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The purpose of this paper is to examine the nature and extent of instability of capital asset pricing model (CAPM) beta in a small emerging capital market.

Inter‐period as well as intra beta instability are examined. Inter‐period instability is examined by Mann‐Whitney z‐scores and Blume's regressions. Intra‐period beta instability is examined using Bruesch‐Pagan LM test and Chow break point test. Robustness tests are performed applying time‐varying parameter models.

Beta instability increases with increase in holding (sample) periods. There is evidence of inter‐period as well as intra‐period beta instability. Analysis of the full eight‐year interval reveals a very high incidence of beta instability, namely, about 26 per cent of the individual stocks tested and about 31 per cent of individual stocks have structural break. The extent of beta instability does not significantly decline when corrected for non‐synchronous trading and thin trading as represented by Dimson beta. However, the extent of beta instability is similar to that of developed market. Time‐varying parameter model under Kalman filter approach using AR(1) specification performs better than any other models in terms of in‐sample forecast errors. Dominance of AR(1) approach suggests that stock betas in DSE are time varying, and shocks to the conditional beta have some degree of persistence which ultimately reverts to a mean. This result is in contrast to the findings of Faff et al. revealing the dominance of Random Walk specification in Australian market, suggesting that shocks to stock beta in Australian market persist indefinitely into the future. These contrasting findings may indicate that beta instability in different markets and for different stocks in the same market are of different nature and different models may be suitable for different markets and different stocks in the same market in capturing the time‐varying nature of beta coefficients.

This study covers only 110 stocks of Dhaka Stock Exchange. It can be extended to include more stocks. The study can also be done in other developing markets.

While the issues of beta instability have been extensively explored for developed markets, evidence for emerging markets is less readily available. The present study contributes to the emerging market literature on beta instability by investigating the extent of beta instability and its time‐varying properties in Dhaka Stock Exchange (DSE), Bangladesh. Understanding the systematic risk behaviour of individual stocks in DSE is important for both local and international investors. With the saturation of investment opportunities in developed markets due to their high integration, and with the enhanced deregulation and liberalization of emerging economies, emerging financial markets like DSE provide suitable and a relatively safe investment environment for international investors and fund managers seeking global diversification for better risk‐return trade‐offs. When most of the world markets declined during the recent global financial crisis, stock prices in DSE experienced a continuous rise. This makes it more interesting as an emerging market to study beta instability.

Reviews previous research on the nature of beta and investigates the stochastic structure of time‐varying beta in Hong Kong, Malaysia and Singapore using the bi‐variate GARCH‐in‐mean model and fractional tests. Develops mathematical models and applies them to 1989‐1998 daily data from all three stock markets. Presents the results, which suggest, in contrast to other findings, that all three time‐varying betas are slowly mean‐reverting (long memory).

As the Association of Southeast Asian Nations (ASEAN) becomes an emerging market, US investors will want to know how their favorite method of calculating asset pricing fits into this new undeveloped market. Also, as the ASEAN becomes more internationalized, managers within will look for ways in which the capital asset pricing model (CAPM) can be applied for their needs. This research looks at the capabilities of the CAPM using ex-post time varying and compares it with the traditional constant beta model. The data include five US sectors and five ASEAN countries, for 10 total portfolios. Find that using a simple nonparametric method that allows for time variation is not statistically different from the traditional constant beta model for portfolios. This research provides additional support for the constant beta.

The purpose of this paper is to empirically estimate industry beta in Indian stock market with three alternative models and compare the accuracy of forecasting error to find the most suitable model for time-varying beta estimation.

The paper applies the standard regression model, Kalman filter model, other statistical approaches and secondary material.

The paper finds that the existence of dynamic beta in Indian market. The results also indicate systematic risk or beta of Indian industries is susceptible to the global economic effect. Finally, the Kalman filter generates the lower forecasting error compared to the other method for almost all the industries.

The accurate estimation of beta which is a measure of systematic risk helps investors to make investment decision easier. The implication of this result is important for finance practitioners such as portfolio managers, investment advisors and security analysts. This study will help to determine the country risk with respect to the global index and analyze the global financial market integration effect on India.

This paper reliably estimate industry portfolio beta for India. The time-varying beta is estimated using Kalman filter method which is rarely applied in Indian literature. This paper contributes by extending the knowledge of existing literature by introducing a new data set with Indian data which is not affected by the “data snooping” bias. This study will also help to determine the country risk with respect to the global index and analyze the global financial market integration effect on India.

This paper empirically investigates the effect of the coronavirus pandemic (COVID-19) on the Indian financial market and firm betas, perhaps the first paper to do so. The results will be helpful for investors tracking betas during future the coronavirus waves.

A conditional capital asset pricing model (CAPM) and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model is used to estimate time-varying daily betas of the 50 largest Indian stocks spread across 16 industries over five years (Nov 2017 to May 2021), including the two waves of COVID-19 in India.

The results show that the betas increased during the COVID wave-1 (2020) but not during COVID wave-2 (2021). Moreover, the increase is more pronounced for consumer goods, infrastructure, insurance and information technology, unlike energy (oil and gas, power and mining) industries. Further, there are positive abnormal residual returns during the COVID waves. The results will be helpful for investors tracking betas during future COVID-19 waves.

