market risk premium
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<p>This paper applies the Ibbotson and Sinquefield (1976) method and the Lally (2002) method to New Zealand data over the period 1960-2005 in order to estimate the market risk premium (MRP) in two versions of the capital asset pricing model (CAPM). With respect to the standard CAPM, the resulting Ibbotson estimate of the MRP for New Zealand was 6.11%. The resulting Lally estimate of the MRP ranged from 5.52% (in 1970) to 18.40% (in 1990), with an average of 7.95%, and was 6.40% for 2005. With respect to the simplified Brennan-Lally CAPM, the resulting Ibbotson estimate of the MRP for New Zealand was 8.49%. The resulting Lally estimate of the MRP ranged from 7.91% (in 1970) to 20.79% (in 1990), with an average of 10.33%, and was 8.78% for 2005. The Lally and the Ibbotson estimates of the MRP are similar in general. However, when market leverage is unusually high or low, they diverge significantly. In future, practitioners may need to choose between the estimates from the two methods when market leverage goes beyond the normal level.</p>

<p>This paper applies the Ibbotson and Sinquefield (1976) method and the Lally (2002) method to New Zealand data over the period 1960-2005 in order to estimate the market risk premium (MRP) in two versions of the capital asset pricing model (CAPM). With respect to the standard CAPM, the resulting Ibbotson estimate of the MRP for New Zealand was 6.11%. The resulting Lally estimate of the MRP ranged from 5.52% (in 1970) to 18.40% (in 1990), with an average of 7.95%, and was 6.40% for 2005. With respect to the simplified Brennan-Lally CAPM, the resulting Ibbotson estimate of the MRP for New Zealand was 8.49%. The resulting Lally estimate of the MRP ranged from 7.91% (in 1970) to 20.79% (in 1990), with an average of 10.33%, and was 8.78% for 2005. The Lally and the Ibbotson estimates of the MRP are similar in general. However, when market leverage is unusually high or low, they diverge significantly. In future, practitioners may need to choose between the estimates from the two methods when market leverage goes beyond the normal level.</p>

PurposeThe purpose of this article is to propose a detailed methodology to estimate, model and incorporate the non-constant volatility onto a numerical tree scheme, to evaluate a real option, using a quadrinomial multiplicative recombination.Design/methodology/approachThis article uses the multiplicative quadrinomial tree numerical method with non-constant volatility, based on stochastic differential equations of the GARCH-diffusion type to value real options when the volatility is stochastic.FindingsFindings showed that in the proposed method with volatility tends to zero, the multiplicative binomial traditional method is a particular case, and results are comparable between these methodologies, as well as to the exact solution offered by the Black–Scholes model.Originality/valueThe originality of this paper lies in try to model the implicit (conditional) market volatility to assess, based on that, a real option using a quadrinomial tree, including into this valuation the stochastic volatility of the underlying asset. The main contribution is the formal derivation of a risk-neutral valuation as well as the market risk premium associated with volatility, verifying this condition via numerical test on simulated and real data, showing that our proposal is consistent with Black and Scholes formula and multiplicative binomial trees method.

This study investigates the impact of return distribution such as skewness and kurtosis on lagged market risk premium to risk premium in Indonesia capital market during COVID-19 pandemic. Data are monthly, from january to December 2020, and 674 firms. Panel data predictive regression is used The method  For this study, I first looked for market risk premium and risk premium desripitives. Second, I used monthly panel data predictive regression from lagged market risk premium and risk premium in 2020. Third, I incorporate skewness and kurtosis simultaneously. Fourth, I exclude kurtosis or skewness in previous model. The results are market risk premium and risk premium having negative return. Risk premium has lower returns than market risk premium. The beta lagged market risk premium is significant to risk premium. The skewness and kurtosis market risk premium do not signify to risk premium together but significant separately. I can clonclude that the movement market risk premim and risk premium during COVID-19 pandemic are average negative. Beta lagged market risk premium can explain the future monthly risk premium. Contrary skewness and kurtosis, those can not be run together. When the model used to beta lagged market risk premium and skewness, partially the skewness was significant and the direction was positive. However, only beta lagged market risk premium and kurtosis were staying negative to the previous model. Incorporating lagged assumptive distribution only explain the risk premium under 1 % about 0.24%.

This study provides new insights to predict the excess return of a security. As if factor premia are getting influenced by the sentiments that means sentiments are ultimately affecting the excess return of a security. To meet the objective, a composite index developed by Baker and Wurgler is used as sentiment proxy. Monthly data are used from July 1965 to September 2015 in U.S. context. Granger casualty, Vector Autoregression (VAR), and Fama–Macbeth regression are applied to get the results. Results show that investor sentiments significantly drive the Fama factors’ premia: size premium and profitability premium. Sentiments also contain some information to explain the investment premia but fail to explain the market risk premium and value premium. Furthermore, results suggest that sentiments increase the explanatory power of model measured by R square. In short, this study suggests that investor sentiments play a role in explaining the Fama–French five-factor premia.

Abstract We develop a new global asset pricing model to study how illiquidity interacts with market segmentation and investability constraints in 42 markets. Noninvestable stocks that can only be held by foreign investors earn higher expected returns compared to freely investable stocks due to limited risk sharing and higher illiquidity. In addition to the world market premium, on average, developed and emerging market noninvestables earn an annual unspanned local market risk premium of $1.17\%$ and $9.04\%$, and a liquidity level premium of $1.06\%$ and $2.39\%$, respectively. These results obtained in a conditional setup are robust to the choice of liquidity measure.

PurposeThe purpose of this study is to examine the bi-directional causality between political uncertainty and the market risk premium in the US.Design/methodology/approachI use a theoretical model to motivate signs and then check signs based on a vector autoregression.FindingsI find that political uncertainty has a small positive, delayed effect on the market risk premium. The market risk premium, on the other hand, has a large permanent, negative effect on political uncertainty.Originality/valueThis is the first research paper to consider the bi-directional effects of political uncertainty on the market risk premium and vice versa. It also finds interesting empirical results.