Asset Pricing Context Research Articles

What are the events that shake our world? Measuring and hedging global COVOL

Some events impact volatilities of most assets, asset classes, sectors and countries, causing serious damage to investment portfolios. The magnitude of such shocks is defined as global COVOL which is an abbreviation for global common volatility, a broad measure of all types of global financial risk. This paper introduces a statistical formulation of such events as common volatility innovations in both a multivariate volatility and an asset pricing context. Simulations verify the statistical performance of a simple but novel estimator and of a test to detect global COVOL. Two empirical examples show the events that have had the biggest impact on financial markets. The results are useful for portfolio optimization and risk forecasting.

On tree-structured linear and quantile regression-based asset pricing

PurposeThis study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing.Design/Methodology/ApproachThe approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian stochastic method is developed including model selection and estimation of the tree structure parameters. The framework is applied on numerous U.S. asset pricing models, using alternative mimicking factor portfolios, frequency of data, market indices, and equity portfolios.FindingsThe findings reveal strong evidence that asset returns exhibit asymmetric effects and non- linear patterns to different common factors, but, more importantly, that there are multiple thresholds that create several partitions in the common factor space.Originality/ValueTo the best of the authors' knowledge, this paper is the first to explore and apply a tree-structured and quantile regression framework in an asset pricing context.

Machine learning in empirical asset pricing

The tremendous speedup in computing in recent years, the low data storage costs of today, the availability of “big data” as well as the broad range of free open-source software, have created a renaissance in the application of machine learning techniques in science. However, this new wave of research is not limited to computer science or software engineering anymore. Among others, machine learning tools are now used in financial problem settings as well. Therefore, this paper mentions a specific definition of machine learning in an asset pricing context and elaborates on the usefulness of machine learning in this context. Most importantly, the literature review gives the reader a theoretical overview of the most recent academic studies in empirical asset pricing that employ machine learning techniques. Overall, the paper concludes that machine learning can offer benefits for future research. However, researchers should be critical about these methodologies as machine learning has its pitfalls and is relatively new to asset pricing.

LIQUIDITY AS AN ASSET PRICING FACTOR IN THE UK

This study examines whether there is a strong relationship between stock liquidity, which proxies for the implicit cost of trading shares, and future stock returns in an asset-pricing context in the UK stock market. The time period, 1994–2016, includes the most recent global financial crisis that drained liquidity from financial markets worldwide. Four different measures of stock liquidity are employed; the empirical findings indicate that liquidity is a systematic pricing factor and explains a significant portion of the variation in stock returns, even after the inclusion of the other traditional risk factors. The results are robust to both forms of liquidity, either as a residual effect or in its original form as a separate risk factor. Finally, for the first time quantile regression is applied, showing that the liquidity risk factor (LIQ) absorbs a significant portion of the information content of the size and value factors, while remaining independent of the momentum factor.

The Pricing of Spatial Linkages in Companies\u2019 Underlying Assets

Spatial linkages in returns have not yet received much attention in an asset pricing context, however, they can capture important information about idiosyncratic externalities associated with firms’ holdings. We explain returns of real estate companies by modelling the spatial comovement across their underlying assets. We connect stocks of real estate firms using the location of their property portfolios and show that spatial linkages across real estate assets explain some of the variation in abnormal returns, controlling for exposure to systematic factors and firm characteristics. We propose a trading strategy that exploits the information contained in the spatial linkages of the underlying assets. We show that a long-short hedge that sells the stocks that experience a drop in the price if their connected stocks have also gone down in price and buys the stocks that experience an increase in the price if their connected stocks have also gone up delivers a non-market return of 9.7% per year.

What Goes up Must Not Come Down - Time Series Momentum in Factor Risk Premiums

I document significant time variation and predictability in a set of risk factors based on technical indicators. As these indicators are primarily designed to detect trends in asset prices, these findings imply substantial time series momentum in factor risk premiums. Specifically, risk premiums are significantly larger (lower) following recent uptrends (downtrends) in the underlying risk factor. A trend-based dynamic factor strategy, which uses the trend-based signals in order to lever up or down the risk factor, yields annual utility gains of up to 500 basis points, doubles the risk factors' Sharpe ratio, and more than halves their maximum drawdown. In a conditional asset pricing context, employing technical indicators as conditioning information substantially improves a model's explanatory power. Overall, my evidence poses several challenges to risk-based asset pricing theories and seems to be more in line with theories of sentiment and investors initial under- and subsequent overreaction to new information.

