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The purpose of this paper is to examine information and volatility linkages among real estate, equity, bond and money markets in Australia.

A novel rational expectations framework of financial contagion (Kodres and Pritsker, 2002), along with a combination of robust statistical methods including simple and dynamic correlations and generalized impulse response (Fereidouni et al., 2014) have been employed using data covering three dynamic pre-pandemic economic cycles, namely, global financial crisis (GFC) period, pre-pandemic housing boom and pre-pandemic housing downturn from 2008 (February) to 2019 (December).

Results reveal information linkages across real estate, equity, bond and money markets through correlations in return and volatilities of these series. Finding indicates that the three financial markets (equity, bond and money markets) are interdependent and integrated through information and volatility linkages during the GFC period and pre-pandemic housing downturn period. Financial markets have stronger associations with real estate market during pre-pandemic housing boom. The findings contribute to the general notion that the performances of three financial markets are closely related to the “boom” phase of the real estate cycle.

This research provides an extension of existing literature regarding the information and volatility contagion of the expanded set of core investment markets in Australia. The findings could assist household buyers and investors in designing strategic investment portfolios/hedging strategies and minimizing asset specific risks through diversification over short-term and long-term. In addition, results could support the maintenance, growth and development of a combination of competitive balanced investment markets including real estate, equity, bond and money markets in post-pandemic economy.

This study aimed to investigate return and volatility spillover between the Borsa Istanbul (BIST) and the Moscow Stock Exchange (RTS).

This study used generalized autoregressive conditionally heteroscedasticity (GARCH) model for volatility and the Aggregate Shock (AS) model for return and volatility spillover. The data are divided into six sub-periods. Period events take place between Turkey and Russia.

BIST investors considered the return and volatility of the RTS, it is observed that Moscow Stock Exchange investors considered only the return of BIST at the full sample. It is only a return spillover from BIST to RTS and neither the return nor the volatility of the RTS is spillover to BIST in the pre-crisis period. No evidence of return and volatility spillover between the BIST and the RTS in the post-crisis period. The returns and volatility spillovers between Russia and Turkey are mutual feedback in the jet crisis period.

Economic developments between Turkey and Russia is growing rapidly in recent years. The return and volatility analysis between the stock exchanges of these two countries is important for investment decisions.

There are many studies in the literature about emerging markets. There are also Turkish and Russian stock exchanges in these studies. However, this study only examined return and volatility spillover analysis between the Turkish and Russian stock exchanges and prevents the results from being overlooked among other countries.

As the number of firms participating in export activities increases, information regarding the international environment in turn becomes critical in effectively managing corporate ventures. Given the level of economic volatility within certain export markets and the increasing levels of competition worldwide, effective distribution management venturing and strategy will also become more complex. Decision support is crucial in effectively monitoring and modifying marketing channels in an overseas environment. The purpose of this paper is to identify factors that drive market information use in export distribution decisions, these factors being both internal and external to the organization. Concurrently, the linkage between market information use and export performance is investigated, in order to better understand the role of this strategy in export operations. First, a discussion of strategic, organization specific, and environment specific factors and their association with market information use is discussed. Second, research hypotheses are developed through the support of the existing literature. Next, data collection and analysis procedures are discussed along with results of the hypothesis testing. Finally, a presentation of the findings along with theoretical and managerial implications is offered. The research reveals the strategic importance of market information usage in distribution control, market positioning, venture planning, and market volatility.

The aim of this paper is to examine the effect of information disclosure by unlisted bond issuers on the stock price informativeness of listed firms in the same industry.

This paper takes advantage of information disclosure during the bond issuance and examines the spillover effect of unlisted bond issuers' information disclosure on listed firms in the stock market. The sample is composed of A-share firms listed on the Shanghai and Shenzhen stock exchanges from 2007 to 2018. All the data are obtained from the China Stock Market and Accounting Research and WIND databases. The impact of bond market information disclosure on price informativeness of listed firms in the same industry is identified through multivariate regression analyses.

Empirical results show that price informativeness of listed firms has a significantly positive association with the information disclosure of same-industry unlisted bond issuers. Further analyses show that the above finding is more significant when information disclosure of bond issuers is a more important channel for acquiring industry information (i.e. when industry is more concentrated, when economic uncertainty is high, and when industry information is less transparent) and understanding the industry competitive landscape (i.e. when bond issuers are relatively large, when bond issuers and listed firms have more direct product competition, when bond issuance firms are large-scale state-owned business groups), and when there are more cross-market information intermediaries (i.e. more cross-market institutional investors and more sell-side analysts). This paper indicates that information disclosure of bond issuers has a positive spillover effect on the stock market.

The novelty of the research is that the authors examine industry information spillover from unlisted firms to listed firms leveraging on unlisted firms' information disclosure in bond markets.

The purpose of this paper is to empirically analyze the dynamic relationship between stock market and bond market based on the effect of different information shocks.

This paper decomposes the information of stock market and bond market into public information and private information. The characteristics of response of stock market and bond market to the information shocks are examined by SVAR model and modified BEKK model.

The study shows that the information shocks in financial market yield not only the effect on linear asset return but also the effect on nonlinear asset volatility. The public information mainly produces a short effect of return while the private information mainly produces a permanent effect on volume. The interactive relation between stock market and bond market is mainly reliant on the effect of the information shock volatility to market return volatility.

