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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.

The purpose of the study is to examine the dynamics in the troika of asset pricing, volatility, and the business cycle in the US and Japan.

The study uses a six-factor asset pricing model to derive the realized volatility measure for the GARCH-type models.

The comprehensive empirical investigation led to the following conclusion. First, the results infer that the market portfolio and human capital are the primary discounting factors in asset return predictability during various phases of the subprime crisis phenomenon for the US and Japan. Second, the empirical estimates neither show any significant impact of past conditional volatility on the current conditional volatility nor any significant effect of subprime crisis episodes on the current conditional volatility in the US and Japan. Third, there is no asymmetric volatility effect during the subprime crisis phenomenon in the US and Japan except the asymmetric volatility effect during the post-subprime crisis period in the US and full period in Japan. Fourth, the volatility persistence is relatively higher during the subprime crisis period in the US, whereas during the subprime crisis transition period in Japan than the rest of the phases of the subprime crisis phenomenon.

The study argues that the empirical investigations that employed the autoregressive method to derive the realized volatility measure for the parameter estimation of GARCH-type models may result in incurring spurious estimates. Further, the empirical results of the study show that using the six-factor asset pricing model in an intertemporal framework to derive the realized volatility measure yields better estimation results while estimating the parameters of GARCH-type models.

The purpose of this paper is to decompose and measure the microstructure of property investment returns for Tokyo’s residential property markets in as much detail as possible in comparison with office market.

Using enterprise value data for property investment trust companies composed of share prices available on capital markets, this study proposed a method of estimating property investment returns corresponding to changes in capital markets, and clarified the distortion in capitalization rate that are formed based on property appraisal prices.

The results for residential property showed that as building floor space increased, income and price increased while the discount rate decreased. In particular, a higher return could be obtained from office property than residential property by investing in larger-scale properties. Building age lowered asset price and income for both residential and office property, especially for residential property.

In Japan, investors believe that investment returns are high for properties close to the city centre, relatively new properties and those with large design or floor space. Therefore, this study first measured how asset prices, income and asset price–income ratios that comprise property investment returns change based on differences in these property characteristics. Second, the reliability/distortion of information that can be observed on the property investment market was measured. Furthermore, there was a significant divergence between discount rates and risk premiums formed by asset or space markets versus capital markets.

The differences of discount rate and risk premium formed by asset markets versus capital markets indicate that appraisal prices have biases. Thus, when it comes to property investment decisions, it is essential to make active use not just of property investment returns based on appraisal prices formed by asset markets but also information formed by capital markets.

A greater difference was generated in a shrinking market, suggesting that analysing property returns estimated on asset market information alone could lead to erroneous investment decisions.

This research is the first to use the enterprise value data from real estate investment trust companies composed of share prices available on capital markets for calculating discount rate and risk premium in property market.

This essay provides a non‐technical account of the development of thinking about the ways in which financial markets work. The account is organized by distinguishing between the “financial approach” and the “monetary approach” to the study of financial markets. The financial approach emphasizes the importance of arbitrage in determining financial asset prices. The monetary approach utilizes the more traditional tools of supply and demand, and places greater emphasis on the role of market imperfections. The essay evaluates the contribution of each approach to improving our understanding of financial markets. It concludes that the central problem in financial market research remains that of providing a satisfactory explanation of the determination of asset prices. In the emerging regime of liberalized, competitive financial markets both the financial approach and the monetary approach have a distinctive contribution to make in understanding how these markets work. This paper is based on research funded by the Economic and Social Research Council under grant No. B0023‐2151.

Understanding the pricing of real estate equities is a central objective of real estate research. This paper aims to investigate the impact of liquidity on European real estate equity returns, after accounting for well-documented systematic risk factors.

Based on risk factors derived from general equity data, the authors extend the Fama-French time-series regression approach by a liquidity factor, using a pan-European sample of 272 real estate equities.

The empirical results indicate that liquidity is a significant pricing factor in real estate stock returns, even after controlling for market, size and book-to-market factors. In addition, the authors detect that real estate stock returns load predominantly positively on the liquidity risk factor, suggesting that real estate equities tend to behave like illiquid common equities. These findings are underpinned by a series of robustness checks. Running a comparative analysis with alternative factor models, the authors further demonstrate that the liquidity-augmented asset-pricing model is most appropriate for explaining European real estate stock returns.

The inclusion of sentiment and downside risk factors could provide further insights into real estate asset pricing in European capital markets.

This is the first study to examine the role of liquidity as a systematic risk factor in a pan-European setting.

Financial asset return series usually exhibit nonnormal characteristics such as high peaks, heavy tails and asymmetry. Traditional risk measures like standard deviation or variance are inadequate for nonnormal distributions. Value at Risk (VaR) is consistent with people's psychological perception of risk. The asymmetric Laplace distribution (ALD) captures the heavy-tailed and biased features of the distribution. VaR is therefore used as a risk measure to explore the problem of VaR-based asset pricing. Assuming returns obey ALD, the study explores the impact of high peaks, heavy tails and asymmetric features of financial asset return data on asset pricing.

