Search Results (12)

In this paper, we propose a linear factor model that incorporates investor preferences toward sustainability to analyze indirect effects that climate concerns may have on asset prices. Our approach is based on the relationship between environmental, social, and governance (ESG) investing and climate change considerations by investors. We use ESG scores as a part of the information set used by investors to determine the unconditional version of the conditional capital asset pricing model (CAPM). Our results show that the ESG score allows the linearized version of the conditional CAPM to greatly outperform the classic CAPM and the Fama–French three-factor model for different sorts of stock portfolios, contributing significantly to reducing pricing errors. Furthermore, we find a negative price of risk for stocks that covary positively with ESG growth, which suggests that green assets may perform better than brown ones if ESG concerns suddenly become more pressing over time. Thus, our paper constitutes a step forward in the attempt to shed light on how climate change is priced regardless of the climate risk measure used. Full article

The study focuses on the selected aspects of investment measurement and management for the support of financial and economic decision-making of investors in wood-processing sectors. The aim of the study was to analyze the indicators for the structure and cost of capital of furniture and paper/forest branches in Slovakia, quantify the actual expected return on investment based on the selected methodology, and consequently find out the fundamental differences resulting from the specific conditions of given sectors. Methodologically, the study uses procedures for the weighted average cost of capital (WACC), capital asset pricing model (CAPM) for determining the cost of equity, and calculation of the beta coefficient considering the risk premium. The results of the study demonstrated a similar levered beta in both analyzed sectors (1.17 in furniture, 1.20 in paper/forest), but in each sector for a different reason. The expected rate of return is higher in furniture (7.84%) compared to paper/forest products at the level of 6.04%. The findings provide the possibility of comparing the required and expected rate of return on invested capital and making the appropriate long-term investment decisions. Full article

This paper assesses the extent to which carry trade operations affect the performance of equity and bond markets in a target country, South Africa, by considering the US and the euro area as the funding countries. A two- and three-factor capital asset pricing model (CAPM) is employed to assess whether the pricing of equity and bond markets in South Africa depends on the US dollar/rand and euro/rand carry trade returns. Moreover, the paper uses the quantile regression technique to assess whether this pricing varies with the distribution of the equity and bond returns. The findings support that the US- and euro-funded carry trade are essential factors for the pricing of equity and bond markets in South Africa. Moreover, the results of the two-factor model show a negative relationship between the equity excess return and the US-carry trade returns at lower quantiles of the equity market returns. The positive relationship is observed in the upper quantiles of the equity market. The negative relationship means that carry trade activities reduce equity market returns during a bear market as investors close out their position when conditions in the equity market become unfavourable. The results of the three-factor model, controlling for the global volatility or uncertainty, show that carry trade investors exit the equity market to invest in the bond market when global uncertainty rises. This finding shows that carry trade investors choose less risky assets during rising global uncertainty. Full article

This article analyzed the cost of capital under risk conditions for thermoelectric plants in Brazil, applying the Capital Asset Pricing Model—CAPM and the Weighted Average Capital Cost—WACC. To estimate the local CAPM, we used information from the Electric Energy Index—IEE of publicly traded companies in the electricity sector in Brazil and for the global CAPM, we observed the companies associated with the Edison Electric Institute—EEI, listed on the New York Stock Exchange—NYSE and at the National Association of Securities Dealers Automated Quotations—NASDAQ—USA. The risk conditions for capital costs were represented by Monte Carlo simulation using, as a basis, the WACC of a fuel oil thermoelectric plant and the local and global CAPM. The main results show that the IEE and EEI companies obtained a positive average daily return. Due to the Brazil risk, under deterministic conditions, the local WACC (11.13% p.a.) was more attractive to investors when compared to the global WACC (10.32% p.a.) and the regulatory WACC of 10.55% p.a., established by the National Electric Energy Agency—ANEEL. The most risk-sensitive input variables were: unleveraged beta, net debt and equity. Under risk conditions observed by the market from the point of view of Brazilian companies, the chances of the WACC of the fuel oil thermoelectric plant being 11.1% p.a. was 68.30% and from a global perspective, the chance of WACC being 10.32% p.a. was 99.51%. It is concluded that the cost of capital under risk conditions provides a more realistic view of decision-making for privately held companies. Full article

