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The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory.

This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry.

This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased.

This paper has research limitations in variable measurement and data selection.

This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration.

The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

The purpose of this paper is to model the volatility of treasury bill (T-bill) rates in five Sub-Saharan capital markets to investigate whether or not differences in capital mobility affect volatility.

Primary data was collected from weekly T-bill auctions in five Sub-Saharan countries and was analysed using a range of Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models in order to determine the volatility characteristics of each of these instruments. Differences in the institutional arrangements for each market are used to interpret the results of the econometric analysis.

Evidence is presented that indicates that the size and financial liberalisation of capital markets affect volatility. While the markets with the greatest exposure to international investors exhibit greater volatility in the long-run, the presence of non-residents in the market appears to contribute to more efficient pricing of these instruments.

The limited sample restricts the ability to generalise these findings, however, the finding that differences exist in the volatility of these markets even though they are geographically similar indicates the value of this methodological approach.

The finding that greater capital mobility may result in increased volatility and greater efficiency has significant policy implications for governments and market regulators who have to weigh the costs and benefits of financial liberalisation.

The paper employs a unique data set to model the volatility characteristics of the selected T-bills to improve the understanding of the behaviour of these important instruments in Sub-Saharan frontier markets. More specifically the study provides a novel empirical approach to addressing the question of whether capital mobility is linked to increased volatility. The finding that capital mobility is linked to greater market efficiency offers a fresh insight to this debate.

This study examined the dynamic role of the Japanese property sector, particularly the real estate investment trusts (REITs), in mixed-asset portfolios of stocks and bonds, as well as office, retail, hotel and residential REITs.

Daily data were retrieved from 01 January 2008 to 31 December 2019. The sample time frame consisted of in-sample and out-of-sample periods. The dynamic conditional correlation-generalised autoregressive conditional heteroskedastic (DCC-GJRGARCH) model was deployed to obtain the forecast estimates of time-varying volatility of REITs and correlations with other assets. The estimates were employed to construct out-of-sample portfolios based on the three assets for daily investment. The five sets of portfolios with each individual property sector REITs, as well as a portfolio of stocks and bonds that served as a benchmark, were produced. The average utility for each set of portfolios was estimated and compared with the average utility of the benchmark portfolio. The average transaction cost (TC) for portfolio rebalancing was calculated as well.

The forecast of volatility estimates for each property sector revealed that each asset displayed a similar pattern with the differences in the volatility magnitude. Notably, hotel and retail REITs were more volatile than other property sector REITs. The property sector REITs exhibited a positive correlation with stocks but negatively linked with bonds. The results unveiled the diversification benefits of incorporating property sector REITs. The portfolio with property sector REITs had higher risk-adjusted returns and utility, compared to portfolio consisting of stocks and bonds. The benefits outweighed the TC for portfolio rebalancing.

This study highlights the importance of quantifying the conditional time-varying volatility and correlations of the property sector REITs with other asset returns, especially for investment decision, to select and include property sector REITs in mixed-asset portfolios. For fund managers seeking liquid assets in daily investment, this analysis suggests the inclusion of hotel and retail REITs to enhance REITs' portfolio performance.

This study is the first to investigate the dynamic characteristics of the volatility and correlation of each property sector REITs with other financial assets by employing the conditional framework that accounted for short- and long-run persistency in economic shocks. The reported outcomes shed light on the differences in the underlying properties that contribute to the variances in dynamic volatility of each sector REITs, as well as REITs' correlations with stocks and bonds. This application enables the authors to transmit the dynamics of variance-covariance matrix amongst each property sector REITs, stocks and bonds into asset allocation problem on a daily basis.

This paper investigates the relationship between institutional ownership and idiosyncratic volatility in Chinese and the USA stock markets and explores the potential explanations.

In this paper, the authors use the panel data regressions and the dynamic tests of two-way Granger causality in the panel VAR model to examine the relationship between institutional ownership and idiosyncratic volatility in Chinese and the USA stock markets.

The authors find that the institutional ownership in the Chinese (the USA) stock market is significantly and positively (negatively) related to idiosyncratic volatility through various tests. This paper indicates that institutional investors in the USA are more prudent and risk-averse, while the Chinese institutional investors are not because of high risk-bearing capacity.

This paper deepens the authors’ understanding on the relationship between institutional ownership and idiosyncratic volatility and in the USA and the Chinese stock markets. This paper explains the opposite relationships between institutional ownership and idiosyncratic volatility in the stock markets in China and USA.

The main purpose of this study is to enunciate the underlying factors that enhance the performance of scaled momentum strategies.

In previous studies, the negative relationship between the lagged volatility and future return of momentum strategy is exploited to manage the risk. But this negative relationship only holds when volatility is higher, further the volatility is shown to be persistent. The implication of these two characteristics is important and this paper highlights that.

