFT trading.From these examples,we will distill a number of common characteristics that seem to be in operation,and then assess the extent to which HFT practices have begun to be observed in Asian regional financial markets,and what will be their likely impacts.We also discuss a number of theoretical and empirical research directions of interest.

Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market

Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.

Pricing,management and decision‑making of financial markets with artificial intelligence:introduction to the issue

While the advancement of Internet Technology generates massive amounts of data that can facilitate decision makings of financial markets,it also arouses new challenges to financial activities,such as the acquisition,processing and analysis of multiple information resources.In addition,the external environment of the financial market is full of uncertainties,such as the occurrence of COVID-19 pandemic and government intervention,which makes the asset pricing and management much more challenging.In recent years,artificial intelligence technology has developed rapidly and has been widely used in various fields,including the financial markets.Due to its capability of mining patterns from big data,artificial intelligence is regarded as an efficient tool to address the abovementioned challenges.

Herding behavior in Ramadan and financial crises: the case of the Pakistani stock market

This study examines herding behavior in the Pakistani Stock Market under different market conditions,focusing on the Ramadan effect and Crisis period by using data from 2004 to 2014.Two regression models of Christie and Huang(Financ Analysts J 51:31-37,1995)and Chang et al.,(J Bank Finance 24:1651-1679,2000)are used for herding estimations.Results based on daily stock data reveal that there is an absence of herding behavior during rising(up)and falling(down)market as well as during high and low volatility in market.While herding behavior is detected during low trading volume days.Yearly analysis shows that herding existed during 2005,2006 and 2007,while it is not evident during rest of the period.However,herding behavior is not detected during Ramadan.Furthermore,during financial crisis of 2007-08,Pakistani Stock Market exhibits herding behavior due to higher uncertainty and information asymmetry.

Volatility spillover effect between financial markets:evidence since the reform of the RMB exchange rate mechanism

The volatility spillover effect between the foreign exchange and stock markets has been a major issue in economic and financial studies.In this paper,GC-MSV model was used to study the spillover effect between the foreign exchange market and the stock market after the reform of the RMB exchange rate mechanism.The empirical results show that there is a negative correlation of dynamic price spillovers between the foreign exchange and stock markets.There are asymmetric volatility spillover effects between these two markets for both RMB stages—continued RMB appreciation or constant RMB shock(a significant reduction in appreciation).However,this has been reduced over time.In conclusion,The RMB exchange rate is a key variable that can affect the internal and external equilibrium of the national economy in an open economic environment,and the stock market is capable of quickly reflecting subtle changes in the real economy.In order to keep the stability of the financial markets and the healthy and rapid development of national economy,some suggestions were proposed.

Introduction to the special issue on Impact of COVID‑19 and cryptocurrencies on the global financial market

In the last year,the global outbreak of COVID-19 and the sharply rising price of Bitcoin greatly influence the financial market.Such influences include but are not limited to the trading strategies between different cryptocurrencies,portfolio diversification,foreign exchange markets,and macroeconomic policy.In this 26th issue of Financial Innovation(FIN),Volume 7,No.2(2021),22 researchers from 7 countries have used both traditional statistical methods and the newly developed machine learning techniques to analyze how COVID-19 affects the global financial market and how cryptocurrencies affect portfolio diversification.The research results could be beneficial to governments in making macroeconomic policies and financial investors for making better investment decisions.

COVID‑19 pandemic and the crude oil market risk:hedging options with non‑energy financial innovations

This study examines the hedging effectiveness of financial innovations against crude oil investment risks,both before and during the COVID-19 pandemic.We focus on the non-energy exchange traded funds(ETFs)as proxies for financial innovations given the potential positive correlation between energy variants and crude oil proxies.We employ a multivariate volatility modeling framework that accounts for important statistical features of the non-energy ETFs and oil price series in the computation of optimal weights and optimal hedging ratios.Results show evidence of hedging effectiveness for the financial innovations against oil market risks,with higher hedging performance observed during the pandemic.Overall,we show that sectoral financial innovations provide resilient investment options.Therefore,we propose that including the ETFs in an investment portfolio containing oil could improve risk-adjusted returns,especially in similar financial crisis as witnessed during the pandemic.In essence,our results are useful for investors in the global oil market seeking to maximize risk-adjusted returns when making investment decisions.Moreover,by exploring the role of structural breaks in the multivariate volatility framework,our attempts at establishing robustness for the results reveal that ignoring the same may lead to wrong conclusions about the hedging effectiveness.

