An unobserved component model of asset pricing across financial markets

Our research focuses on multifactor asset pricing models that investigate the importance of economic factors in the pricing of assets beyond the scope of the stock market. We present a Bayesian learning model of asset pricing across financial markets in which unobserved components are estimated using a Kalman filter (KF). Economic factors serve to drive the pricing of risk in the market, and agents update expectations recursively, as new information becomes available. We generally find that the Kalman filter provides superior performance and that economic factors like industrial production and unanticipated inflation provide consistent implications across financial markets.     

Equilibrium asset pricing: with non-Gaussian factors and exponential utilities

We analyse the equilibrium asset pricing implications for an economy with single period return exposures to explicit non-Gaussian systematic factors, that may be both skewed and long-tailed, and Gaussian idiosyncratic components. Investors maximize expected exponential utility and equilibrium factor prices are shown to reflect exponentially tilted prices for non-Gaussian factor risk exposures. It is shown that these prices may be directly estimated from the univariate probability law of the factor exposure, given an estimate of average risk aversion in the economy. In addition, a residual form of the capital asset pricing model continues to hold and prices the idiosyncratic or Gaussian risks. The theory is illustrated on data for the US economy using independent components analysis to identify the factors and the variance gamma model to describe the probability law of the non-Gaussian factors. It is shown that the residual CAPM accounts for no more than 1% of the pricing of risky assets, while the exponentially tilted systematic factor risk exposures account for the bulk of risky asset pricing.     

Estimating the cost of capital with basis assets

Instead of using industry groups or asset pricing models to estimate the cost of capital we propose using risk equivalent classes known as basis assets. A basis asset is constructed by grouping firms together whose returns indicate they share a common risk exposure, which in theory permits a precise and accurate expected return estimate. Thus, knowing to which basis asset a firm belongs, the firm’s cost of capital can be obtained. Empirically, we show that basis assets lead to superior cost of capital estimates when compared with widely used industry groupings. This means we are no longer reliant on asset pricing models or industry groups to estimate the cost of capital of a firm.     

Making trading decisions for financial‐engineered derivatives: a novel ensemble of neural networks using information content

Over the last decades, there has been a growing interest in applying artificial intelligence techniques to solve a spectrum of financial problems. A number of studies have shown promising results in using artificial neural networks (ANNs) to guide investment trading. Given the expanding role of ANNs in financial trading, this paper proposes the use of a hybrid neural network, which consists of two independent ANN architectures, and comparatively evaluates its performance against independent ANNs and econometric models in the trading of a financial‐engineered (synthetic) derivative composed of options on foreign exchange futures. We examine the financial profitability and the market timing ability of the competing neural network models and statistically compare their attributes with those based on linear and nonlinear statistical projections. A random walk model and the option pricing method are also included as benchmarks for comparison. Our empirical investigation finds that, for each of the currencies analysed, trading strategies guided by the proposed dual network are financially profitable and yield a more stable stream of investment returns than the other models. Statistical results strengthen the notion that diffusion of information contents and cross‐validation between the independent components within the dual network are able to reduce bias and extreme decision making over the long run. Moreover, the results are robust with respect to different levels of transaction costs. Copyright © 2009 John Wiley & Sons, Ltd.     

Conditional beta pricing models: A nonparametric approach

We propose a two-stage procedure to estimate conditional beta pricing models that allows for flexibility in the dynamics of asset betas and market prices of risk (MPR). First, conditional betas are estimated nonparametrically for each asset and period using the time-series of previous data. Then, time-varying MPR are estimated from the cross-section of returns and betas. We prove the consistency and asymptotic normality of the estimators. We also perform Monte Carlo simulations for the conditional version of the three-factor model of Fama and French (1993) and show that nonparametrically estimated betas outperform rolling betas under different specifications of beta dynamics. Using return data on the 25 size and book-to-market sorted portfolios, we find that the nonparametric procedure produces a better fit of the three-factor model to the data, less biased estimates of MPR and lower pricing errors than the Fama–MacBeth procedure with betas estimated under several alternative parametric specifications.     

Heterogeneity,convergence, and autocorrelations

This paper is a contribution to the literature on the explanatory power and calibration of heterogeneous asset pricing models. We set out a new stochastic market-fraction asset pricing model of fundamentalists and trend followers under a market maker. Our model explains key features of financial market behaviour such as market dominance, convergence to the fundamental price and under- and over-reaction. We use the dynamics of the underlying deterministic system to characterize these features and statistical properties, including convergence of the limiting distribution and autocorrelation structure. We confirm these properties using Monte Carlo simulations.     

