Dynamics of Equity Factor Returns and Asset Pricing

In empirical equity asset pricing, the stochastic discount factor (SDF) is implicitly modeled as a linear function of equity factors and is influenced by the empirical properties of the factor returns. We investigate the pricing error introduced by a misspecified SDF which ignores each of the following established empirical phenomena: autocorrelation, dynamics of covariances, dynamics of correlations, and heavy tails for the conditional factor return distribution. We consider near-linear SDFs and nonlinear specifications characterized by a high degree of risk aversion. We find that assuming constant covariances or constant correlations can significantly overprice certain equity portfolios at all risk-aversion levels and that ignoring fat tails can lead to large pricing errors for some derivative assets for highly nonlinear SDFs.

Factors That Fit the Time Series and Cross-Section of Stock Returns

We propose a new method for estimating latent asset pricing factors that fit the time series and cross-section of expected returns. Our estimator generalizes principal component analysis (PCA) by including a penalty on the pricing error in expected returns. Our approach finds weak factors with high Sharpe ratios that PCA cannot detect. We discover five factors with economic meaning that explain well the cross-section and time series of characteristic-sorted portfolio returns. The out-of-sample maximum Sharpe ratio of our factors is twice as large as with PCA with substantially smaller pricing errors. Our factors imply that a significant amount of characteristic information is redundant. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Testing derivatives pricing models under higher-order moment swaps

Purpose This paper aims to test three parametric models in pricing and hedging higher-order moment swaps. Using vanilla option prices from the volatility surface of the Euro Stoxx 50 Index, the paper shows that the pricing accuracy of these models is very satisfactory under four different pricing error functions. The result is that taking a position in a third moment swap considerably improves the performance of the standard hedge of a variance swap based on a static position in the log-contract and a dynamic trading strategy. The position in the third moment swap is taken by running a Monte Carlo simulation. Design/methodology/approach This paper undertook empirical tests of three parametric models. The aim of the paper is twofold: assess the pricing accuracy of these models and show how the classical hedge of the variance swap in terms of a position in a log-contract and a dynamic trading strategy can be significantly enhanced by using third-order moment swaps. The pricing accuracy was measured under four different pricing error functions. A Monte Carlo simulation was run to take a position in the third moment swap. Findings The results of the paper are twofold: the pricing accuracy of the Heston (1993) model and that of two Levy models with stochastic time and stochastic volatility are satisfactory; taking a position in third-order moment swaps can significantly improve the performance of the standard hedge of a variance swap. Research limitations/implications The limitation is that these empirical tests are conducted on existing three parametric models. Maybe more critical insights could have been revealed had these tests been conducted in a brand new derivatives pricing model. Originality/value This work is 100 per cent original, and it undertook empirical tests of the pricing and hedging accuracy of existing three parametric models.

Overreaction in Trading

Using intraday trading data during the 2008 financial crisis, from the Standard and Poor’s Depository Receipt (SPDR) market, we test for evidence of the informational advantage of traders. In addition, we examine the effect of pricing error on trade price. If traders are rational, and have accurate information, they will only purchase an asset at a premium (discount) if they have reason to believe that the fundamental value of that asset will increase (decrease). Our results show that the trading price of the SPDR does not significantly predict the movement of underlying asset values. This finding is consistent with traders overreacting to disparities between price and underlying value during the financial crisis.

Cross Section of KOSPI Returns Based on Cash Flow Risk Factors

This paper introduces two risk factors which are the covariance between long-run consumption growth and cash flows and the duration of cash flow, and investigates how these factors serve to explain the KOSPI return risk premiums. Based on our empirical results comparing the proposed two-factor cash flow model with the standard benchmark models such as CAPM and Fama-French 3-factor model (FF-3F), using KOSPI equity including de-listed stocks, the cash flow model explains 74.7% of the cross-section of equity risk premium while CAPM and FF-3F model explains 41.9% and 64.1% to the maximum, respectively, showing that the cash-flow model is superior in explaining the risk premium factor structure compared with the benchmark models. Also, the pricing error is only 4% in the two-factor cash flow model, while CAPM and FF-3F are 7.7% and 4.7%, respectively, indicating the cash flow model outperforms the standard benchmark models in pricing error as well. These results can be interpreted that the cross section of the equity risk premium is related to a firm’s cash flow and long-run consumption, and therefore the growth rate of consumption in the long run rather than contemporaneous consumption growth rate has a greater influence on the determination of the risk premium.

Information Content of Implicit Spot Prices Embedded in Single Stock Future Prices: Evidence from Indian Market

This study examines the information content of pricing error, measured by the difference between the implied price computed using the cost of carry model and the spot price of Single Stock Futures (SSFs), traded on National Stock Exchange (NSE), India. The returns of portfolios, based on ranking of such pricing errors, are investigated. The consistency of results is verified by controlling for established risk factors, that is, market, size, value and momentum premium, and idiosyncratic factors such as firm’s liquidity and size. Our study reveals that the pricing error is a priced risk factor that contains incremental information about stock returns of day t, and not beyond. We conclude that implied spot prices from stock futures market are useful for traders to profit in the spot market. JEL Classification: G120, G130