Exchange Rates and the Conversion of Currency‐Specific Risk Premia

How do the risk factors that drive asset prices influence exchange rates? Are the parameters of asset price processes relevant for specifying exchange rate processes? Most international asset pricing models focus on the analysis of asset returns given exchange rate processes. Little work has been done on the analysis of exchange rates dependent on asset returns. This paper uses an international stochastic discount factor (SDF) framework to analyse the interplay between asset prices and exchange rates. So far, this approach has only been implemented in international term structure models. We find that exchange rates serve to convert currency‐specific discount factors and currency‐specific prices of risk – a result linked to the international arbitrage pricing theory (IAPT). Our empirical investigation of exchange rates and stock markets of four countries presents evidence for the conversion of currency‐specific risk premia by exchange rates.     

Changes in Expected Security Returns,Risk, and the Level of Interest Rates

Regressions of security returns on treasury bill rates provide insight about the behavior of risk in rational asset pricing models. The information in one-month bill rates implies time variation in the conditional covariances of portfolios of stocks and fixed-income securities with benchmark pricing variables, over extended samples and within five-year subperiods. There is evidence of changes in conditional “betas” associated with interest rates. Consumption and stock market data are examined as proxies for marginal utility, in a general framework for asset pricing with time-varying conditional covariances.     

Options Trading and the CAPM

This article studies equilibrium asset pricing when agents facenonnegative wealth constraints. In the presence of these constraintsit is shown that options on the market portfolio are nonredundantsecurities and the economy's pricing kernel is a function ofboth the market portfolio and the nonredundant options. Thisimplies that the options should be useful for explaining riskyasset returns. To test the theory, a model is derived in whichthe expected excess return on any risky asset is linearly related(via a collection of betas) to the expected excess return onthe market portfolio and to the expected excess returns on thenonredundant options. The empirical results indicate that thereturns on traded index options are relevant for explainingthe returns on risky asset portfolios.     

Common asset pricing factors in volatilities and returns in futures markets

Factor-based asset pricing models have been used to explain the common predictable variation in excess asset returns. This paper combines means with volatilities of returns in several futures markets to explain their common predictable variation. Using a latent variables methodology, tests do not reject a single factor model with a common time-varying factor loading. The single common factor accounts for up to 53% of the predictable variation in the volatilities and up to 14% of the predictable variation in the means. S&P500 futures volatility predicted by the factor model is highly correlated with volatility implied in S&P500 futures options. But both the factor and implied volatilities are significant in predicting future volatility. In derivatives pricing, both implied volatility from options and factors extracted from asset pricing models should be employed.     

Liquidity and asset pricing: Evidence from the Hong Kong stock market

This study investigates the role of liquidity in pricing stock returns in the Hong Kong stock market. Our results show that liquidity is an important factor for pricing returns in Hong Kong after taking well-documented asset pricing factors into consideration. The results are robust to adding portfolio residuals and higher moment factor in the factor models. The results are also robust to seasonality, and conditional-market tests. We also compare alternative factor models and find that the liquidity four-factor model (market excess return, size, book-to-market ratio, and liquidity) is the best model to explain stock returns in the Hong Kong stock market, while the momentum factor is not found to be priced.     

Testing conditional asset pricing models: An emerging market perspective

The CAPM as the benchmark asset pricing model generally performs poorly in both developed and emerging markets. We investigate whether allowing the model parameters to vary improves the performance of the CAPM and the Fama–French model. Conditional asset pricing models scaled by conditioning variables such as Trading Volume and Dividend Yield generally result in small pricing errors. However, a graphical analysis reveals that the predictions of conditional models are generally upward biased. We demonstrate that the bias in prediction may be the consequence of ignoring frequent large variation in asset returns caused by volatile institutional, political and macroeconomic conditions. This is characterised by excess kurtosis. An unconditional Fama–French model augmented with a cubic market factor performs the best among some competing models when local risk factors are employed. Moreover, the conditional models with global risk factors scaled by global conditioning variables perform better than the unconditional models with global risk factors.     

