计量经济分析 = Econometric analysis

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Our article comprehensively reexamines the performance of variables that have been suggested by the academic literature to be good predictors of the equity premium. We find that by and large, these models have predicted poorly both in-sample (IS) and out-of-sample (OOS) for 30 years now; these models seem unstable, as diagnosed by their out-of-sample predictions and other statistics; and these models would not have helped an investor with access only to available information to profitably time the market.

/pdf/a-comprehensive-look-at-the-empirical-performance-of-equity-a8mudsid3i.pdf

A Comprehensive Look at the Empirical Performance of Equity Premium Prediction

We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility. IT IS WELL KNOWN THAT THE VOLATILITY OF STOCK RETURNS varies over time. While considerable research has examined the time-series relation between the volatility of the market and the expected return on the market (see, among others, Campbell and Hentschel (1992) and Glosten, Jagannathan, and Runkle (1993)), the question of how aggregate volatility affects the cross-section of expected stock returns has received less attention. Time-varying market volatility induces changes in the investment opportunity set by changing the expectation of future market returns, or by changing the risk-return trade-off. If the volatility of the market return is a systematic risk factor, the arbitrage pricing theory or a factor model predicts that aggregate volatility should also be priced in the cross-section of stocks. Hence, stocks with different sensitivities to innovations in aggregate volatility should have different expected returns. The first goal of this paper is to provide a systematic investigation of how the stochastic volatility of the market is priced in the cross-section of expected stock returns. We want to both determine whether the volatility of the market

https://www0.gsb.columbia.edu/faculty/rhodrick/crosssection.pdf

The Cross-Section of Volatility and Expected Returns

This paper introduces methods to compute impulse responses without specification and estimation of the underlying multivariate dynamic system The central idea consists in estimating local projections at each period of interest rather than extrapolating into increasingly distant horizons from a given model, as it is done with vector autoregressions (VAR) The advantages of local projections are numerous: (1) they can be estimated by simple regression techniques with standard regression packages; (2) they are more robust to misspecification; (3) joint or point-wise analytic inference is simple; and (4) they easily accommodate experimentation with highly non-linear and flexible specifications that may be impractical in a multivariate context Therefore, these methods are a natural alternative to estimating impulse responses from VARs Monte Carlo evidence and an application to a simple, closed-economy, new-Keynesian model clarify these numerous advantages

Estimation and Inference of Impulse Responses by Local Projections

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. THE RETURN ON THE MARKET PORTFOLIO plays a central role in the capital asset pricing model (CAPM), the financial theory widely used by both academics and practitioners. However, the intertemporal properties of stock market returns are not yet fully understood. 1 In particular, there is ongoing debate in the literature about the relation between stock market risk and return, and the extent to which stock market volatility moves stock prices. This paper provides new evidence on the risk‐return relation by estimating a variant of Merton’s (1973) intertemporal capital asset pricing model (ICAPM). In his seminal paper, Merton (1973) shows that the conditional excess market return, Et−1rM,t − rf ,t−1, is a linear function of its conditional variance, σ 2 M,t−1 , (the risk component) and its covariance with investment opportunities, σMF,t−1, (the hedge component); that is,

/pdf/uncovering-the-risk-return-relation-in-the-stock-market-4o1t0rme6s.pdf

Uncovering the Risk-Return Relation in the Stock Market

Oil shocks exert influence on macroeconomic activity through various channels, many of which imply a symmetric effect However, the effect can also be asymmetric In particular, sharp oil price changes-either increases or decreases-may reduce aggregate output temporarily because they delay business investment by raising uncertainty or induce costly sectoral resource reallocation Consistent with these asymmetric-effect hypotheses, the authors find that a volatility measure constructed using daily crude oil futures prices has a negative and significant effect on future gross domestic product (GDP) growth over the period 1984-2004 Moreover, the effect becomes more significant after oil price changes are also included in the regression to control for the symmetric effect The evidence here provides economic rationales for Hamilton's (2003) nonlinear oil shock measure: It captures overall effects, both symmetric and asymmetric, of oil price shocks on output

/pdf/oil-price-volatility-and-u-s-macroeconomic-activity-2e9z3674dr.pdf

Oil Price Volatility and U.S. Macroeconomic Activity

We argue that changes in average idiosyncratic volatility provide a proxy for changes in the investment opportunity set, and this proxy is closely related to the book-to-market factor. We test this idea in two ways using G7 countries' data. First, we show that idiosyncratic volatility has statistically significant predictive power for aggregate stock market returns over time. Second, we show that idiosyncratic volatility performs just as well as the book-to-market factor in explaining the cross section of stock returns. Our results suggest that the hedge against changes in investment opportunities is an important determinant of asset prices.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID615242_code517200.pdf?abstractid=615242

Average Idiosyncratic Volatility in G7 Countries

We find that the value-weighted idiosyncratic stock volatility and aggregate stock market volatility jointly exhibit strong predictive power for excess stock market returns. The stock market risk–return relation is found to be positive, as stipulated by the capital asset pricing model; however, idiosyncratic volatility is negatively related to future stock market returns. Also, idiosyncratic volatility appears to be a pervasive macrovariable, and its forecasting abilities are very similar to those of the consumption–wealth ratio proposed by Lettau and Ludvigson.

/pdf/idiosyncratic-volatility-stock-market-volatility-and-86ujsxxinh.pdf

Idiosyncratic Volatility, Stock Market Volatility, and Expected Stock Returns

We investigate the relative importance of various bankruptcy predictors commonly used in the existing literature by applying a variable selection technique, the least absolute shrinkage and selection operator (LASSO), to a comprehensive bankruptcy database. Over the 1980–2009 period, LASSO admits the majority of Campbell et al. (2008) predictive variables into the bankruptcy forecast model. Interestingly, by contrast with recent studies, some financial ratios constructed from only accounting data also contain significant incremental information about future default risk, and their importance relative to that of market-based variables in bankruptcy forecasts increases with prediction horizons. Moreover, LASSO-selected variables have superior out-of-sample predictive power and outperform (1) those advocated by Campbell et al. (2008) and (2) the distance to default from Merton’s (1974) structural model.

/pdf/variable-selection-and-corporate-bankruptcy-forecasts-25l86s68dx.pdf

Hui Guo

Papers

Uncovering the Risk-Return Relation in the Stock Market

Oil Price Volatility and U.S. Macroeconomic Activity

Average Idiosyncratic Volatility in G7 Countries

Idiosyncratic Volatility, Stock Market Volatility, and Expected Stock Returns

Variable selection and corporate bankruptcy forecasts