A VaR-based capital asset pricing model (CAPM) was constructed under the ALD that follows the logic of the classical CAPM and derive the corresponding VaR-β coefficients under ALD.

ALD-based VaR exhibits a minor tail risk than VaR under normal distribution as the mean increases. The theoretical derivation yields a more complex capital asset pricing formula involving β coefficients compared to the traditional CAPM.The empirical analysis shows that the CAPM under ALD can reflect the β-return relationship, and the results are robust. Finally, comparing the two CAPMs reveals that the β coefficients derived in this paper are smaller than those in the traditional CAPM in 69–80% of cases.

The paper uses VaR as a risk measure for financial time series data following ALD to explore asset pricing problems. The findings complement existing literature on the effects of high peaks, heavy tails and asymmetry on asset pricing, providing valuable insights for investors, policymakers and regulators.

The accounting literature has traditionally focused on firm-level studies to examine the capital market implications of earnings and other accounting variables. We first develop the arguments for studying capital market implications at the aggregate level as well. A central issue is that diversification makes equity investors at least partially and potentially almost completely immune to several firm-level properties of earnings by holding diversified portfolios. Diversification is particularly important when assessing the welfare consequences of random errors in accounting measurement (imperfect accruals) and, to the extent it is independent across firms, of deliberate manipulation (earnings management). Consequently, some firm-level metrics of association, timeliness, value relevance, conservatism and other earnings properties do not map easily into investor welfare. Similarly, earnings-related risk manifests itself to equity investors largely through systematic earnings risk (covariation with aggregate earnings and/or other macroeconomic indicators). We conclude that the design and evaluation of financial reporting must adopt at least in part an aggregate perspective. We then summarize the literature in accounting, economics and finance on aggregate earnings and stock prices. Our review highlights the importance of studying earnings at the aggregate level.

This paper aims to examine the impact of crude oil prices on Australian industry stock returns. With rising energy prices, it is important to consider oil as a pricing factor in asset pricing models.

Multifactor static and dynamic models consider crude oil and other macroeconomic factors as pricing factors in industry excess returns from January 1980 to August 2006. The macroeconomic factors comprise the market portfolio, oil prices, exchange rates and the term premium. The industries consist of banking, diversified financials, energy, insurance, media, property trusts, materials, retailing and transportation.

Oil prices are an important determinant of returns in the banking, energy, materials, retailing and transportation industries. The findings also suggest oil price movements are persistent. Nonetheless, the proportion of variation in excess returns explained by the contemporaneous and lagged oil prices appears to have declined during the sample period.

Macroeconomic factors are important for multifactor asset pricing at the industry level. Apart from oil prices, the market portfolio is a significant pricing factor in all industry excess returns. Exchange rates are also an influential factor for excess returns in the banking and diversified financials industries, and the term premium as a proxy for future real activity is a priced factor in the energy, insurance and retailing industries.

While past studies have provided some evidence that oil prices constitute a source of systematic asset price risk and that exposure varies across industries, no recent work is known in the Australian context.

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.

This paper aims to investigate whether the use of conditional and unconditional Fama and French (1993) three-factor and Carhart (1997) four-factor asset pricing models (APMs) captures the role of asset pricing anomalies in the context of emerging stock market like India.

The first step time series regression approach has been used to drive the risk-adjusted returns of individual securities. For examining the predictability of firm characteristics or asset pricing anomalies on the risk-adjusted returns of individual securities, the panel data estimation technique has been used.

Fama and French (1993) three-factor and Carhart (1997) four-factor model in their unconditional specifications capture the impact of book-to-market price and liquidity effects completely. When alternative APMs in their conditional specifications are tested, the importance of medium- and long-term momentum effects has been captured to a greater extent. The size, market leverage and short-term momentum effects still persist even in the case of alternative unconditional and conditional APMs.

The empirical analysis does not extend for different market scenarios like high and low volatile market or good and bad macroeconomic environment. Because of the constraint of data availability, the authors could not include certain important anomalies like net operating assets, change in gross profit margin, external equity and debt financing and idiosyncratic risk.

Although the active investment approach in stock market shares a common ground of semi-strong form of market efficiency hypothesis which also supports the presence of asset pricing anomalies, less empirical evidence has been explored in this regard to support or repute such belief of practitioners. Our empirical findings make an attempt in this regard to suggest certain anomaly-based trading strategy that can be followed for active portfolio management.

From an emerging market perspective, this paper provides out-of-sample empirical evidence toward the use of conditional Fama and French three-factor and Carhart four-factor APMs for the complete explanation of market anomalies. This approach retains its importance with respect to the comprehensiveness of analysis considering alternative APMs for capturing unique effects of market anomalies.