Value-at-risk and stock returns: evidence from India

PurposeThe purpose of this paper is to examine the role of value-at-risk (VaR) in the cross-section of stock returns in the Indian stock market during the period 1999-2014.Design/methodology/approachThe paper follows the methodology of Bali and Cakici (2004) to investigate the relationship between VaR and stock returns and employs Fama and French’s (1993) and Fama and Macbeth’s (1973) methods to find out the predictive power of VaR in time-series and cross-section settings. Further, it follows Fama and French (2008) to estimate separate cross-section regressions for small, medium and big stocks to verify the pervasiveness of the anomaly.FindingsThis study finds positive risk premium associated with VaR in the Indian stock market during 2001-2008, the period of short selling constraint for institutional investors. This premium is confined to small stocks and low institutional holdings. The positive premium can be attributed to short selling constraints.Practical implicationsThe risk-return tradeoff can be utilized by investors and fund managers. As it is confined to small stocks, transaction costs may affect the profitability of the investment strategy.Originality/valueThe study contributes to the scanty empirical literature on the role of VaR in the cross-section of expected stock returns. Moreover, this is the first study that explores the relationship between VaR and stock returns in the asset pricing context for the Indian stock market.

Asset Pricing, Spatial Linkages and Contagion in Real Estate Stocks

Although spatial techniques have been used to capture the spillovers in asset returns across different regions, they have not yet been applied in an asset pricing context. Combining asset pricing models and equilibrium spatial models can be a good way to disentangle spillover effects across assets thereby accounting for systemic risks. We use an innovative approach and estimate a four-factor ‘Spatial’ Capital Asset Pricing Model (SCAPM) which allows us to account for correlations in the error terms. We can account for direct or indirect spillover effects in the presence of increased spillovers through the idiosyncratic component or of increased spillovers through the market respectively. We find that contagion dramatically increases during the global financial crisis and the spillover effect can explain up to 40% of total asset variation. In the rest of the time, idiosyncratic risks have been the predominant type of risk in real estate stocks. Our results have implications for investors showing that the market can channel asset volatility leading to contagion during crisis periods and therefore residual linkages between country indices need to be accounted for as a means of assessing the diversification benefits of a global portfolio.

The Pricing of Spatial Linkages in Companiess Underlying Assets

Spatial linkages in returns have not yet received much attention in an asset pricing context, however, they can capture important information about idiosyncratic externalities associated with firms’ holdings. We explain abnormal returns of real estate companies by modelling the spatial comovement across their underlying assets. We connect stocks using the location of their underlying assets, the properties. Comovement arises from price pressures associated with property idiosyncrasies. We show that spatial linkages across the firms’ holdings explain some of the variation in abnormal returns, controlling for exposure to systematic return factors and firm characteristics. We propose a trading strategy which exploits the information contained in the spatial linkages of the underlying assets. We show that a long-short hedge strategy which buys the stocks that experience an increase in the price if their connected stocks have also gone up and sells the stocks that experience a drop if their connected stocks have also gone down in price can earn a non-market return of an annual 12%.

Interest rate dynamic effect on stock returns and central bank transparency: Evidence from emerging markets

Interest rate dynamic effect on stock returns is examined under different levels of central bank transparency under an asset pricing context. Using a large set of emerging countries in a panel data framework, we provide evidence for a negative link between stock returns and interest rate differences. However, this negative effect is reduced significantly under a transparent central bank, underlying a non-linear impact on stock returns. Our study is focused on a period from 1998 to 2008 where fundamental changes in the level of central banks’ transparency were occurred. Our findings imply that restrictive monetary policies under high levels of transparency lead to smoother reductions on stock returns with significant benefits for financial stability.

Quantum-Like Tunnelling and Levels of Arbitrage

We apply methods of wave mechanics to financial modelling. We proceed by assigning a financial interpretation to wave numbers. This paper makes a plea for the use of the concept of 'tunnelling' (in the mathematical formalism of quantum mechanics) in the modelling of financial arbitrage. Financial arbitrage is a delicate concept to model in social science (i.e. in this case economics and finance) as its presence affects the precision of benchmark financial asset prices. In this paper, we attempt to show how 'tunnelling' can be used to positive effect in the modelling of arbitrage in a financial asset pricing context.

