Beta Instability Research Articles

Beta Instability and Implications for Hedging Systematic Risk: Takeaways from the COVID-19 Crisis

The authors use the COVID-19 crisis as an example to highlight two dimensions of beta instability that have been largely overlooked: First, the relative order in stock betas could change drastically when entering a crisis as the nature of market risk shifts. Second, although the relative order in beta may remain stable throughout a crisis, the absolute market risk sensitivity could also display significant changes from day to day in how individual stock returns react to the market news. These two dimensions of beta instability pose challenges for risk hedging. The authors compare the efficacy of a few approaches that hedge this systematic risk. Using a generic momentum portfolio as an illustration, the authors find that an optimized portfolio approach that maximizes/minimizes the momentum signal while matching the beta and sector exposures of the long and short legs can reduce the worst daily return and the volatility during the COVID-19 crisis by more than 60%. Their findings extend beyond this crisis period and can help investors address beta instability and hedging more broadly. <b>TOPICS:</b>Security analysis and valuation, quantitative methods, risk management, financial crises and financial market history <b>Key Findings</b> ▪ Both sector beta and relative riskiness of individual stocks within a sector could be unstable, as evidenced in the COVID-19 crisis. ▪ The magnitude of systematic reaction could also change from day to day. ▪ An optimized portfolio approach proves flexible and efficient in hedging systematic risk exposures.

Modeling time-varying beta in a sustainable stock market with a three-regime threshold GARCH model

This study revisits an important issue in financial theory: the instability of market beta. To this end, we demonstrate that the linear constant risk model is misleading and does not reproduce changes in beta correctly. We develop a new nonlinear market model to capture beta instability over time for three main states: bear, normal, and bull markets. Our model endogenously identifies these states and their thresholds. We then apply this econometric specification to four major sustainable stock indexes in the US, Europe, Asia, and the World for 2004–2015. The results provide three main findings. First, the market beta is time-varying and changes asymmetrically and nonlinearly, suggesting that the systematic risk statistically differs between market regimes for the US, Europe, and the World. Second, the positive sign of beta in the bull market for these three regions suggests that systematic risk increases as economic conditions improve. Third, the lowest level of beta in the bear market indicates the usefulness of the sustainable stock index to hedge and cover investors’ portfolios against risk, particularly in a bear market.

What drives the premium labour model, beta instability risk or human capital?

Purpose – The paper examines the conditional capital asset pricing model (CCAPM) of Jagannathan and Wang using the UK data and develops a data-driven measure of beta instability risk that is pertinent to the UK stock market. In contrast to the view that the main part of the Jagannathan and Wang's model is the inclusion of human capital, however, the paper finds that human capital remains insignificant in most tests. Design/methodology/approach – Data were taken from the London Share Price Database and Datastream. This paper therefore examines the premium labour (PL) model of Jagannathan and Wang using the UK data, while the paper attaches particular importance to the measure of beta instability as a source of time variation in betas. In analysing the measure of beta instability risk, this study considers a testable measure of instability risk that varies across markets and across time as the interaction between the stock market and the economy varies across different time periods. Hence, this paper develops a data-driven measure of beta instability risk that is pertinent to the UK stock market. Findings – The results confirm the premium version of the model, that is, the CCAPM without a proxy for human capital. In particular, the paper finds that over the entire time period of this study, the measure for beta instability risk and market portfolio has significant explanatory power for the variations of returns. More specifically, when using the average earnings index as a proxy for human capital in the PL model, the premium model performs better than the PL model. When total income from employment is used as a proxy for human capital, the performance of the PL model improves for the full period. However, the results for the two sub-periods are less favourable for the PL model as, again, labour income is not priced for these periods. These results indicate that the PL model is sensitive to proxies used for human capital. Originality/value – The results revive the importance of beta instability risk in CCAPM of Jagannathan and Wang's model and suggest that the beta instability drives this model.

Beta is Still Useful

This paper investigates whether beta can predict the expected return after controlling for the beta instability resulting from shift in the covariance structure. Such a shift is primarily due to noise investors chasing stocks with high idiosyncratic volatility. Consequently, these stocks tend to have low future returns from overpricing, and high beta because noise investors can also move the market at the same time. Indeed, we see that the beta estimate of the current period is positively related to the beta estimate and negatively related to the idiosyncratic volatility measure of the last period. More important, different from existing studies, we find that beta estimates of the current period can significantly explain the cross-sectional differences in future returns of individual stocks, when allowing for an interaction between the current idiosyncratic volatility and the beta estimates. We also show that our simple model can predict the historical expected return well. All results are robust with respect to different measures of beta and idiosyncratic volatility and subsample.

