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Asset pricing theory
\"Asset Pricing Theory is an advanced textbook for doctoral students and researchers that offers a modern introduction to the theoretical and methodological foundations of competitive asset pricing.\" \"Costis Skiadas develops in depth the fundamentals of arbitrage pricing, mean-variance analysis, equilibrium pricing, and optimal consumption/portfolio choice in discrete settings, but with emphasis on geometric and martingale methods that facilitate an effortless transition to the more advanced continuous-time theory.\" \"Asset Pricing Theory is complete with extensive exercises at the end of every chapter and comprehensive mathematical appendixes, making this book a self-contained resource for graduate students and academic researchers, as well as mathematically sophisticated practitioners seeking a deeper understanding of concepts and methods on which practical models are built.\"--BOOK JACKET.
Book
Pricing Model Performance and the Two-Pass Cross-Sectional Regression Methodology
Over the years, many asset pricing studies have employed the sample cross-sectional regression (CSR) R² as a measure of model performance. We derive the asymptotic distribution of this statistic and develop associated model comparison tests, taking into account the impact of model misspecification on the variability of the CSR estimates. We encounter several examples of large R² differences that are not statistically significant. A version of the intertemporal capital asset pricing model (CAPM) exhibits the best overall performance, followed by the Fama-French three-factor model. Interestingly, the performance of prominent consumption CAPMs is sensitive to variations in experimental design.
Journal Article
Low-Risk Anomalies?
This paper shows that low-risk anomalies in the capital asset pricing model and in traditional factor models arise when investors require compensation for coskewness risk. Empirically, we find that option-implied ex ante skewness is strongly related to ex post residual coskewness, which allows us to construct coskewness factor-mimicking portfolios. Controlling for skewness renders the alphas of betting-against-beta and betting-against-volatility insignificant. We also show that the returns of beta- and volatility-sorted portfolios are driven largely by a single principal component, which in turn is explained largely by skewness.
Journal Article
Do Strict Capital Requirements Raise the Cost of Capital? Bank Regulation, Capital Structure, and the Low-Risk Anomaly
Traditional capital structure theory predicts that reducing banks' leverage reduces the risk and cost of equity but does not change the weighted average cost of capital, and thus the rates for borrowers. We confirm that the equity of better-capitalized banks has lower beta and idiosyncratic risk. However, over the last 40 years, lower risk banks have not had lower costs of equity (lower stock returns), consistent with a stock market anomaly previously documented in other samples. A calibration suggests that a binding ten percentage point increase in Tier 1 capital to risk-weighted assets could double banks' risk premia over Treasury bills.
Journal Article
Does It Pay to Bet Against Beta? On the Conditional Performance of the Beta Anomaly
Prior studies find that a strategy that buys high-beta stocks and sells low-beta stocks has a significantly negative unconditional capital asset pricing model (CAPM) alpha, such that it appears to pay to \"bet against beta.\" We show, however, that the conditional beta for the high-minus-low beta portfolio covaries negatively with the equity premium and positively with market volatility. As a result, the unconditional alpha is a downward-biased estimate of the true alpha. We model the conditional market risk for beta-sorted portfolios using instrumental variables methods and find that the conditional CAPM resolves the beta anomaly.
Journal Article
Investor Sentiment, Beta, and the Cost of Equity Capital
The security market line accords with the capital asset pricing model by taking on an upward slope in pessimistic sentiment periods, but is downward sloping during optimistic periods. We hypothesize that this finding obtains because periods of optimism attract equity investment by unsophisticated, overconfident, traders in risky opportunities (high beta stocks), whereas such traders stay along the sidelines during pessimistic periods. Thus, high beta stocks become overpriced in optimistic periods, but during pessimistic periods, noise trading is reduced, so that traditional beta pricing prevails. Unconditional on sentiment, these effects offset each other. Although rational explanations cannot completely be ruled out, analyses using earnings expectations, fund flows, the probability of informed trading, and order imbalances do provide evidence that noise traders are more bullish about high beta stocks when sentiment is optimistic, whereas investor behavior appears to accord more closely with rationality during pessimistic periods, supporting our hypothesis.
This paper was accepted by Wei Jiang, Finance.
Journal Article
Asset price dynamics, volatility, and prediction
This book shows how current and recent market prices convey information about the probability distributions that govern future prices. Moving beyond purely theoretical models, Stephen Taylor applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions.
Stephen Taylor provides a comprehensive introduction to the dynamic behavior of asset prices, relying on finance theory and statistical evidence. He uses stochastic processes to define mathematical models for price dynamics, but with less mathematics than in alternative texts. The key topics covered include random walk tests, trading rules, ARCH models, stochastic volatility models, high-frequency datasets, and the information that option prices imply about volatility and distributions.
Asset Price Dynamics, Volatility, and Predictionis ideal for students of economics, finance, and mathematics who are studying financial econometrics, and will enable researchers to identify and apply appropriate models and methods. It will likewise be a valuable resource for quantitative analysts, fund managers, risk managers, and investors who seek realistic expectations about future asset prices and the risks to which they are exposed.
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