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Using a portfolio comprising liquid global stocks and bonds, this study aims to limit absolute risk to that of a standardised benchmark and determine whether this has a significant impact on expected return in both high volatility period (HV) and low volatility period (LV).

Using a traditional benchmark comprising 40% equity and 60% bonds, a constant tracking error (TE) frontier was constructed and implemented. Portfolio performance for different TE constraints and different economic periods (expansion and contraction) was explored.

Results indicate that during HV, replicating benchmark portfolio risk produces portfolios that outperform both the maximum return (MR) portfolio and the benchmark. MR portfolios outperform those with the same risk as that of the benchmark in LV. The MR portfolio weights assets to obtain the highest return on the TE frontier. During HV, the benchmark replicated risk portfolio obtained a higher absolute risk value than that of the MR portfolio because of an inefficient benchmark. In HV, the benchmark replicated risk portfolio favoured intermediate maturity treasury bills.

There is a dearth of literature exploring the performance of active portfolios subject to TE constraints. This work addresses this gap and demonstrates, for the first time, the relative portfolio performance of several standard portfolio choices on the frontier.

This research study aims to design a novel risk-managed time-series momentum approach. The present study also examines the time-series momentum effect in the Indian equity market. Apart from this, the study also proposes a novel risk-managed time-series momentum approach.

The study considers the adjusted monthly closing prices of the stocks listed on the Bombay Stock Exchange from January 1996 to December 2020 to formulate long-short portfolios. Newey–West t statistics were used to test the significance of momentum returns. The present research has considered standard risk factors, i.e. market, size and value, to evaluate the risk-adjusted performance of time-series momentum portfolios.

The present research reports a substantial absolute momentum effect in the Indian equity market. However, absolute momentum strategies are exposed to occasional severe losses. The proposed time-series momentum approach not only yields 2.5 times higher return than the standard time-series momentum approach but also causes substantial enhancement in downside risks and higher-order moments.

The study's outcomes offer valuable insights for professional investors, capital market regulators and asset management companies.

This study is one of the pioneers attempting to test the time-series momentum effect in emerging economies. Besides, current research contributes to the escalating literature on risk-managed momentum by suggesting a novel revised time-series momentum approach.

The nature of price volume relationship in asset market has been an interesting subject in financial research as it reveals a very important aspect which has implications for market efficiency. The purpose of this paper is to examine price volume relationships in Indian commodity futures market.

There are two competing models in price volume relationship. Mixture of distribution hypothesis, suggesting a positive contemporaneous relationship and sequential information arrival hypothesis (SIH), suggesting a positive intertemporal causal relationship. Both are tested using correlation coefficient and Granger causality test with vector auto regressive methodology.

Though there exists contemporaneous correlation between volume and price change in some of the cases, but in general on the basis of the presence of Granger causality it follows that SIH is supported.

As only three commodities futures have been studied in this paper, this study can be extended to include more number of commodities currently being traded so as to make it more exhaustive.

The research has been done with the data of MCX Gold, MCX Silver and MCX Crude. The results of causality suggest that inefficiency level is maximum in Silver which may be attributed to informational asymmetry.

The Indian commodity futures market is of very recent origin. Hence, very little research work has been undertaken in this space. The paper presents an assessment of the existence of informational asymmetry among the three commodity futures under the study.

A well-documented pattern in the literature concerns the outperformance of small-cap stocks relative to their larger-cap counterparts. This paper aims to address the “small-cap versus large-cap” issue using for the first time data from the exchange traded funds (ETFs) industry.

Several raw return and risk-adjusted return metrics are estimated over the period 2012-2016.

Results are partially supportive of the “size effect”. In particular, small-cap ETFs outperform large-cap ETFs in overall raw return terms even though they fail the risk test. However, outperformance is not consistent on an annual basis. When risk-adjusted returns are taken into consideration, small-cap ETFs are inferior to their large-cap counterparts.

