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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.

This study aims to document the time varying risk premia for market, size, value and momentum factors for an emerging market using a sophisticated conditional asset pricing model. The focus of this study is Turkish stock market denominated in local currency with its peculiar risk premia.

The authors employ Gagliardini et al.'s (2016) econometric method that uses cross-sectional and time series information simultaneously to infer the path of risk premia from individual stocks.

Using this methodology, the authors assess several conditioning information and conclude that local dividend yield, inflation and exchange rates have the most explanatory power. The authors document the time varying risk premia in Turkey over three decades.

Existing studies on dynamic estimation of risk premia lack a consensus as to which state variables should be included and to what extent they impact the magnitude of the premium. The authors extend the conditioning information set beyond the ones existing in the literature to determine variables that are specifically important for an emerging market.

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.

Daniel and Titman (1997) contend that the Fama‐French three‐factor model’s ability to explain cross‐sectional variation in expected returns is a result of characteristics that firms have in common rather than any risk‐based explanation. The primary aim of the current paper is to provide out‐of‐sample tests of the characteristics versus risk factor argument. The main focus of our tests is to examine the intercept terms in Fama‐French regressions, wherein test portfolios are formed by a three‐way sorting procedure on book‐to‐market, size and factor loadings. Our main test focuses on ‘characteristic‐balanced’ portfolio returns of high minus low factor loading portfolios, for different size and book‐to‐market groups. The Fama‐French model predicts that these regression intercepts should be zero while the characteristics model predicts that they should be negative. Generally, despite the short sample period employed, our findings support a risk‐factor interpretation as opposed to a characteristics interpretation. This is particularly so for the HML loading‐based test portfolios. More specifically, we find that: the majority of test portfolios tend to reveal higher returns for higher loadings (while controlling for book‐to‐market and size characteristics); the majority of the Fama‐French regression intercepts are statistically insignificant; for the characteristic‐balanced portfolios, very few of the Fama‐French regression intercepts are significant.

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.

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.

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.

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.

The purpose of this paper is to examine the relationship between beta and returns in the Athens stock exchange (ASE), taking into account the difference between positive and negative market excess returns' yields.

The data were taken from DataStream database and the sample period consists of 12 years divided into four six‐year periods such that the test periods do not overlap. Regression analysis is applied, using both the traditional (unconditional) test procedure and the conditional approach.

The estimation of return and beta without differentiating positive and negative market excess returns produces a flat unconditional relationship between return and beta. However, when using the conditional capital asset pricing model (CAPM) and cross‐sectional regression analysis, the evidence tends to support the significant positive relationship in up market and a significant negative relationship in down market.

The small number of listed companies in the ASE led to the inclusion of the financial and insurance companies in the sample, and to the formation of a small number of portfolios. The same research methodology could be applied to individual stocks of the ASE and with the exclusion of all financial companies.

The results tend to support the existence of a conditional CAPM relation between risk and realized return trade‐off.

This study aims to investigate the risk-return trade-off in the Indian equity market at both the aggregate equity market level and in the cross-sections of stock return using alternative risk measures.

The study uses weekly and monthly data of 3,085 Bombay Stock Exchange-listed stocks spanning over 20 years from January 2000 to December 2019. The study evaluates the risk-return trade-off at the aggregate equity market level using the value-weighted and the equal-weighted broader portfolios. Eight different risk proxies belonging to the conventional, downside and extreme risk categories are considered to analyse the cross-sectional risk-return relationship.

The results show a positive equity premium on the value-weighted portfolio; however, the equal-weighted portfolio of these stocks shows an average return lower than the return on the 91-day Treasury Bills. The inverted size premium mainly causes this anomaly in the Indian equity market as the small stocks have lower returns than big stocks. The study presents a strong negative risk-return relationship across different risk proxies. However, under the subsample of more liquid stocks, the low-risk anomaly regarding other risk proxies becomes moderate except the beta-anomaly. This anomalous relationship seems to be caused by small and less liquid stocks having low institutional ownership and higher short-selling constraints.

The findings have important implications for investors, managers and practitioners. Investors can incorporate the effects of different highlighted anomalies in their investment strategies to fetch higher returns. Managers can also use these findings in their capital budgeting decisions, resource allocations and other diverse range of direct and indirect decisions, particularly in emerging markets such as India. The findings provide insights to practitioners while valuing the firms.