Asset Pricing Behaviour with Dual-Beta in Case of Pakistani Stock Market

This study investigates the dynamics of beta by the asymmetric response of beta to bullish and bearish market environment on 50 stocks traded in Karachi Stock Exchange during 1993-2007. The results show that the betas increase (decrease) when the market is bullish (bearish). The results however suggest that investors receive a positive premium for accepting down-side risk, while a negative premium is associated with up-market beta. The results suggest that the conditional Fama and French three factor model has performed better than the conditional CAPM when news asymmetry was taken into account compared with the unconditional Fama and French three factor model and the unconditional dual-beta CAPM in explaining the relationship in beta and returns in case of Pakistani market. JEL classification: G12, G15 Keywords: Beta Instability, High Market Beta, Low Market Beta, EGARCH Model, News Asymmetry, Fama and French Three Factor Model

Instability of stock beta in Dhaka Stock Exchange, Bangladesh

PurposeThe 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.Design/methodology/approachInter‐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.FindingsBeta 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.Research limitations/implicationsThis 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.Originality/valueWhile 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.

Time-Varying Betas of German Stock Returns

The market model assumes stock returns to be a linear function of the market return. However, there is considerable evidence that the beta stability assumption commonly used when estimating the market model is invalid. In this paper we account for beta instability in German stock returns by allowing the coefficients to vary over time in estimation. For time-varying beta estimation we rely on the Flexible Least Squares approach, the Random Walk Model and Moving Window Least Squares. Due to our results time-varying estimation fits the data considerably better than time-invariant estimation and, hence, increases the efficiency of beta based risk measurement.

Beta Instability of Firms: The Case of the Taiwan Stock Market During Its Financial Development

In this paper, we find evidence of the intra- and inter-period beta instability of firms in the Taiwan stock market during its financial development from 1982 to 1998. Particularly noteworthy is the result that the proportions of firms exhibiting beta instability, in the full sample and two well-known export-oriented industries, the textile and the electronics industries, are lower in the sub-period January 1987-December 1992 than in the other two sub-periods, January 1982- December 1986 and January 1993-December 1998. The result may be attributable to non-fundamental-driven optimism or pessimism about the market induced by speculative capital flows during the sub-period January 1987- December 1992.

A Multivariate Test of a Dual‐Beta CAPM: Australian Evidence

AbstractI apply a multivariate one‐step testing procedure to investigate a dual‐beta CAPM. I begin by establishing that there is no statistical relation between beta and returns for the standard CAPM. I then re‐cast the one‐step test to accommodate a dual‐beta CAPM under bull and bear market conditions. When the excess market return is negative (positive), I find strong evidence of a negative (positive) relation between beta and returns. The strength of my results suggests that the success of the model is not crucially dependent on the argument for beta instability.

An investigation into the extent of beta instability in the Singapore stock market

This paper explores the issue of beta instability in the Singaporean stock market over the period 1986 to 1993. Analysis of the full eight-year interval reveals a very high incidence of beta instability, namely, at about 40% of the individual stocks tested. However, this sample straddles a listing change between the Singapore and Kuala Lumpur markets which occurred at the beginning of 1990. Accordingly, to assess what impact this listing change may have on our results, we examine the four-year sub-periods either side of the listing change, as well as the four-year sub-period which straddles the listing change. A comparison of our results across these different smaller overlapping subperiods indicates an incidence of beta instability for individual stocks at about 20% or less – a somewhat lower level than that observed for the eight-year sample. Hence, while this analysis suggests an absence of a listing change effect on beta stability, a sample length effect may exist. Our results are largely insensitive to at least three different factors. First the results are insensitive to whether betas are estimated by OLS or the Dimson (1979)technique. Second the results are insensitive to whether the Straits Times Index or the Morgan Stanley Country Index for Singapore is used to measure the market returns. Finally the results are insensitive to survivorship bias.