Weighing asset pricing factors: a least squares model averaging approach

Empirical evidence has demonstrated that certain factors in asset pricing models are more important than others for explaining specific portfolio returns. We propose a technique that evaluates the factors included in popular linear asset pricing models. Our method has the advantage of simultaneously ranking the relative importance of those pricing factors through comparing their model weights. As an empirical verification, we apply our method to portfolios formed following Fama and French [A five-factor asset pricing model. J. Financ. Econ., 2015, 116, 1–22] and demonstrate that models accommodated to our factor rankings do improve their explanatory power in both in-sample and out-of-sample analyses.     

On Model Testing in Financial Economics

This paper discusses the two different contradicting philosophies for testing models in financial economics (asset pricing, corporate finance, and market‐microstructure) using linear regression. We synthesize these two contradicting approaches, document the errors that may occur in the existing estimation methodologies, and suggest a modified procedure that avoids these errors.     

Allocation of Capital in the Insurance Industry

ABSTRACT: This article discusses and critiques the methods that have been proposed for allocating capital in financial institutions, with an emphasis on applications in the insurance industry. The author discusses the rationale for allocating capital by line of business and explains how capital allocation can be used to maximize firm value. The implications for capital allocation of regulatory risk-based capital and the capital asset pricing model are discussed. The advantages and disadvantages of using value-at-risk and insolvency put option criteria in capital allocation are analyzed. Finally, recently proposed methods of marginal capital allocation are evaluated. One conclusion is that using the insolvency put option is superior to value-at-risk for allocating capital but that both methods fail to account for diversification across lines in the multi-line firm. The primary conclusion is that marginal capital allocation methodologies based on option-pricing models that recognize the effects of diversification are the best approach for allocating capital in the financial industry.     

World capital markets and Finnish stock returns

The paper explores issues related to time-varying global equity market integration from a Finnish perspective. Finland is an interesting market since profound economic changes and financial deregulation have taken place since the mid-1980s. Using Finnish firm size ranked portfolios and a conditional four-factor asset pricing model, several restrictions on asset behaviour are examined. It is found that a proxy for changing market integration — lagged foreign equity ownership — has a significant impact on the relative importance of local and global risk factors. Significant differences are found between the pricing of shares that were freely-available to all (unrestricted shares) and domestic investors only (restricted shares). Results also suggest that major capital market reforms profoundly affect the degree of market integration, but local risk factors do not become redundant.     

The Relationships between Real Estate Price and Expected Financial Asset Risk and Return: Theory and Empirical Evidence

In pricing real estate with indifference pricing approach, market incompleteness is shown to significantly alter the conventional pricing relationships between real estate and financial asset. Specifically, we focus on the pricing implication of market comovement because comovement tends to be stronger in financial crisis when investors are especially sensitive to price declines. We find that real estate price increases with expected financial asset return but only in weak market comovement (i.e., a normal market environment) when investors enjoy diversification benefit. When market comovement is strong, real estate price strictly declines with expected financial asset return. More importantly, contrary to the conventional positive relationship from real option studies, real estate price generally declines with expected financial asset risk. With realistic market parameters, we show that there is a nonlinear relationship between real estate price and financial risk. When the market comovement is strong, real estate price only increases with financial asset risk when the risk is low but eventually declines with the risk when it becomes high. Our cross-country empirical results also show that the relationship between financial market risk and real estate price is non-monotonic, conditional on the degree of market comovement.     

Impact of on-field football success on the off-field financial performance of AFL football clubs

In this study, we examine the factors that contribute to the financial performance of clubs in the Australian Football League over the period from 1993 to 2002. Primarily, we examine the association between the on‐field football success of clubs and their level of off‐field financial performance. We find that match attendance is positively related to both short‐term and long‐term success of football clubs and also to the uncertainty as to the match outcome (i.e. the expected closeness of the match). We also find that club membership is highly persistent and is positively related to both the past football success of the club and the marketing expense incurred. Finally, we find that there is a significant association between the level of marketing revenue and the level of on‐field success in the prior 2 years.     