The study is among the earlier attempts to examine the risk-return trade-off in an emerging equity market at both the aggregate equity market level and in the cross-sections of stock returns using alternative measures of risk and expected returns.

This paper aims to examine the relationship between the conditional variance of the factors from the Fama–French three‐factor model and macroeconomic risk, where macroeconomic risk is proxied by the conditional variance for a default risk premium and real gross domestic product (GDP) growth.

A generalised autoregressive conditional heteroscedastic model is used to generate the conditional volatilities and bivariate Granger causality tests are used to examine the empirical relationship between the risk measures.

Past values of the conditional variance for a default risk premium have information that is precedent to the conditional volatility for value premium and the small stock risk premium, and the conditional variance for the market risk premium has information about the future volatility of macroeconomic risk, as proxied by the conditional variance for GDP growth.

The implications are that conditional volatility associated with default is related to current and future volatility in value premium; however, volatility associated with the market risk premium appears to be a predictor of future macroeconomic risk. A caveat is that the results are dependent on the proxies used for macroeconomic risk and more refined measures of macroeconomic risk may yield different results.

This paper suggests that examination of the relationship between the volatility of macroeconomic factors and the explanatory factors in asset‐pricing models will help to further understanding of the relationship between risk and expected return.

This paper focuses directly on the links between risk associated with the Fama–French factors and macroeconomic risk. This added knowledge is beneficial to practitioners and academics whose interest lies in asset price modelling.

Many studies have shown that from a theoretical and empirical point of view, downside risk-based measures of risk are better than the traditional ones. Despite academic appeal and practical implications, downside risk has not been thoroughly examined in markets outside developed country markets. Using downside beta as a measure of downside risk, this study examines the relationship between downside beta and stock returns in Indian equity market, an emerging market with unique investor, asset and market characteristics.

This is an empirical study done by using ranked portfolio return analysis and regression analysis methodologies.

The study results show that downside risk, as measured by downside beta, is distinctly priced in the Indian equity market. There is a direct positive relationship between downside beta and contemporaneous realized returns, indicating a premium for downside risk. Downside risk carries a higher weightage than upside potential in the aggregate return of the stock portfolios. Downside beta is a better measure of systematic risk than conventional market beta and downside coskewness.

The empirical results support the adoption of downside beta in practice and provide a case for replacing traditional beta with downside beta in asset pricing applications, trading and investment strategies, and capital allocation decision-making.

This is one of the first in-depth studies examining downside beta in Indian equity markets using a broad sample of individual stock returns covering a wide time range of 22 years. To the best of our knowledge, this study is the first one to compare downside beta and downside coskewness using individual stock data from the Indian equity market.

The purpose of this paper is to investigate the pricing of European real estate equities. The study examines the main drivers of real estate equity returns and determines whether loadings on systematic risk factors – the excess market return, small minus big (SMB), HIGH minus low (HML) – can explain cross‐sectional return differences in unconditional as well as in conditional asset pricing tests.

The paper draws upon time‐series regressions to investigate determinants of real estate equity returns. Rolling Fama‐French regressions are applied to estimate time‐varying loadings on systematic risk factors. Unconditional as well as conditional monthly Fama‐MacBeth regressions are employed to explain cross‐sectional return variations.

Systematic risk factors are important drivers of European real estate equity returns. Returns are positively related to the excess market return and to a value factor. A size factor impacts predominantly negatively on real estate returns. The results indicate increasing market integration after the introduction of the Euro. Loadings on systematic risk factors have weak explanatory power in unconditional cross‐section regressions but can explain returns in a conditional framework. Beta – and to a lesser extent the loading on HML – is positively related to returns in up‐markets and negatively in down markets. Equities which load positively on SMB outperform in down markets.

The implementation of a liquidity or a momentum factor could provide further evidence on the pricing of European real estate equities.

The findings could help investors to manage the risk exposure more effectively. Investors should furthermore be able to estimate their cost of equity more precisely and might better be able to pick stocks for time varying investment strategies.

This is the first paper to examine the pricing of real estate equity returns in a pan‐European setting.