This research only covers the ETF market in the USA. However, given the tremendous growth of ETF markets worldwide, a similar examination of the “small vs large capitalization” issue could be conducted with data from other developed ETF markets in Europe and Asia. In such a case, useful comparisons could be made, so that we could conclude whether the findings of the current study are unique and US-specific or whether they could be generalized across the several international ETF markets.

A possible generalization of the findings would entail that profitable investment strategies could be based on the different performance and risk characteristics of small- and large-cap ETFs.

This is the first study to examine the performance of ETFs investing in large-cap stock indices vis-à-vis the performance of ETFs tracking indices comprised of small-cap stocks.

This paper employs three months of observations sampled at 60‐second intervals to analyze the behavior of two basket indices from the emerging Athens Stock Exchange: the General Index of the Main (Listed) Securities Market and the Index of the Secondary (Unlisted) Securities Market. The empirical analysis employs robust regression using dummy variables to uncover a rich variety of time‐of‐day regularities in the first four moments of the distribution of returns, the tail behavior, and the dynamic and cross‐dynamic behavior of the two markets. Markets tend to behave differently during their opening and closing, while results are invariably sensitive to outliers. Overall, the results are comparable to those reported for developed equity markets. However, in contrast to other studies, we find no conclusive evidence of long‐memory in either the mean or variance process. ARMA models of seasonally differenced absolute returns were used as a simple but effective way of dealing with the strong regularity in volatility.

This study examines the information content of firms’ operations-related disclosures (ORDs) and the importance of these disclosures as an information source to stock markets relative to other commonly examined sources of information. I find that ORDs constitute a large portion of corporate press releases. These disclosures are associated with significant stock price reactions and trading volume. The stock price reactions to ORDs are greater than the reactions to 10-K/Q reports and are of similar magnitudes to the reactions to 8-K filings. On average, ORDs explain variation in firms’ quarterly returns to a similar degree as management earnings forecasts and 10-K/Q reports for the full sample and to a greater degree for small firms and firms with lower earnings quality.

The real estate markets in Asia have attracted significant investor attention as they have grown rapidly in recent years. Both local and foreign investors continue to display a strong appetite for Asian real estate investment projects. Given the different characteristics of listed real estate stocks, the purpose of this paper is to focus on the causal relations between the financial variables of these stocks. This financial analysis can help investors to understand the characteristics of listed real estate companies, provide implications for optimal asset allocation decisions, and also increase the predictability of portfolio returns.

In this research, the paper investigates the contemporaneous and causal relations between stock returns, trading volume and volatility in a domestic market context and between different national markets for listed real estate companies in seven Asian economies.

The paper finds that there are positive contemporaneous relations between trading volume and both returns and absolute returns. When the paper examines the causal relations between the financial variables, the evidence implies that current trading volume helps to explain the returns indirectly by leading return volatility; however, trading volume does not help to explain future returns directly. Extending the causality test to international markets, the listed real estate portfolios of the four Southeast Asian countries are found to be more closely correlated than those of the other three countries studied here. Among the four Southeast Asian countries, Singapore, the only developed country, is found to play an influential role, its current financial variables having predictive power for the other countries.

This research provides global investors with a better understanding of the Asian listed real estate market, showing that trading volume contains important information regarding returns, that the characteristics of listed real estate companies are closer to those of the financial market than those of the real estate markets, and that the markets of the major economies have extensive influence over the smaller markets. Moreover, given the scarcity of research on the performance of Asian listed real estate companies themselves, this study improves the completeness of the academic literature.

The purpose of this paper is to examine the long memory property of equity returns and volatility of emerging equity market by focusing on the Malaysian equity market, namely the Kuala Lumpur Stock Exchange (KLSE).

The study adopts the Fractionally Integrated GARCH (FIGARCH) model and Fractionally Integrated Asymmetric Power ARCH (FIAPARCH), focusing on the Malaysian data covering the period from April 15, 2004 to April 30, 2007.

