Search results

The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices.

The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency.

The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market.

The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity.

The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.

The purpose of this paper is to examine whether the superiority of the implied volatility from a stochastic volatility model over the implied volatility from the Black and Scholes model on the forecasting performance of future realized volatility still holds when intraday data are analyzed.

Two implied volatilities and a realized volatility on KOSPI200 index options are estimated every hour. The grander causality tests between an implied volatility and a realized volatility is carried out for checking the forecasting performance. A dummy variable is added to the grander causality test to examine the change of the forecasting performance when a specific environment is chosen. A trading simulation is conducted to check the economic value of the forecasting performance.

Contrary to the previous studies, the implied volatility from a stochastic volatility model is not superior to that from the Black and Scholes model for the intraday volatility forecasting even if both implied volatilities are informative on one hour ahead future volatility. The forecasting performances of both implied volatilities are improved under high volatile market or low return market.

The trading strategy using the forecasting power of an implied volatility earns positively, in particular, more positively under high volatile market or low return market. However, it looks risky to follow the trading strategy because the performance is too volatile. Between two implied volatilities, it is hardly to say that one implied volatility beats another in terms of the economic value.

This is the first study which shows the forecasting performances of implied volatilities on the intraday future volatility.

While most practitioners are familiar with traditional customer satisfaction surveys, research findings suggest that best practice companies use multiple tools to bring the voice of the customer inside the organization. The purpose of this study is to examine how best practice companies use various tools to listen to customers. The primary contribution of this article is in discussing a variety of different customer listening tools used by practitioners, along with introducing new customer listening tools to the literature. Furthermore, this article puts forth a framework that captures essential characteristics of each tool, depicting when their use is most appropriate. Finally, this article depicts how customer listening tools are linked together and synthesized into a customer performance model.

Practicing flexible revenue management (RM) at hotels during Covid-19 is essential. The well-performed hotels ponder how to transform the target from revenue to net profits. This paper aims, first, to develop a value stream mapping (VSM) model for a productive RM based on six key drivers: organizational culture, demand forecasting, dynamic distribution channels, competition breakdown, dynamic and customized pricing and daily reviewing, and, second, to examine the nexus between RM and hotel’s efficiency during Covid-19 using the wavelet analysis (WA) to visualize this relationship’s time and frequency-based lead–lag dynamics.

Using time-series data, a multiple case study of 31 luxury hotels in Egypt was applied based on semi-structured interviews and self-administered questionnaires.

The first phase results showed that consensus toward the RM framework was achieved, regardless of current challenges, indicating that RM managers and scholars could use it. In Phase 2, the WA confirmed a positive correlation and significant influence between Covid-19 and RM practices at most business cycle frequencies. Furthermore, overall high causal relationships between RM practices and hotel efficiency were discovered in the short and medium terms and through different occurrence cycles. Though, the dynamic pricing in the long term was apart from this relationship. The causal effects between Covid-19 and hotel efficiency are not observable in the long-run spectra, indicating that resilience efforts with Covid-19 perhaps mitigated the impact.

Hotel managers could use the RM model developed from this study during the downturn to improve efficiency. The outcome may lead to the recovery of the hotel market and the whole economy. WA maps display possible directions for hotel managers to be more efficient based on the time and frequency domains.

This study shows opportunities for RM implementation during Covid-19 based on the VSM and the WA approaches in hotels.

This paper aims to analyze the downside risk for the stock indices of BRICS countries. The study also aimed to study the interrelationship, directional influence and interdependence among the stock exchanges of BRICS economies to provide insights for policymakers, fund managers, investors and other stakeholders.

The authors used Value at Risk (VaR) as an indicator of downside risk and time series econometrics for measuring the long run relationship, directional influence and interdependence.

The calculated VaR estimates, long-run linkages and strong interdependence among these indices especially with the returns of Brazil exerting a notable impact on the returns of other BRICS nations. These results emphasize the significance of taking into account cross-country spillover effects and domestic market dynamics in the context of portfolio management and risk assessment strategies. Further, from the extended results of variance decomposition analysis, the authors find that Brazil’s, China’s and South African stock market returns have a significantly lagged impact on their own stock market, while Russia’s and India stock market returns do not have a significantly lagged impact on their own stock markets.

To the best of the authors’ knowledge, this is the first study comprehensively analyzing the BRICS indices downside risk through the historical simulation method of VaR estimation, which is an unexplored area of risk management.

This study aims to investigate the cointegration and causality relationships between Hong Kong’s residential property price and stock price, using quarterly data, from the 1st quarter of 1980 to the 3rd quarter of 2015.

In contrast to other studies, the cointegration test used is the autoregressive distributed lag (ARDL) cointegration (bounds testing) approach of Pesaran et al. (2001) that based on the estimation of an unrestricted error correction model and the causality test is based on non-causality test of Granger et al. (2000). Moreover, this research employs recursive least square procedures and Chow (1960) breakpoint test to detect unknown structural break and variation of relationships between residential property and stock price over the whole sample period.

The results of ARDL cointegration tests running from stock to residential property markets provide strong evidence to support the hypothesis that the stock and residential properties are cointegrated. The results of Granger et al. (2000) non-causality test support the view of wealth effect that stock price has an important causal effect on residential property price in Hong Kong but not vice versa. In addition, the results of recursive ordinary least squares coefficients estimates and Chow (1960) test (breakpoint test) for structural instability confirm the variation of the relationships between stock and residential property markets over the sample period.