The evolution of capital asset pricing models

The capital asset pricing models (CAPM) has been the benchmark of asset pricing models and has been used to calculate asset returns and the cost of capital for more than four decades. Many researchers have tried to relax the original assumptions and generalize the static CAPM. We survey the important alternative theoretical models of capital asset pricing and provide a complete review of the evolution of asset pricing models. We also discuss the interrelationships among these models and suggest several possible directions for future research. Our results might be used as a guideline for future theoretical and empirical research in capital asset pricing.     

The Variation of Economic Risk Premiums in Real Estate Returns

We examine the predictable components of returns on stocks, bonds, and real estate investment trusts (REITs). We employ a multiple-beta asset pricing model and find that there are varying degrees of predictability among stocks, bonds, and REITs. Furthermore, we find that most of the predictability of returns is associated with the economic variables employed in the asset pricing model. The stock market risk premium is highly important in capturing the predictable variation in stock portfolios, and the bond market risk premiums (term and risk structure of interest rates) are important in capturing the predictable variation in bond portfolios. For REITs, however, both the stock and bond market risk premiums capture the predictable variation in returns. REITs have comparable return predictability to stock portfolios. We conclude that there is an important economic risk premium for REITs that are not captured by traditional multiple-beta asset pricing models.     

Valuation Risk and Asset Pricing

Standard representative‐agent models fail to account for the weak correlation between stock returns and measurable fundamentals, such as consumption and output growth. This failing, which underlies virtually all modern asset pricing puzzles, arises because these models load all uncertainty onto the supply side of the economy. We propose a simple theory of asset pricing in which demand shocks play a central role. These shocks give rise to valuation risk that allows the model to account for key asset pricing moments, such as the equity premium, the bond term premium, and the weak correlation between stock returns and fundamentals.     

Corporate sustainability in asset pricing models and mutual funds performance measurement

This study explores whether corporate sustainability is a relevant factor in multifactor asset pricing models. It contributes to the literature on asset pricing, as well as to the literature that examines how sustainability impacts capital markets, by constructing a new factor that captures differences in the returns of sustainable and non-sustainable firms. Specifically, it examines whether an additional sustainability factor has explanatory power in asset pricing models that include size, book-to-market equity, and momentum factors. This research has practical implications for the performance measurement of portfolios and mutual funds that are managed in accordance with sustainability criteria in that it disentangles general stock-picking skills from the differences in returns between sustainable and non-sustainable stocks.     

Data-snooping biases in tests of financial asset pricing models

Tests of financial asset pricing models may yield misleadinginferences when properties of the data are used to constructthe test statistics. In particular, such tests are often basedon returns to portfolios of common stock, where portfolios areconstructed by sorting on some empirically motivated characteristicof the securities such as market value of equity. Analyticalcalculations, Monte Carlo simulations, and two empirical examplesshow that the effects of this type of data snooping can be substantial.     

Limits to mutual funds' ability to rely on mean/variance optimization

Our evidence suggests that estimation error in the required statistics is an important factor inhibiting investors' ability to rely on mean/variance analysis. We compare the returns reported by mutual funds to the returns obtained from a mean/variance optimized portfolio of fund holdings. The results suggest that funds tend to outperform the optimized portfolio out-of-sample (when means/variances/covariances are unknown), but under-perform in-sample (when the required statistics in the optimization are known). Therefore, a popular assumption in asset pricing models that investors rely on a basic mean/variance analysis with known underlying statistics is likely to be grossly violated in the case of mutual funds.     

The Valuation of Assets under Moral Hazard

The design of managerial incentive contracts is examined in a setting in which economic agents are risk averse, and the actions of managers can affect asset returns which contain both systematic and idiosyncratic risks. It is shown that in the absence of moral hazard, owners of assets will insure managers against idiosyncratic risks, but with moral hazard, contracts will depend on both systematic and idiosyncratic risks. The traditional recommendation of asset pricing models, namely, to focus only on systematic risks, is thus proved to be valid only when there is no moral hazard. The major empirically testable predictions of the model are (1) managerial incentive contracts will generally depend on systematic as well as idiosyncratic risks, (2) idiosyncratic risks will generally be important in investment decisions, (3) the managers of firms with relatively high levels of idiosyncratic risks will have compensations that are less dependent on their firms' excess returns, and (4) the compensations of managers of larger firms will be relatively more dependent on the excess returns of their firms.     