Cross-sectional performance and investor sentiment in a multiple risk factor model

Economists have long recognized the importance of information veracity in valuing risky securities. Market participants concerned about the credibility of information measures may require additional compensation to entice them to hold stocks with less transparent information. These same securities are expected to display greater sensitivities to measures of market sentiment. We find that investor sentiment sensitivities increase directly with multiple measures of opacity in the cross-section. Next we examine the extent to which sentiment sensitivities are priced in an asset pricing context. Using the Jha et al. (2009) model of conditional performance evaluation, we find an inverse relation between ex ante known investor sentiment and the marginal performance of opaque stocks. In contrast, translucent stocks exhibit relatively little variability in performance across levels of sentiment.

Skewness and Kurtosis Persistence: Conventional vs. Robust Measures

Recently, independent of each other, there has been interest in (i) time-variation in higher-order moments; (ii) idiosyncratic skewness and predictability of skewness in the asset pricing context; and (iii) robust measures of skewness and kurtosis. The second literature questions the usefulness of past skewness as a predictor of future skewness, arguing that skewness may not exhibit strong enough persistence. We look at an intersection of these three literatures and study the persistence (in the time-series) of conventional and robust measures of higher-order moments for eight financial series. Conventional and robust measures, when the moments exist, are highly correlated. Furthermore, robust skewness and kurtosis measures exhibit as much or more persistence as the conventional measures. The results suggest that one could consider robust measures, instead of the conventional measures, in the asset pricing context.

The Global Price of Market Risk and Country Inflation

This paper approaches the central questions of the identification and the price of risk in an international asset pricing context. We construct and use factor mimicking portfolios to obtain factor loadings for testing unconditional and conditional pricing. We use a new measure of specification error for conditional models. The dynamic stochastic discount factor which explicitly admits skewness and kurtosis factors in the aggregate global market portfolio significantly expands the factor frontier more than the effect achieved by adding the Fama-French factors to the return on the world market portfolio. A cubic SDF augmented by country-specific inflation and inflation skewness with time-varying risk premiums that are functions of global predictive variables is the best performing model overall for pricing the size, book-to-market and momentum portfolios in the U.S., U.K. and Japan. The country-specific risks are significantly priced, suggesting that the financial markets in these countries may be partially-segmented.

Momentum and mean reversion across national equity markets

Numerous studies have separately identified mean reversion and momentum. This paper considers these effects jointly. Our empirical model assumes that only global equity price index shocks can have permanent components. This is motivated in a production-based asset pricing context, given that production levels converge across developed countries. Combination momentum-contrarian strategies, used to select from among 18 developed equity markets at a monthly frequency, outperform both pure momentum and pure contrarian strategies. The results continue to hold after corrections for factor sensitivities and transaction costs. They reveal the importance of controlling for mean reversion in exploiting momentum and vice versa.

Bank Credit Cycles

A bank determines whether potential borrowers are credit-worthy, that is, whether they meet the bank's or lending standards. In making this determination, each bank is in competition with other banks, but without knowing the competitor banks' standards. The resulting unique form of competition leads to endogenous cycles, periodic credit crunches. Empirical tests of this repeated bank lending game are constructed based on parameterizing public information about relative bank performance that is at the root of banks' beliefs about rival banks' lending standards. The relative performance of rival banks has predictive power for subsequent lending in the card market, where we can identify the main competitors. At the macroeconomic level, the relative bank performance of commercial and industrial loans is an autonomous source of macroeconomic fluctuations. In an asset pricing context, the relative bank performance is a priced risk factor for both banks and nonfinancial firms. The factor-coefficients for non-financial firms are decreasing with size, consistent with smaller firms being more bank-dependent.

Risk and Return to Housing, Tenure Choice and the Value of Housing in an Asset Pricing Context

Homeowners do not diversify their risky home equity because of fixed costs of issuing securities and information costs. An asset pricing model is developed for homeowners with the undiversifiable home equity asset. Homeowner value and house value to diversified landlords are compared, and a tenure choice equation is developed. We demonstrate the existence of a rational expectations equilibrium under appropriate conditions.

Finite sample properties of the generalized method of moments in tests of conditional asset pricing models

Abstract We develop evidence on the finite sample properties of the Generalized Method of Moments (GMM) in an asset pricing context. The models imply nonlinear, cross-equation restrictions on predictive regressions for security returns. We find that a two-stage GMM approach produces goodness-of-fit statistics that reject the restrictions too often. An iterated GMM approach has superior finite sample properties. The coefficient estimates are approximately unbiased in simpler models, but their asymptotic standard errors are understated. Simple adjustments for the standard errors are partially successful in correcting the bias. In more complex models the coefficients and their standard errors can be highly unreliable. The power of the tests to reject a single-premium model is higher against a two-premium, fixed-beta alternative than against a conditional Capital Asset Pricing Model with time-varying betas.