The empirical results from cointegration and causality tests suggest that the residential asset returns are better predicted by including the lagged difference values of stock price.

This is the pioneering study to examine the cointegration and causality study of stock and residential property price in Hong Kong by employing Pesaran ARDL cointegration approach and Granger non-causality approach. Investors are able to perform an effective evaluation to assist in allocating investment funds, and the government bodies can implement supplement housing policy in response to the public needs.

The purpose of this paper is to examine the linkage between Islamic financing principles and economic growth (EG) by taking into consideration two Islamic Financing Principles: Risk Sharing and non-risk sharing separately.

The data for this study are obtained from the annual reports of all Islamic banks from Bangladesh using Bank scope database and annual report for the period 1984-2014. The research uses an Autoregressive Distributive Lags (ARDL) approach. For robustness, this study also employs a continuous wavelet transform approach.

The empirical findings reveal that the risk sharing instruments are positively related to the EG of the country. On the other hand, non-risk sharing instruments are negatively related to the EG of the country.

The dominant use of non-risk sharing-based financing has undermined the greater possibility of Islamic banking to contribute more to the EG of the country. Banks and other financial institutions need to pay greater attention to systemic risk created by risk transfer and apply risk sharing methods of financing more vigorously to achieve greater equity, efficient allocation of resources, stability and growth of the financial system and welfare of the society as a whole.

This study has advanced the knowledge by examining the issue of Islamic financing principles and EG. This is probably one of the first attempts to find the linkage between Islamic financing principles and EG by taking into consideration two portfolios: risk sharing and non-risk sharing separately and provide significant insights for policy makers, market players and academicians.

The purpose of this paper is to study the lead-lag relationship between the futures and spot foreign exchange (FX) market in India to understand the price discovery mechanism and the relationship between these two markets.

The estimation of lead-lag relationship is realized in three steps. First unit root and stationarity tests (Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski-Phillips-Schmidt-Shin) are applied to check the stationarity of the data. Second, cointegration tests (Engle and Granger’s residual based approach and Johansen’s cointegration test) are applied to determine long run relationship between the markets. Third, error correction estimation is carried out by applying Vector Error Correction Model (VECM) to determine the leading market.

The study finds that there is a long run relationship between the futures and spot market where the futures market has emerged as the leading market for the four currencies studied in the paper.

Majorly, the studies on Indian FX market limit themselves to identifying the efficiency of the market and the studies which talk about the lead-lag relationship focus on the Indian stock market. This paper enhances the existing literature on Indian FX market by exploring the less explored subject of the lead-lag relationship between futures and spot FX market in India.

The purpose of this paper is to examine the lead‐lag relationships between the National Stock Exchange (NSE) Nifty stock market index (in India) and its related futures and options contracts, and also the interrelation between the derivatives markets.

The paper uses serial correlation of return series and autoregressive moving average (ARMA) model for studying the lead‐lag relationship between hourly returns on the NSE Nifty index and its derivatives contracts like futures, call and put options. Further, the lead‐lag relation between hourly returns of the derivatives contracts among themselves is also studied using ARMA models.

The ARMA analysis shows that the NSE Nifty derivatives markets tend to lead the underlying stock index. The futures market clearly leads the cash market although this lead appears to be eroding slightly over time. Although the options market leads the cash overall, there is some feedback between the two with the underlying index leading at times. Further, it is found that the index call options lead the index futures more strongly than futures lead calls, while the futures lead puts more strongly than the reverse.

The results imply that the derivative contracts on NSE Nifty lead the underlying cash market. Thus, the derivative markets are indicative of futures price movements and this will certainly be helpful to potential investors to design their risk‐return portfolio while investing in stocks and derivatives contracts.

This paper is an original piece of work towards exploring the lead‐lag relation between NSE Nifty and the derivative contracts. The issue of price discovery on futures and spot markets and the lead‐lag relationship are topics of interest to traders, financial economists, and analysts.

Market links (and price discovery) between financial assets and lead–lag relationships are topics of interest for financial economists, financial managers and analysts. The lead–lag relationship analysis should consider both short and long-term investors. From a portfolio diversification perspective, the first type of investor is generally more interested in determining the co-movement of financial assets at higher frequencies, which are short-run fluctuations, while the latter concentrates on the relationship at lower frequencies, or long-run fluctuations. The paper aims to discuss these issues.

For this study, a technique was employed known as the wavelet approach, which has recently been imported to finance from engineering sciences to study the co-movement dynamics between global sukuk and bond markets. Data cover the period from January 2010 to December 2015.

The results indicate that: there is no unidirectional causality from developed market bond indices to Malaysia and Dow Jones indices, which is promising for fixed-income investors of a developed market; and in relation to emerging markets, the Malaysian sukuk market has a bidirectional causality with Indonesia, Malaysia, India and South Korea bond indices but not China bond indices, while in terms of the Dow Jones sukuk index, there is no unidirectional causality between the listed emerging markets and the sukuk index except Indonesia’s market during the sample period.

This analysis provides evidence regarding the timely and appropriate measure of correlation changes and the behaviour of sukuk and bond indices globally, which is beneficial to the management of sukuk and bond portfolios.

The evidence hitherto unexplored, which was produced by the application of a wavelet cross-correlation amongst the selected sukuk and bond indices, provides robust and useful information for international financial analysts as well as long and short-term investors.