The rapid growth of electric vehicles, solar roofs, and wind power suggests that the potential growth in green equity investments is an emerging trend. Accordingly, this study measured the predictors of excess equity returns in a portfolio of global green energy producers, from 2010 to 2019. Fixed-effects panel data regressions of daily returns, followed by quantile regressions, were performed. There was some support for the explanation of green equity returns by market returns and market risk (beta), as indicated by the single-factor Capital Asset Pricing Model (CAPM), and the multifactor Fama–French Three-Factor and Fama–French Five-Factor Models. The most significant predictors of green equity returns were Value-at-Risk at a 95% confidence level, and Value-at-Risk at a 99% confidence level. Expected Shortfall was another extreme risk value measure. The importance of extreme value measures suggests the presence of fat-tailed leptokurtic distributions, whereby excess returns were explained by the risk of loss given adverse conditions, primarily at 95% confidence. We conclude that the proliferation of small firms and new entrants in the renewable energy sector has led to the explanation of returns by extreme values of risk. Full article

This paper introduces a new methodology to estimate time-varying alphas and betas in conditional factor models, which allows substantial flexibility in a time-varying framework. To circumvent problems associated with the previous approaches, we introduce a Bayesian time-varying parameter model where innovations of the state equation have a spike-and-slab mixture distribution. The mixture distribution specifies two states with a specific probability. In the first state, the innovation variance is set close to zero with a certain probability and parameters stay relatively constant. In the second state, the innovation variance is large and the change in parameters is normally distributed with mean zero and a given variance. The latent state is specified with a threshold that governs the state change. We allow a separate threshold for each parameter; thus, the parameters may shift in an unsynchronized manner such that the model moves from one state to another when the change in the parameter exceeds the threshold and vice versa. This approach offers great flexibility and nests a plethora of other time-varying model specifications, allowing us to assess whether the betas of conditional factor models evolve gradually over time or display infrequent, but large, shifts. We apply the proposed methodology to industry portfolios within a five-factor model setting and show that the threshold Capital Asset Pricing Model (CAPM) provides robust beta estimates coupled with smaller pricing errors compared to the alternative approaches. The results have significant implications for the implementation of smart beta strategies that rely heavily on the accuracy and stability of factor betas and yields. Full article

This paper aims to develop some static and conditional (dynamic) models to predict portfolio returns in the Borsa Istanbul (BIST) that are calibrated to combine the capital asset-pricing model (CAPM) and corporate governance quality. In our conditional model proposals, both the traditional CAPM (beta) coefficient and model constant are allowed to vary on a binary basis with any degradation or improvement in the country’s international trade competitiveness, and meanwhile a new variable is added to the models to represent the portfolio’s sensitivity to excess returns on the governance portfolio (BIST Governance) over the market. Some robust and Bayesian linear models have been derived using the monthly capital gains between December 2009 and December 2019 of four leading index portfolios. A crude measure is then introduced that we think can be used in assessing governance quality of portfolios. This is called governance quality score (GQS). Our robust regression findings suggest both superiority of conditional models assuming varying beta coefficients over static model proposals and significant impact of corporate governance quality on portfolio returns. The Bayesian model proposals, however, exhibited robust findings that favor the static model with fixed beta estimates and were lacking in supporting significance of corporate governance quality. Full article