This is perhaps the first paper to study the firm betas in light of the COVID-19 pandemic.

The purpose of this paper is to estimate New Zealand's country level risk using a time‐varying country beta market model. Country beta is allowed to vary as a function of several macro‐economic variables, including the net government overseas borrowing, 90‐day bill rate, ten‐year bill rate, wool price, trade‐weighted index, manufacturers’ price index, retail trade, current account balance, and money supply.

Multivariate regression analysis is used to test the relation between country volatility and the macro‐economic variables for the period September 1985 to March 2000.

It is found that the US dollar exchange rate (USD) and the monetary conditions index (MCI) have a significant impact on New Zealand's country beta. The temporal variance of New Zealand's country beta displayed a great deal of volatility prior to and immediately following the 1987 stock market crash. The beta was far less volatile during the 1990s.

The variable set is restricted by the availability of data concerning the key macro‐economic statistics.

Risk at the country level is of increasing importance in the evaluation of offshore investments. Practical implications relate to the evaluation of investments in foreign markets, specifically the appropriate cost of capital, given increased integration of financial markets.

The study provides a better appreciation of the relationship between the country beta and several macro‐economic variables that has not been applied to the New Zealand economy before.

The purpose of this paper is to examine the predictive ability of different well-known models for capturing time variation in betas against a novel approach where the beta coefficient is treated as a function of market return.

Different GARCH models, the Kalman filter algorithm and the Schwert and Seguin model are used against our novel approach. The mean square error, the mean absolute error and the Diebold and Mariano test statistic constitute the measures of forecast accuracy. All models are tested over nine consecutive years and three different samples.

The results show substantial differences in predictive accuracy among the samples. The new approach of modelling the systematic risk overwhelms the rest of the models in longer samples. In the smallest sample, the Kalman filter random walk model prevails. The examination of parameters between two groups of stocks with best and worst accuracy results depicts significant variations. For these stocks, the iid assumption of return is rejected and large differences exist on diagnostic tests.

This study contributes to the literature with different ways. First, it examines the predictive accuracy of betas with different well-known models and introduces a novel approach. Second, after constructing betas from the estimated models’ parameters, they are used for out-of-sample instead of in-sample forecasts over nine consecutive years and three different samples. Third, a more closely examination of the models’ parameters could signal at an early stage the candidate models with the expected lowest forecasting errors. Finally, the study carries out some diagnostic tests for examining whether the existence of iid normal returns is accompanied by better performance.

The purpose of this paper is to quantify beta for an online gambling portfolio in the UK and investigates whether it is time-varying. It also examines the dynamic correlations of the online gambling portfolio with both the market and socially responsible portfolios. In addition, this paper documents the effect of important UK gambling legislation on the betas and correlations of the online gambling portfolio.

This study uses static and time-varying models (e.g. rolling regressions, multivariate GARCH models) to estimate betas and correlations for a portfolio of UK online gambling stocks.

This study finds that beta for the online gambling portfolio is less than 1, indicative of defensiveness toward the market, a result that is consistent with prior literature for sin stocks. In addition, the conditional correlation between the market and online gambling portfolio is small when compared to the correlation of the market and socially responsible portfolios. Findings suggest that the adoption of the Gambling Act 2005 increases the conditional correlation between the market and online gambling portfolio and it also increases the conditional betas for the online gambling portfolio.

This paper serves as a starting point for future research on online gambling stocks. Going forward, studies can focus on the financial performance or accounting performance of online gambling stocks.

This empirical investigation provides insight into the risk characteristics of publicly listed online gambling companies in the UK.

A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-à-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management.

Glode provides theoretical and empirical evidence that, in aggregate, funds underperform during economic expansions and outperform during contractions. The authors find that this result is not robust to the more appropriate conditional CAPM and to alternative methods for estimating market states. The purpose of this paper is therefore to thoroughly analyze mutual fund performance across the business cycle by disaggregating funds into different investment objectives to determine which funds possess this cyclical performance and which do not.

In this paper, the authors employ a conditional asset pricing model that better captures the variations in the pricing kernel in different economic states. The empirical model adjusts for time‐variation in both risk (beta) and performance (alpha). The authors specify economic states using an ex‐ante measure, the expected market risk premium. This measure is continuous and better captures changing economic circumstances than the ex‐post, binary NBER cycle dates that are common in the mutual fund literature.

In this conditional framework, the authors find that recession protection is only offered by certain types of equity mutual funds. Managers of small‐cap and mid‐cap growth equity funds are able to deliver such state‐dependent performance but managers of value funds do not. In a comparison of active mutual funds with passive counterparts, it is found that both the stocks held by the small‐cap managers as well as their stewardship of the portfolio contribute to that performance.

Drawing from the recent asset pricing literature, the authors are the first to adapt an integrated conditional CAPM framework to examine the state‐dependent performance of mutual funds. Rather than report aggregate equity mutual fund performance, the authors provide an analysis for subsets of mutual funds separated by investment styles. Both managers of and investors in these funds will benefit from an understanding of how portfolio performance is impacted by changing economic conditions.