The paper empirically analyzes the influence characteristics of different information shocks, which has some reference value not only for deeply understanding the market microstructure but also for improving the construction of various capital markets.

This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading?

Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted.

Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information.

To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.

This study explores the price linkage between the Chinese commodity futures market and other dominant futures markets, and examines the forces behind the price linkages. The contribution by the trading hour innovations in the United States (or United Kingdom) market to the overnight price changes in the Chinese market is larger in scale than the contribution by the daytime information from the Chinese market to the overnight returns of the corresponding US (or UK) market. Several futures have significant interactions of the domestic and foreign factors in the price linkages while the Chinese domestic factors explain better the global market price linkage in some futures (aluminum, gold, and corn), demonstrating the leading role of the Chinese futures markets in these world markets.

This paper aims to investigate asymmetric pricing behaviour and impact of coronavirus (Covid-19) pandemic shocks on house price index (HPI) of Turkey and Kazakhstan.

Monthly HPIs and consumer price index (CPI) data ranges from 2010M1 to 2020M5 are used. This study uses a nonlinear autoregressive distributed lag model for empirical analysis.

The findings of this study reveal that the Covid-19 pandemic exerted both long-run and short-run asymmetric relationship on HPI of Turkey while in Kazakhstan, the long-run impact of Covid-19 pandemic shock is symmetrical long-run positive effect is similar in both HPI markets.

The main limitations of this study are the study scope and data set due to data constraint. Several other macroeconomic variables may affect housing prices; however, variables used in this study satisfy the focus of this study in the presence of data constraint. HPI and CPI variables were made available on monthly basis for a considerably longer period which guaranteed the ranges of data set used in this study.

Despite the limitation, this study provides necessary information for authorities and prospective investors in HPI to make a sound investment decision.

This is the first study that rigorously and simultaneously examines the pricing behaviour of Turkey and Kazakhstan HPIs in relation to the Covid-19 pandemic shocks at the regional level. HPI of Kazakhstan is recognized in the global real estate transparency index but the study is rare. The study contributes to regional studies on housing price by bridging this gap in the real estate literature.

The Chinese capital markets are divided into two segments comprising of A-shares (traded by domestic investors) and B-shares (traded by foreign investors). Firms issuing A-shares are required to produce accounting reports under the Chinese Accounting Standards (CAS) and firms issuing B-shares are required to report under the International Accounting Standards (IAS). The purpose of this paper is to investigate the comparative value-relevance of accounting information in the Chinese capital markets, in particular whether the value-relevance associated IAS exceeds that of CAS.

This study undertakes a capital market research approach. Two statistical models are employed to test the value-relevance of competing accounting information on share prices: the Price Model and the Return Model. This study takes advantage of the parallel reporting frameworks governing the A-share and B-share markets buy using the same firms which issue both A-shares and B-shares.

The analysis supporting the study demonstrates that both CAS and IAS information is value relevant to investors in the Chinese capital markets but that IAS provide more useful information. Additionally it is observed that reconciliation variables (representing the discrepancy between IAS- and CAS-based accounting figures) are not significant in explaining market valuation or returns on stock.

This study provides evidence of value-relevance of accounting reports on the Chinese capital markets for the period of 1999-2005. The period under investigation captures the significant development in China's accounting regulations which took place in 1998 and 2001. The recent shift in accounting regulations in China from CAS to IAS is expected to improve the dissemination of financial information by publicly listed Chinese firms.

This study investigates the reporting requirements on the Chinese capital markets during a period in which accounting reporting requirements underwent a significant change as part of the internationalization of accounting standards. Both A- and B-share markets were investigated simultaneously in order to provide an objective analysis and avoid sampling selection bias present in other studies.

The recent shift in accounting regulations in China from CAS to IAS is expected to improve the dissemination of financial information by publicly listed Chinese firms.

This paper extends previous research on value-relevance of accounting reports in the Chinese capital markets by capturing the period in which the reporting requirements had experienced significant change. This paper also takes advantage of the dual reporting framework in order to mitigate potential sampling bias present in previous studies and employs a reconciliation variables not previously used.

The purpose of the study is to measure cross-country stock market correlation and volatility transmission during the global coronavirus disease 2019 (COVID-19) pandemic. The paper traces trajectory of Islamic equity investments in order to get insights on the behavior of the markets during the crisis.

The paper uses generalized method of moments (GMM), autoregressive distributed lag (ARDL) and multivariate GARCH (MGARCH) models for analysis of dynamic causality, stock market cointegration, correlation and volatility transmission between Islamic stock indices.

The result of normal correlation analysis on the share indices show the markets move together. The result of ARDL cointegration test shows the markets returns are cointegrated as a group. To further make sense of the data; the indices were grouped into four different categories, then cointegration tests were conducted. The results of the analysis show that the subgroups are cointegrated except the low COVID-19 subgroup. Based on MGARCH findings, the possibility of volatility transmission between markets during the crisis is high. The market returns indices show the usual herd mentality common during the period of crisis.

Unlike other works in this area, this paper attempt to trace the trajectory of Islamic equity investment in order to get relevant insights and arrives at appropriate ways of responding to the crisis.