Using stock return data for the Japanese equity market, for the period from July 1983 to June 2018, we analyze the effect of major nuclear disasters worldwide on Japanese discount rates. For that purpose, we compare the performance of the capital asset pricing model (CAPM) conditional on the event of nuclear disasters with that of the classic CAPM and the Fama–French three- and five-factor models. In order to control for nuclear disasters, we use an instrument that allows us to parameterize the linear stochastic discount factor of the conditional CAPM and transform the classic CAPM into a three-factor model. In this regard, the use of nuclear disasters as an explanatory variable for the cross-sectional behavior of stock returns is a novel contribution of this research. Our results suggest that nuclear disasters account for a large fraction of the variation of stock returns, allowing the CAPM to perform similarly to the Fama–French three- and five-factor models. Furthermore, our results show that, in general, nuclear disasters are positively related to the expected returns of a large number of assets under study. Our results have important implications for the task of estimating the cost of equity and constitute a step forward in understanding the relationship between equity risk premiums and nuclear disasters. Full article

The cornerstone of the capital asset pricing model (CAPM) lies with its beta. The question of whether or not beta is dead has attracted great attention from academics and practitioners in the last 50 years or so, and the debate is still ongoing. Many empirical studies have been conducted to test the validity of beta within the framework of CAPM. However, it is a claim of this paper that beta at the industry level has been largely ignored in the current literature. This study is conducted to examine if beta, proxied for a systematic risk, should be considered valid in the application of the CAPM at the industry level for Australia using daily data on 2200 stocks listed on the Australian Securities Exchange from January 2007 to 31 December 2016. Various portfolio formations are utilized in this paper. General economic conditions such as interest rate, inflation, and GDP are examples of systematic risk. Findings from this study indicate that the selection of portfolio construction, estimation technique, and news about economic conditions significantly affects the view whether or not beta should be considered as a valid measure of systematic risk. Full article

The conditional capital asset pricing model (CAPM) theory postulates that the systematic risk ( $\beta$ ) of an asset or portfolio varies over time. Several dynamics are thus given to systematic risk in the literature. This article looks for the dynamic that seems to best explain the returns of the assets of the Regional Stock Exchange of West Africa (BRVM) by comparing two dynamics: one by the Kalman filter (assuming that the $\beta$ follow a random walk) and the other by the Markov switching (MS) model (assuming that $\beta$ varies according to regimes) for four portfolios of the BRVM. Having found a link between the beta of the market portfolio and the size criterion (measured by capitalization), the two previous models were re-estimated with the addition of the SMB (Small Minus Big) variable. The results show according to the RMSE criterion that the estimation by the Kalman filter fits better than MS, which suggests that investors cannot anticipate systematic risk because of its high volatility. Full article

In this work, a Capital Asset Pricing Model (CAPM) with time-varying betas is considered. These betas evolve over time, conditional on financial and non-financial variables. Indeed, the model proposed by Adrian and Franzoni (2009) is adapted to assess the behavior of some selected Brazilian equities. For each equity, several models are fitted, and the best model is chosen based on goodness-of-fit tests and parameters significance. Finally, using the selected dynamic models, VaR (Value-at-Risk) measures are calculated. We can conclude that CAPM with time-varying betas provide less conservative VaR measures than those based on CAPM with static betas or historical VaR. Full article

The study adds an empirical outlook on the predicting power of using data from the future to predict future returns. The crux of the traditional Capital Asset Pricing Model (CAPM) methodology is using historical data in the calculation of the beta coefficient. This study instead uses a battery of Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models, of differing lag and parameter terms, to forecast the variance of the market used in the denominator of the beta formula. The covariance of the portfolio and market returns are assumed to remain constant in the time-varying beta calculations. The data spans from 3 January 2005 to 29 December 2014. One ten-year, two five-year, and three three-year sample periods were used, for robustness, with ten different portfolios. Out of sample forecasts, mean absolute error (MAE) and mean squared forecast error (MSE) were used to compare the forecasting ability of the ex-ante GARCH models, Artificial Neural Network, and the standard market ex-post model. Find that the time-varying MGARCH and SGARCH beta performed better with out-of-sample testing than the other ex-ante models. Although the simplest approach, constant ex-post beta, performed as well or better within this empirical study. Full article