The study finds evidence of long memory property as well as asymmetric effects in the volatility of the KLSE. The traditional ARCH/GARCH is shown to be insufficient in modeling the volatility persistence. The FIAPARCH specification outperforms the FIGARCH model by capturing both asymmetry effects and long memory in the conditional variance.

The results of this study have practical implications for the investors intending to invest in the emerging markets such as Malaysia. Understanding volatility and developing the appropriate models are important since volatility can be a measure of risk which is highly relevant in forecasting the conditional volatility of returns for portfolio selection, asset pricing, and value at risk, option pricing and hedging strategies.

This study contributes in providing the empirical evidence on the long memory property of equity returns and volatility of an emerging equity market with reliable estimation models, which is currently lacking, particularly for emerging markets.

The purpose of this paper is to examine the evidence of long-range dependence behaviour in both house price returns and volatility for fifteen main regions in Finland over the period of 1988:Q1 to 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 apartments types.

For each house price return series, both parametric and semiparametric long memory approaches are used to estimate the fractional differencing parameter d in an autoregressive fractional integrated moving average [ARFIMA (p, d, q)] process. Moreover, for cities and sub-areas with significant clustering effects (autoregressive conditional heteroscedasticity [ARCH] effects), the semiparametric long memory method is used to analyse the degree of persistence in the volatility by estimating the fractional differencing parameter d in both squared and absolute price returns.

A higher degree of predictability was found in all three apartments types price returns with the estimates of the long memory parameter constrained in the stationary and invertible interval, implying that the returns of the studied types of dwellings are long-term dependent. This high level of persistence in the house price indices differs from other assets, such as stocks and commodities. Furthermore, the evidence of long-range dependence was discovered in the house price volatility with more than half of the studied samples exhibiting long memory behaviour.

Investigating the long memory behaviour in both returns and volatility of the house prices is crucial for investment, risk and portfolio management. One reason is that the evidence of long-range dependence in the housing market returns suggests a high degree of predictability of the asset. The other reason is that the presence of long memory in the housing market volatility aids in the development of appropriate time series volatility forecasting models in this market. The study outcomes will be used in modelling and forecasting 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 authors’ knowledge, this is the first research that assesses the long memory behaviour in the Finnish housing market. Also, it is the first study that evaluates the volatility of the Finnish housing market using data on both municipal and geographical level.

The main purpose of this paper is to examine the asymmetry and long memory properties in the volatility of the stock indices of the PIIGS economies (Portugal, Ireland, Italy, Greece and Spain).

The paper utilizes the wavelets approach (based on Haar, Daubechies‐4, Daubechies‐12 and Daubechies‐20 wavelets) and the GARCH class of models (namely, ARFIMA (p,d′,q)‐GARCH (1,1), IGARCH (1,1), FIGARCH (1,d,0), FIGARCH (1,d,1), EGARCH (1,1) and FIEGARCH (1,d,1)) to accomplish the desired goals.

The findings provide evidence in support of the presence of long range dependence in the various proxies of volatility of the PIIGS economies. The results from the wavelet approach also support the Taylor effect in the volatility proxies. The results show that ARFIMA (p,d′,q)‐FIGARCH (1,d,0) model specification is better able to capture the long memory property of conditional volatility than the conventional GARCH and IGARCH models. In addition, the ARFIMA (p,d′,q)‐FIEGARCH (1,d,1) model is better able to capture the asymmetric long memory feature in the conditional volatility.

This paper has both methodological and empirical originality. On the methodological side, the study applies the wavelet technique on the major proxies of volatility (squared returns, absolute returns, logarithm squared returns and the range) because the wavelet‐based estimator exhibits superior properties in modeling the behavior of the volatility of stock returns. On the empirical side, the paper finds asymmetry and long range dependence in the conditional volatility of the stock returns in PIIGS economies using the GARCH family of models.