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The authors consider a variety of cryptocurrency and equity risk factors as potential forces that drive cryptocurrency returns and carry risk premiums. In a cross-section of 2,000 biggest cryptocurrencies during 2014–2020, only downside market risk, cryptocurrency size and cryptocurrency policy uncertainty factors are systematically priced with significant premiums. Cryptocurrencies, which have greater exposures to these factors, yield higher returns subsequently. Equity market risk, particularly equity downside market risk, appears to be more important than cryptocurrency market risk, suggesting greater linkages between cryptocurrency and equity markets than we used to think. Global and the US equity factors are more relevant for the cryptocurrency market than local factors from other markets. However, there is no evidence that exposure to momentum, volatility and Fama–French factors is compensated by higher returns.

Purpose – The purpose of this chapter is to examine whether or not the Capital Asset Pricing Model (CAPM) reasonably describes the return generating process on the Ghanaian Stock Exchange using monthly return data of 19 individual companies listed on the Exchange during the period January 2000 to December 2009.

Methodology/approach – We follow a methodology similar to Jensen (1968) time series approach. Parameters are estimated using OLS. This study is designed to measure beta risk across different times by following the time series approach. The betas of the individual securities are estimated using time series data of the excess return version of the CAPM.

Findings – Our test results show that although market beta contributes to the variation in equity returns in Ghana, its contribution is not as significant as predicted by the CAPM, and in some cases very weak. Our results also reject the strictest form of the Sharpe–Lintner CAPM, but we found positive linear relationship between equity risk premium and market beta. Instead, our evidence uphold the Jensen (1968) and Jensen, Black, and Scholes (1972) versions of the CAPM.

Research limitations/implications – This study is limited to the single-factor CAPM. Future studies will extend the test to include both size and BE/ME fundamentals and factors relating to P/E ratio, momentum and liquidity.

Practical implications – Our results will make corporate managers to be cautious when using CAPM as a basis to determine cost of equity for investment appraisal purposes, and fund managers when evaluating asset and portfolio performance.

Originality/value – The CAPM is applied to individual securities instead of portfolios, since the model was developed using information on a single security.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

Investors often have much of their portfolios invested in equities that are exposed to interest rate risk. Hedging underlying exposures are not easy; whereas fixed income investors have duration to immunize bond portfolios from small fluctuations in interest rates. US equity duration estimates from dividend discount models result in long durations – often in excess of 50 years. Based on the UK data, we develop an alternative approach to generate equity duration as a by-product of asset pricing. Our analysis suggests that the equity premium puzzle may comprise an important element in reconciling this approach to equity duration, with traditional DDM alternatives.