Bridging the gap between accounting and finance

I suggest that the separation of the academic disciplines of accounting and finance has had some detrimental consequences for the development of research and practice in both disciplines, and especially in finance. I argue that an understanding of financial statement numbers and the accounting principles on which they depend – the accounting microstructure – can be important in developing better valuation and asset pricing models and in identifying relevant dimensions of risk. Therefore finance research can benefit from assimilating recent advances in accounting research. Similarly, accounting research relevant to valuation and asset pricing can benefit by adopting theoretical perspectives and empirical methods from finance research.     

Price and Volatility Transmission across Borders

Over the past forty years, financial markets throughout the world have steadily become more open to foreign investors. With open markets, asset prices are determined globally. A vast literature on portfolio choice and asset pricing has evolved to study the importance of global factors as well as local factors as determinants of portfolio choice and of expected returns on risky assets. There is growing evidence that risk premia are increasingly determined globally. An important outcome of this force of globalization is increased comovement in asset prices across markets. This survey study examines the literature on the dynamics of comovements in asset prices and volatility across markets around the world. The literature began in the 1970s in conjunction with early theoretical developments on international asset pricing models, but it blossomed in the late 1980s and early 1990s with the availability of comprehensive international stock market databases and the development of econometric methodology to model these dynamics.     

Multivariate option pricing with time varying volatility and correlations

In this paper we consider option pricing using multivariate models for asset returns. Specifically, we demonstrate the existence of an equivalent martingale measure, we characterize the risk neutral dynamics, and we provide a feasible way for pricing options in this framework. Our application confirms the importance of allowing for dynamic correlation, and it shows that accommodating correlation risk and modeling non-Gaussian features with multivariate mixtures of normals substantially changes the estimated option prices.     

Pricing of time-varying liquidity risk in Finnish stock market: new evidence

Using two recently developed illiquidity measures, we estimate a conditional version of liquidity-adjusted capital asset pricing model (LCAPM), which allows for a time-varying decomposition of the total illiquidity premium into a level component and three risk components. The total estimated annualized illiquidity premium for the Finnish equities during 1997–2015 is 1.13–1.90% depending on the illiquidity measure. Of the three systematic liquidity risk components, risk arising from hedging of wealth shocks is the most important followed by commonality in liquidity risk, whereas flight to liquidity risk is not significantly priced in the Finnish stock market. Our results show that the liquidity risk is time varying, therefore the models estimating the risk-return relationship should address the issue of conditionality.     

A rational asymmetric reaction to news: evidence from English football clubs

Using a large dataset of matches played between two publicly traded English football (soccer) clubs, we test for and confirm an asymmetric market reaction to winning and losing and that the stock market respond stronger and slower to bad news (losing) than good news (winning). In contrast to previous studies, we show that financial fundamentals help explain this asymmetry. In particular, club short-term financial performance is negatively impacted by losing but not impacted by winning. Furthermore, losing is a significantly stronger predictor of future match outcomes than winning.     

The pricing of volatility risk in the US equity market

We analyze whether the pricing of volatility risk depends on the asset pricing framework applied in the tests, the specified volatility proxies, and the portfolio sorts used for spanning the asset universe. For this purpose, we compare the results using a macroeconomic and fundamental based asset pricing model using three proxies of volatility and uncertainty, using size/value sorted and industry sector portfolios. Our results reveal that the marginal pricing effect of the VIX volatility factor is strong and statistically significant throughout the models and specifications, while the effect of an EGARCH-based volatility factor is mixed, mostly smaller but with the correct sign. In most cases, the EGARCH factor does not impair the pricing effect of the VIX. The portfolio sorts have a substantial impact on the volatility premiums in both model frameworks. The size of the volatility risk premium is more uniform across the models if the industry sector portfolio sort is used. Finally, the size/value portfolio sort generates larger volatility risk premiums for both models.     

Uncovering the Risk–Return Relation in the Stock Market

There is ongoing debate about the apparent weak or negative relation between risk (conditional variance) and expected returns in the aggregate stock market. We develop and estimate an empirical model based on the intertemporal capital asset pricing model (ICAPM) that separately identifies the two components of expected returns, namely, the risk component and the component due to the desire to hedge changes in investment opportunities. The estimated coefficient of relative risk aversion is positive, statistically significant, and reasonable in magnitude. However, expected returns are driven primarily by the hedge component. The omission of this component is partly responsible for the existing contradictory results.