Beta stability and monthly seasonal effects: evidence from the Australian capital market

A number of studies exist across a range of equity markets showing that a significant proportion of stocks in those markets have betas that vary over time. A research challenge posed by this body of evidence is to identify the factors that explain this time variation in individual stock betas. There is also an extensive literature reporting the existence of strong monthly seasonal patterns in equity returns from many markets and across extraordinarily lengthy time periods. Accordingly, using monthly Australian equity returns data, this paper investigates whether seasonal regularities such as the January effect can provide an explanation of beta variation. A key finding of this paper is that taking account of the January monthly seasonal and other monthly seasonals has no effect on the beta stability characteristics of individual stocks. Hence, based on our analysis, seasonal effects does not provide an explanation of beta instability. A subsiduary finding is that stocks with significant monthly seasonal effects tend to have a smaller average market capitalization.

A new test of the relationship between regulatory change in financial markets and the stability of beta risk of depository institutions

The US economy experienced major regulatory changes in the banking and finance industry with the passage of five major acts over the period 1980 to 1991. This has generated an extensive literature investigating the effects of these changes on the US banking industry. In particular, this body of research has examined the share market reaction to regulatory changes, as well as their impact on the risk, return, market value and profitability of banking industry stocks. Generally, it has been found that the regulatory changes have had a substantial impact, although the effect has not been uniform across all depository institutions. This paper extends this literature by analysing the stability of a sample of eighteen US banking industry stock betas across five periods: a pre-regulatory change period, a monetary experiment period, a deregulation period, a reregulation period and a post-regulatory change period. Based on an analysis of weekly returns, we find that the level of bank risk has increased. Further, we also find a tendency towards greater beta instability in both the monetary experiment and deregulation samples. The degree of beta instability is lowest in the pre-regulatory change and post-regulatory change samples. Overall the monetary experiment and regulatory change coincided with an increased tendency towards beta instability. We also compare our results to a randomly selected control sample of non-banks and find some more general effects of regulatory change on individual stock betas.

Some evidence on the stability of beta coefficients on the JSE

Knowledge of beta stability is crucial for projections involving the use of beta coefficients, in particular, in the areas of project evaluation and portfolio design. This paper investigates the issue of beta stability on the JSE. The investigation reveals how spurious inferences on stability can be made if estimation techniques do not correct for thin trading. The findings reveal that the levels of stability are similar to that reported on UK and USA stock markets once the distortions caused by thin trading are removed. Further a particular phenomenon associated with beta instability, known as the regression tendency, was investigated. This phenomenon was found to be present on the JSE and it was demonstrated empirically how a Bayesian adjustment counteracts this regression bias.

A further examination of the effect of diversification on the stability of portfolio betas

Although many studies have found a non-trivial incidence of beta instability for individual common stocks, there exists controversy over the beta stability characteristics to expect in portfolios formed from these stocks. The extent is examined to which portfolio formation can diversify away beta instability. Specifically, although a constant beta for the market portfolio is acknowledged as the trivial limiting case, particular attention is devoted to shedding light on the speed with which diversification can deliver a reasonable expectation of stable portfolio betas. The issue is examined by forming portfolios which mix together constant beta stocks and varying beta stocks. The evidence is generally consistent with the presence of a diversification effect, but as the portfolio size increases, results do show a greater proportion of constant beta stocks are needed to maintain the relative stability of the portfolio.

Beta stability and portfolio formation

A number of studies using a variety of techniques over a range of different economies and time periods have found evidence of time instability in both individual common stock and porfolio betas. Finance theory would suggest that as individual stocks are aggregated into portfolios, a diversification effect should produce a beta for the portfolio that is relatively more stable, provided that beta instability in the individual stocks is driven, at least in part, by microeconomic factors. Despite this Collins et al. (1987) for US data, and Faff et al. (1992) and Brooks et al. (1992) for Australian data, found a higher degree of beta instability in portfolios formed from randomly chosen stocks, relative to the beta instability found in individual stocks. Collins et al. (1987) attributed this finding to a reduction in background noise through portfolio formation making beta instability easier to detect, and hence, dominating the diversification effect. It is also possible that the increased beta instability could be due to the instability in the individual stocks being driven by macroeconomic factors. This paper investigates the effect of portfolio formation on beta stability by considering a number of portfolios formed from individual stocks with particular beta stability characteristics. In particular, we wish to assess the empirical validity of the competing arguments associated with the different effects. The methodology used is similar to that of Brooks et al. (1992), in that, all testing is conducted using point optimal tests (see King (1987a)).