The higher performance of the scaled momentum strategies for the US market is linked with the length of the investment horizon. The traditional asset pricing models fail to explain this relationship. However, the authors find that the excess variance loaded on the long side of these strategies is one important explanation of this horizon bound performance of these strategies.

This study highlights that the volatility scaled momentum strategy has higher gains as the investment horizon increases. Therefore, it is an advisable investment strategy for the pension fund industry.

Momentum strategy is unique as it fulfils two criteria of performance enhancement through volatility scaling, such as, the persistent in volatility and its negative relationship with the returns. However, the impact on the performance of the negative relationship between volatility and return that only exist in highest volatility related states is not discussed. The authors have shown that this aspect of volatility and return relationship of the momentum strategy has an important bearing on the performance of the volatility scaled momentum strategies.

Highlights of the Paper

1. This study finds that the Sharpe ratios and the alphas of the volatility scaled strategies increase as the investment horizon increases.

2. This is because the volatility series are highly persistent and the negative predictive relationship between the volatility and future momentum returns only exist when the volatility is higher. The impact of these two characteristics of the volatility series on the performance of the scaled momentum strategies is not discussed in the literature.

3. We find that the scaled strategies invest more/less when the volatility of the momentum strategy is lower/higher. By investing less when volatility is higher, the scaled strategies avoid momentum crashes and lessens the contribution of the variance from the short side in the overall variance of these strategies.

4. It is further shown that the higher performance of the volatility scaled strategies, at each investment related horizon can be explained by the higher variance loaded on the long side of such strategies in comparison to the traditional momentum strategy.

This study finds that the Sharpe ratios and the alphas of the volatility scaled strategies increase as the investment horizon increases.

This is because the volatility series are highly persistent and the negative predictive relationship between the volatility and future momentum returns only exist when the volatility is higher. The impact of these two characteristics of the volatility series on the performance of the scaled momentum strategies is not discussed in the literature.

We find that the scaled strategies invest more/less when the volatility of the momentum strategy is lower/higher. By investing less when volatility is higher, the scaled strategies avoid momentum crashes and lessens the contribution of the variance from the short side in the overall variance of these strategies.

It is further shown that the higher performance of the volatility scaled strategies, at each investment related horizon can be explained by the higher variance loaded on the long side of such strategies in comparison to the traditional momentum strategy.

The purpose of this paper is to examine whether the house prices in Finland share financial characteristics with assets such as stocks. The studied regions are 15 main regions in Finland over the period of 1988:Q1-2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two rooms and more than three rooms apartment types.

Both Ljung–Box and Lagrange multiplier tests are used to test for clustering effects (autoregressive conditional heteroscedasticity effects). For cities and sub-areas with significant clustering effects, the generalized autoregressive conditional heteroscedasticity (GARCH)-in-mean model is used to determine the potential impact that the conditional variance may have on returns. Moreover, the exponential GARCH model is used to examine the possibility of asymmetric effects of shocks on house price volatility. For each apartment type, individual models are estimated; enabling different house price dynamics, and variation of signs and magnitude of different effects across cities and sub-areas.

Results reveal that clustering effects exist in over half of the cities and sub-areas in all studied types of apartments. Moreover, mixed results on the sign of the significant risk-return relationship are observed across cities and sub-areas in all three apartment types. Furthermore, the evidence of the asymmetric impact of shocks on housing volatility is noted in almost all the cities and sub-areas housing markets. These studied volatility properties are further found to differ across cities and sub-areas, and by apartment types.

The existence of these volatility patterns has essential implications, such as investment decision-making and portfolio management. The study outcomes will be used in a forecasting procedure of the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study.

To the best of the author’s knowledge, this is the first study that evaluates the volatility of the Finnish housing market in general, and by using data on both municipal and geographical level, particularly.

Environmental volatility is a central construct in strategy studies. This paper argues that three factors confound the literature on volatility: asymmetry in conceptualization, asymmetry in operationalization, and lack of attention to level of analysis. These limitations inhibit the development of the concept and make much of the research on volatility non‐additive. However, environments do matter and to make better sense of it we need a meta‐conceptualization. To do this, the paper presents a process‐based resources‐oriented view of volatility that argues that the volatility experienced by the firm is largely a function of the resources it has available to meet the demands made of it. It is proposed that volatility originates from four basic resource configurations: managerial‐human resources configuration, physical resources‐conversion configuration, intangible resources configuration, and positional configuration. Propositions consistent with prior theories and incorporating the new resources‐oriented viewpoint are presented and discussed.

The purpose of this study is to investigate empirically the pattern of co-movement between prices and implied volatility in the future markets for crude oil.

The tool of non-parametric quantile regression is applied to daily price returns and implied volatility changes from 2007 to 2018.