Modeling Asset Premiums and the Riskfree Rate in General Equilibrium CCAPM

We elaborate on the consumption capital asset pricing model (CCAPM) to reveal a set of underlying forces that determine asset returns. We use generalized preferences, allow for labor-leisure choice, a broad asset portfolio, and holding international claims. A calibration of the model with US data learns that excess stock and bond returns can be replicated. At the same time, however, the riskfree interest rate generally appears to be mispriced, consistent with Weil (1989). Additional results show that in general two optimal values of the intertemporal substitution parameter correspond with a specified coefficient of risk aversion. Tests that assess the dynamic properties of the model yield mixed results, but are most favorable when home bias is allowed.     

Option pricing under regime switching

Abstract

This paper develops a family of option pricing models when the underlying stock price dynamic is modelled by a regime switching process in which prices remain in one volatility regime for a random amount of time before switching over into a new regime. Our family includes the regime switching models of Hamilton (Hamilton J 1989 Econometrica 57 357–84), in which volatility influences returns. In addition, our models allow for feedback effects from returns to volatilities. Our family also includes GARCH option models as a special limiting case. Our models are more general than GARCH models in that our variance updating schemes do not only depend on levels of volatility and asset innovations, but also allow for a second factor that is orthogonal to asset innovations. The underlying processes in our family capture the asymmetric response of volatility to good and bad news and thus permit negative (or positive) correlation between returns and volatility. We provide the theory for pricing options under such processes, present an analytical solution for the special case where returns provide no feedback to volatility levels, and develop an efficient algorithm for the computation of American option prices for the general case.     

Investment shocks and the commodity basis spread

I identify a “slope” factor in the cross section of commodity futures returns: high-basis commodity futures have higher loadings on this factor than low-basis commodity futures. Combined with a level factor (an index of commodity futures), this slope factor explains most of the average excess returns of commodity futures portfolios sorted by basis. More importantly, I find that this factor is significantly correlated with investment shocks, which represent the technological progress in producing new capital. I investigate a competitive dynamic equilibrium model of commodity production to endogenize this correlation. The model reproduces the cross-sectional futures returns and many asset pricing tests.     

Where do betas come from? Asset price dynamics and the sources of systematic risk

In this article we break asset's betas with common factors intocomponents attributable to news about future cash flows, realinterest rates, and excess returns. To achieve this decomposition,we use a vector autoregressive time-series model and an approximatelog-linear present value relation. The betas of industry andsize portfolios with the market are largely attributed to changingexpected returns. Betas with inflation and industrial productionreflect opposing cash flow and expected return effects. We alsoshow how asset pricing theory restricts the expected excessreturn components of betas.     

Some Tests of the Risk-Return Relationship Using Alternative Asset Pricing Models and Observed Expected Returns

This study examines the performance of three asset pricing models: the CAPM, the APT and the UAPT using observed expected returns from a three-phase dividend discount model with Value Line analyst estimates of future company-level earnings, dividends and growth rates. Our study is the first we know of to test the three major asset pricing models using observed expected returns. Our results are similar to prior research using ex post (realized) returns in that we find that the UAPT using macroeconomic factors is the best performing model, followed by the APT and the CAPM. However, our results also suggest that the importance of macroeconomic factors is much greater to expected returns than to realized returns, and the corresponding R2 values for models using expected returns are much higher than for models using realized returns. Combining our results for the UAPT with those of Marston and Harris (1993) for the CAPM suggests that these models are more successful in tests using observed expected returns than in tests using realized returns as proxies for expected returns. Unit root tests suggest that monthly observed expected returns follow the classic random walk without drift model while monthly realized returns do not.