A Multifractal Detrended Fluctuation Analysis of the Ising Financial Markets Model with Small World Topology

We present a multifractal detrended fluctuation analysis （MFDFA） of the time series of return generated by our recently-proposed Ising financial market model with underlying small world topology. The result of the MFDFA shows that there exists obvious multifractal scaling behavior in produced time series. We compare the MFDFA results for original time series with those for shuffled series, and find that its multifractal nature is due to two factors： broadness of probability density function of the series and different correlations in small- and large-scale fluctuations. This may provide new insight to the problem of the origin of multifractality in financial time series.

Asymmetric and symmetric meta-correlations in financial markets

In financial markets, the relation between fluctuations of stock prices and trading behaviors is complex. It is intriguing to quantify this kind of meta-correlation between market fluctuations and the synchronous behaviors. We refine the theoretical index leverage model proposed by Reigneron et al., to exactly quantify the meta-correlation under various levels of price fluctuations [Reigneron P A, Allez R and Bouchaud J P 2011 Physica A 390 3026]. The characteristics of meta-correlations in times of market losses, are found to be significantly different in Chinese and American financial markets. In addition,unlike the asymmetric results at the daily scale, the correlation behaviors are found to be symmetric at the high-frequency scale.

Financial Derivatives Market and Storage Management

It’s known to all that under ideal condition the s to rage cost is kept in lower level when storage management be arranged by Economic Order Quantity(EOQ).Does this mean that any companies should set up their own storing system in proportion to the scale of the commodities’ producing or sell ing Furthermore, even if they manage storage in EOQ, because of different oper ation scale, geographical condition or ability borrowing money from financial ma rket, different companies pay unequal cost in storing the same commodity.In thi s paper, except for supplying commodities from our own storage system, the autho rs have analyzed other two supplying ways without whole storage system, they are forward contracts and futures contracts.The authors have discussed variable su pply cost for above different supply measures.According to the cost of each sup ply way, the managers can choose the most economical way in supplying the commod ity and predict the price of futures from storage management arranged by EOQ.Th e summary content is as follow: 1. The comparing of supply cost between forward contracts and storing system a rranged by EOQ. (1) The supply cost from forward contracts (2) The supply cost from storage system arranged by Economic Order Quantity (3) The application example for comparing cost in different supply way 2.The comparing of supply cost between futures going physical and storing syst em arranged by Economic Order Quantity. (1) The supply cost from futures going physical (2) The correlation between futures contracts and storage management arranged b y EOQ (3) The application example for comparing cost in different supply way 3.How does storing system of scale economic affect the price of forward and fu tures contracts (1) How does the price of forward and futures contracts fluctuate (2) How do we calculate the price of a commodity at future point from the cost of scale economic storing (3) How do we operate efficiently in derivatives market by using the cost of sc ale economic storing (4) The application example for analyzing the price of futures 4.The correlation among storage managementforward contracts and futures mark et.

Economic Effect of China's Rural Financial Market Growth during 1952-2013

Through study,it is found that since 1952,there has been a long-run equilibrium relationship between China's rural financial market growth and rural economic growth,the government-led rural financial market growth has effectively supported rural economic growth,and increasing the farmers' financing ratio has always helped to boost long-term growth of the rural economy.However,dominated by market mechanism from 1978,there is only one-way support relationship:rural economic growth brings about quantitative growth of rural financial market.

Editorial to special issue“Hidden market linkages between Bitcoin,cryptocurrencies and financial markets:Evidence from high‑frequency data and higher‑order moments”in financial innovation

The cryptomarket has evolved into a complex system of different types of cryptoassets,each playing an important role within the system.With specific features,opportunities,and risks.Studying their apparent and hidden linkages and general connectedness not only inside the system but also the linkages to the outer markets,being it either the traditional financial markets or the macroeconomic and monetary indicators and variables,plays a crucial role in understanding the market,managing risks,and aiming for profitable opportunities.The cryptomarkets are far from being simply Bitcoin or even just the most popular and capitalised cryptocurrencies and tokens which might have been the case just a few years back.