For the total sample period, the link between price returns and forward-looking volatility expectations is contemporaneous, negative and asymmetric, and it exhibits an (approximately) inverted U-shaped pattern suggesting that: the pricing of implied volatility is heavier for large (in absolute value terms) changes relative to small ones and it is lighter for large positive changes relative to large negative ones. The pattern of co-movement, therefore, appears to be in line with the theoretical postulates of fear, exuberance and loss aversion. The main characteristics of the relationship are present in some (but not in all) sub-periods, which are also considered in this study.

Less than a handful of works have assessed the link between implied volatility and prices for commodity ETFs. This is the first one relying on flexible non-parametric quantile regressions.

This paper aims to examine the customer-based and supplier-based trade credit gaps for USA firms from 1970 to 2020.

The authors' study examines USA companies from 1970 to 2020. The authors begin with an analysis of the trends in aggregate working capital, the capital's components and the trade credit gaps. Various regression models are used to estimate the impacts of identified firm characteristics and unidentified sources on customer-based and supplier-based trade credit gaps over time. The authors then decompose the impacts of firm characteristics to further understand whether changing firm characteristics and/or changing sensitivity to firm characteristics drive the variation in trade credit gaps.

There is a gradual reduction in the customer-based trade credit gap and a substantial expansion in the supplier-based trade credit gap. Though identified firm characteristics have dominant impacts on observed trade credit gaps, there is evidence of the effects of time and unobservable factors. The main source of changes in customer-based and supplier-based trade credit gaps lies in changes in sensitivity to firm characteristics. In addition, the authors find that firm age is the factor with the largest average effect on both trade credit gaps when examining the full sample period. However, different firm characteristics appear to be the key driver of variations in trade credit gaps over time and across the two types of trade credit gaps. The authors also find that financial distress has the least impact on both customer-based and supplier-based trade gaps. There are variations in the firm characteristics with the largest impacts when evaluating decade-long evaluation periods.

To the authors' knowledge, this is the first paper to examine the customer-based and supplier-based trade credit gaps. The connection between trade credit and the trade credit's corresponding inventory (INV) component extends prior literature on the joint management of trade credit and INV. The authors analyze both identified firm characteristics and unidentified sources in the search for explanations of the trade credit gaps. Furthermore, the authors' study explores the channels through which firm characteristics affect different types of trade credit gaps. The authors' findings help identify relevant and irrelevant risk factors of corporate working capital policy.

Recognising that different property sectors have distinct risk-return characteristics, this paper assesses whether changes in the level and volatility of short- and long-term interest rates differentially affected excess returns of sector-specific Real Estate Investment Trusts (REITs) in the Pacific Rim region between July 2006 and December 2018. The strategic property risk management implications for sector-specific REITs are also identified.

Daily excess returns between July 2006 and December 2018 are used to analyse the sensitivity in the level and volatility of interest rates for REITs among office, retail, industrial, residential and specialty REITs across the USA, Japan, Australia and Singapore. The generalised autoregressive conditionally heteroskedastic in the mean (GARCH-M) methodology is employed to assess the linkage between interest rates and excess returns of sector-specific REITs.

Compared with diversified REITs, sector-specific REITs were less sensitive to short- and long-term interest rate changes across the USA, Japan, Australia and Singapore between July 2006 and December 2018. Of sector-specific REITs, retail and residential REITs were susceptible to interest rate movements over the full study period. On the other hand, office and specialty REITs were generally less sensitive to changes in the level and volatility of short- and long-term interest rate series across all markets in the Pacific Rim region. However, the interest rate sensitivity of industrial REITs was somewhat mixed. This sector was sensitive to interest rate movements, but no comparable evidence was found since the onset of GFC.

The insignificant exposure to interest rate risk of sector-specific REITs may imply that they have a stronger interest rate risk aversion and greater hedging benefits than their diversified counterparts, particularly for office and specialty REITs. The results support the existence of REIT specialisation value in the Pacific Rim region from the interest rate risk management perspective. This is particularly valuable to international property investors constructing and managing portfolios with REITs in the region. Property investors are advised to be aware of the disparities in the magnitude and direction of sensitivity to the interest rate level and volatility of REITs across different property sectors and various markets in the Pacific Rim region. This study is expected to enhance property investors' understanding of interest rate risk management for different property types of REITs in local, regional and international investment portfolios.

The study is the first to assess the interest rate sensitivity of REITs across different property sectors and various markets in the Pacific Rim region. More importantly, this is the first paper to offer empirical evidence on the existence of specialisation value in the Pacific Rim REIT markets from the aspect of interest rate sensitivity. This research may enhance property investors' understanding of the varying interest rate sensitivity of different property types of REITs across the USA, Japan, Australia and Singapore.