Beta stability and portfolio formation

A number of studies using a variety of techniques over a range of different economies and time periods have found evidence of time instability in both individual common stock and porfolio betas. Finance theory would suggest that as individual stocks are aggregated into portfolios, a diversification effect should produce a beta for the portfolio that is relatively more stable, provided that beta instability in the individual stocks is driven, at least in part, by microeconomic factors. Despite this Collins et al. (1987) for US data, and Faff et al. (1992) and Brooks et al. (1992) for Australian data, found a higher degree of beta instability in portfolios formed from randomly chosen stocks, relative to the beta instability found in individual stocks. Collins et al. (1987) attributed this finding to a reduction in background noise through portfolio formation making beta instability easier to detect, and hence, dominating the diversification effect. It is also possible that the increased beta instability could be due to the instability in the individual stocks being driven by macroeconomic factors. This paper investigates the effect of portfolio formation on beta stability by considering a number of portfolios formed from individual stocks with particular beta stability characteristics. In particular, we wish to assess the empirical validity of the competing arguments associated with the different effects. The methodology used is similar to that of Brooks et al. (1992), in that, all testing is conducted using point optimal tests (see King (1987a)).

Resolving the mechanisms of current gain increase under forward current stress in poly emitter n-p-n transistors

The mechanisms behind moderate bias current gain ( beta ) increase of n-p-n transistors under forward current stress are investigated in polysilicon emitter transistors processed with different dopant impurities and concentrations, and with different amounts of hydrogen plasma treatment. The results suggest that transport of atomic hydrogen toward the poly/monosilicon interface region and its subsequent passivation of dangling bonds at both poly grain boundaries and the poly/monosilicon interface are responsible for the moderate bias beta increase. To alleviate the beta instability, elimination of hydrogen involvement and/or a higher doping concentration inside the poly emitter in the back-end-of-line (BEOL) processes are/is recommended.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Beta Instability and Stochastic Market Weights

An argument is given for individual firm beta instability based upon the stochastic character of the market weights defining the market portfolio and the constancy of its beta. This argument is generalized to market weighted portfolios and the form of the stochastic process generating betas is linked to that of the market return process. The implications of this analysis for adequacy of models of beta nonstationarity and estimation of betas are considered in light of the available empirical evidence.

Explanations for the Instability of Equity Beta: Risk-Free Rate Changes and Leverage Effects

This paper is an initial attempt to bridge the gap that presently exists between the theoretical and empirical literature on the instability of equity beta. We focus on two factors from the joint Option Pricing Model/Capital Asset Pricing Model framework—leverage and unexpected changes in the risk-free rate—which are hypothesized to influence the instability of equity beta across firms and over time. Using alternative variable parameter regression models, we find that highly leveraged firms exhibit greater equity beta instability than firms with lower leverage. Over time, equity betas exhibit greater instability during periods of large unexpected changes in the risk-free rate when compared to periods with small unexpected changes in the risk-free rate.

Computation of stable equilibria in minimum- B mirror devices

We present a three-dimensional code that solves for plasma equilibria in open-field geometries, which allows analysis of most minimum- B mirror systems. Open confinement with p = p( B) only requires the hydromagnetic equilibrium equation to reduce to one elliptic, scalar partial differential equation involving a tensor pressure. The finite difference form of the equation is solved by an implicit iterative algorithm similar to the ADI (alternating direction implicit) method. Cylindrical grid coordinates are employed, and the mesh spacing is variable to allow representation of far boundary conditions. We have time-optimized the program, so the memory requirements are large. It is found that, when the threshold for hydromagnetic instability is passed, the hydromagnetic equilibrium equation becomes hyperbolic, which leads to an ill-posed problem as predicted by Grad. Equilibria are found for an increasing sequence of pressures until the instability threshold is reached. The present code utilized planes of inversion symmetry common to many minimum- B devices, to allow a reduction in the domain to be studied; Ioffe bar devices, Baseball II, 2 X-II, and the proposed mirror fusion experiment are representable. We present sample results for Baseball II and the Ioffe bar device, including cases near the high beta instability threshold.