Crossover Phenomena in Detrended Fluctuation Analysis Used in Financial Markets

A systematic analysis of Shanghai and Japan stock indices for the period of Jan. 1984 to Dec. 2005 is performed. After stationarity is verified by ADF （Augmented Dickey-Fuller） test, the power spectrum of the data exhibits a power law decay as a whole characterized by 1/f^β processes with possible long range correlations. Subsequently, by using the method of detrended fluctuation analysis （DFA） of the general volatility in the stock markets, we find that the long-range correlations are occurred among the return series and the crossover phenomena exhibit in the results obviously.Further, Shanghai stock market shows long-range correlations in short time scale and shows short-range correlations in long time scale. Whereas, for Japan stock market, the data behaves oppositely absolutely. Last, we compare the varying of scale exponent in large volatility between two stock markets. All results obtained may indicate the possibility of characteristic of multifractal scaling behavior of the financial markets.

THE METRIC GENERALIZED INVERSE AND ITS SINGLE-VALUE SELECTION IN THE PRICING OF CONTINGENT CLAIMS IN AN INCOMPLETE FINANCIAL MARKET

This article continues to study the research suggestions in depth made by M.Z.Nashed and G.F.Votruba in the journal"Bull.Amer.Math.Soc."in 1974.Concerned with the pricing of non-reachable"contingent claims"in an incomplete financial market,when constructing a specific bounded linear operator A:l_(1)^(n)→l_(2) from a non-reflexive Banach space l_(1)^(n) to a Hilbert space l_(2),the problem of non-reachable"contingent claims"pricing is reduced to researching the(single-valued)selection of the(set-valued)metric generalized inverse A■ of the operator A.In this paper,by using the Banach space structure theory and the generalized inverse method of operators,we obtain a bounded linear single-valued selection A^(σ)=A+of A■.

Introduction to the special issue on analytical and decision-making technique innovation in financial market

The selection of appropriate analytical methods and techniques for decision-making is crucial in the financial markets,to correctly assess investments.Effective managers use a variety of decision-making techniques as they seek to evaluate alternatives and make sound decisions.After the COVID-19 pandemic,the global financial market has experienced sharp fluctuations.Recent technique innovations in the financial markets include both the development of new models as well as the incorporation of big data.In this special issue,we provide an overview of analytical and decision-making technique innovations arising from research and development.We also introduce the papers in this special issue.We show that global adoption of blockchain and big-data technologies would be the most successful technique in finance and other business sectors,and will lead to analytical and decision-making innovations as well as many research opportunities.

Improvement in Hurst exponent estimation and its application to financial markets

This research aims to improve the efficiency in estimating the Hurst exponent in financial time series.A new procedure is developed based on equality in distribution and is applicable to the estimation methods of the Hurst exponent.We show how to use this new procedure with three of the most popular algorithms(generalized Hurst exponet,total triangles area,and fractal dimension)in the literature.Findings show that this new approach improves the accuracy of the original methods,mainly for longer series.The second contribution of this study is that we show how to use this methodology to test whether the series is self-similar,constructing a confidence interval for the Hurst exponent for which the series satisfies this property.Finally,we present an empirical application of this new procedure to stocks of the S&P500 index.Similar to previous contributions,we consider this to be relevant to financial literature,as it helps to avoid inappropriate interpretations of market efficiency that can lead to erroneous decisions not only by market participants but also by policymakers.

The aggregate and sectoral time‑varying market efficiency during crisis periods in Turkey:a comparative analysis with COVID‑19 outbreak and the global financial crisis

This study aims to examine the time-varying efficiency of the Turkish stock market’s major stock index and eight sectoral indices,including the industrial,financial,service,information technology,basic metals,tourism,real estate investment,and chemical petrol plastic,during the COVID-19 outbreak and the global financial crisis(GFC)within the framework of the adaptive market hypothesis.This study employs multifractal detrended fluctuation analysis to illustrate these sectors’multifractality and short-and long-term dependence.The results show that all sectoral returns have greater persis-tence during the COVID-19 outbreak than during the GFC.Second,the real estate and information technology industries had the lowest levels of efficiency during the GFC and the COVID-19 outbreak.Lastly,the fat-tailed distribution has a greater effect on multifractality in these industries.Our results validate the conclusions of the adaptive market hypothesis,according to which arbitrage opportunities vary over time,and contribute to policy formulation for future outbreak-induced economic crises.

An exchange rate determination model for central banks＇interventions in financial markets

We establish an exchange rate determination model for central banks' interventions in financial markets. The model shows that central banks can adjust exchange rate by several policy instruments and that different instruments may have different effects on exchange rate determination. It specifies potential policy instruments for central banks as well as their policy effects. Based on these effects, feasible matches of policy instruments in contingent intervention are put forth.