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966
Determining the Number of Factors in Approximate Factor Models
 Econometrica
, 2002
"... In this paper we develop some statistical theory for factor models of large dimensions. The focus is the determination of the number of factors, which is an unresolved issue in the rapidly growing literature on multifactor models. We propose a panel Cp criterion and show that the number of factors c ..."
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Cited by 224 (19 self)
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In this paper we develop some statistical theory for factor models of large dimensions. The focus is the determination of the number of factors, which is an unresolved issue in the rapidly growing literature on multifactor models. We propose a panel Cp criterion and show that the number of factors can be consistently estimated using the criterion. The theory is developed under the framework of large crosssections (N) and large time dimensions (T). No restriction is imposed on the relation between N and T. Simulations show that the proposed criterion yields almost precise estimates of the number of factors for configurations of the panel data encountered in practice. The idea that variations in a large number of economic variables can be modelled bya small number of reference variables is appealing and is used in manyeconomic analysis. In the finance literature, the arbitrage pricing theory(APT) of Ross (1976) assumes that a small number of factors can be used to explain a large number of asset returns. 1
Correlation And Dependence In Risk Management: Properties And Pitfalls
 RISK MANAGEMENT: VALUE AT RISK AND BEYOND
, 1999
"... Modern risk management calls for an understanding of stochastic dependence going beyond simple linear correlation. This paper deals with the static (nontimedependent) case and emphasizes the copula representation of dependence for a random vector. Linear correlation is a natural dependence measure ..."
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Cited by 195 (30 self)
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Modern risk management calls for an understanding of stochastic dependence going beyond simple linear correlation. This paper deals with the static (nontimedependent) case and emphasizes the copula representation of dependence for a random vector. Linear correlation is a natural dependence measure for multivariate normally and, more generally, elliptically distributed risks but other dependence concepts like comonotonicity and rank correlation should also be understood by the risk management practitioner. Using counterexamples the falsity of some commonly held views on correlation is demonstrated; in general, these fallacies arise from the naive assumption that dependence properties of the elliptical world also hold in the nonelliptical world. In particular, the problem of finding multivariate models which are consistent with prespecified marginal distributions and correlations is addressed. Pitfalls are highlighted and simulation algorithms avoiding these problems are constructed. ...
Nonparametric Estimation of StatePrice Densities Implicit In Financial Asset Prices
 JOURNAL OF FINANCE
, 1997
"... Implicit in the prices of traded financial assets are ArrowDebreu prices or, with continuous states, the stateprice density (SPD). We construct a nonparametric estimator for the SPD implicit in option prices and derive its asymptotic sampling theory. This estimator provides an arbitragefree metho ..."
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Cited by 192 (3 self)
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Implicit in the prices of traded financial assets are ArrowDebreu prices or, with continuous states, the stateprice density (SPD). We construct a nonparametric estimator for the SPD implicit in option prices and derive its asymptotic sampling theory. This estimator provides an arbitragefree method of pricing new, complex, or illiquid securities while capturing those features of the data that are most relevant from an assetpricing perspective, e.g., negative skewness and excess kurtosis for asset returns, volatility "smiles" for option prices. We perform Monte Carlo experiments and extract the SPD from actual S&P 500 option prices.
Valuation Ratios and the LongRun Stock Market Outlook: An Update
 Journal of Portfolio Management
, 2001
"... The use of priceearnings ratios and dividendprice ratios as forecasting variables for the stock market is examined using aggregate annual US data 1871 to 2000 and aggregate quarterly data for twelve countries since 1970. Various simple efficientmarkets models of financial markets imply that ..."
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Cited by 95 (8 self)
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The use of priceearnings ratios and dividendprice ratios as forecasting variables for the stock market is examined using aggregate annual US data 1871 to 2000 and aggregate quarterly data for twelve countries since 1970. Various simple efficientmarkets models of financial markets imply that these ratios should be useful in forecasting future dividend growth, future earnings growth, or future productivity growth. We conclude that, overall, the ratios do poorly in forecasting any of these.
Understanding Predictability
 JOURNAL OF POITICAL ECONOMY
, 2004
"... We propose a general equilibrium model with multiple securities in which investors’ risk preferences and expectations of dividend growth are time varying. While time varying risk preferences induce the standard positive relation between the dividend yield and expected returns, time varying expected ..."
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Cited by 89 (4 self)
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We propose a general equilibrium model with multiple securities in which investors’ risk preferences and expectations of dividend growth are time varying. While time varying risk preferences induce the standard positive relation between the dividend yield and expected returns, time varying expected dividend growth induces a negative relation between them. These offsetting effects reduce the ability of the dividend yield to forecast returns and eliminate its ability to forecast dividend growth, as observed in the data. The model links the predictability of returns to that of dividend growth, suggesting specific changes to standard linear predictive regressions for both. The model’s predictions are con…rmed empirically.
Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH
, 2001
"... In this paper, we develop the theoretical and empirical properties of a new class of multivariate GARCH models capable of estimating large timevarying covariance matrices, Dynamic Conditional Correlation Multivariate GARCH. We show that the problem of multivariate conditional variance estimation ca ..."
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Cited by 88 (7 self)
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In this paper, we develop the theoretical and empirical properties of a new class of multivariate GARCH models capable of estimating large timevarying covariance matrices, Dynamic Conditional Correlation Multivariate GARCH. We show that the problem of multivariate conditional variance estimation can be simplified by estimating univariate GARCH models for each asset, and then, using transformed residuals resulting from the first stage, estimating a conditional correlation estimator. The standard errors for the first stage parameters remain consistent, and only the standard errors for the correlation parameters need be modified. We use the model to estimate the conditional covariance of up to 100 assets using S&P 500 Sector Indices and Dow Jones Industrial Average stocks, and conduct specification tests of the estimator using an industry standard benchmark for volatility models. This new estimator demonstrates very strong performance especially considering ease of implementation of the estimator.
Hedge Funds and the Technology Bubble
 THE JOURNAL OF FINANCE • VOL. LIX, NO. 5 • OCTOBER 2004
, 2004
"... This paper documents that hedge funds did not exert a correcting force on stock prices during the technology bubble. Instead, they were heavily invested in technology stocks. This does not seem to be the result of unawareness of the bubble: Hedge funds captured the upturn, but, by reducing their pos ..."
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Cited by 87 (5 self)
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This paper documents that hedge funds did not exert a correcting force on stock prices during the technology bubble. Instead, they were heavily invested in technology stocks. This does not seem to be the result of unawareness of the bubble: Hedge funds captured the upturn, but, by reducing their positions in stocks that were about to decline, avoided much of the downturn. Our findings question the efficient markets notion that rational speculators always stabilize prices. They are consistent with models in which rational investors may prefer to ride bubbles because of predictable investor sentiment and limits to arbitrage.
Nonlinear Pricing Kernels, Kurtosis Preference, and the CrossSection of Assets Returns
 Journal of Finance
, 2002
"... This paper investigates nonlinear pricing kernels in which the risk factor is endogenously determined and preferences restrict the definition of the pricing kernel. These kernels potentially generate the empirical performance of nonlinear and multifactor models, while maintaining empirical power and ..."
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Cited by 82 (2 self)
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This paper investigates nonlinear pricing kernels in which the risk factor is endogenously determined and preferences restrict the definition of the pricing kernel. These kernels potentially generate the empirical performance of nonlinear and multifactor models, while maintaining empirical power and avoiding ad hoc specifications of factors or functional form. Our test results indicate that preferencerestricted nonlinear pricing kernels are both admissible for the cross section of returns and are able to significantly improve upon linear single and multifactor kernels. Further, the nonlinearities in the pricing kernel drive out the importance of the factors in the linear multifactor model. A PRINCIPAL IMPLICATION OF THE Capital Asset Pricing Model ~CAPM! is that the pricing kernel is linear in a single factor, the portfolio of aggregate wealth. Numerous studies over the past two decades have documented violations of this restriction. 1 In response, researchers have examined the performance of alternative models of asset prices. These models have generally fallen into two classes: ~1! multifactor models such as Ross ’ APT or Merton’s ICAPM, in which factors in addition to the market return determine asset prices; or ~2! nonparametric models, such as Bansal et al. ~1993!, Bansal and Viswanathan ~1993!, and Chapman ~1997!, in which the pricing kernel is not
Equity volatility and corporate bond yields
 Journal of Finance
, 2003
"... This paper explores the e¡ect of equity volatility on corporate bond yields. Panel data for the late 1990s show that idiosyncratic ¢rmlevel volatility can explain as much crosssectional variation in yields as can credit ratings. This ¢nding, together with the upward trend in idiosyncratic equity v ..."
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Cited by 82 (1 self)
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This paper explores the e¡ect of equity volatility on corporate bond yields. Panel data for the late 1990s show that idiosyncratic ¢rmlevel volatility can explain as much crosssectional variation in yields as can credit ratings. This ¢nding, together with the upward trend in idiosyncratic equity volatility documented by Campbell, Lettau, Malkiel, and Xu (2001), helps to explain recent increases in corporate bond yields. DURING THE LATE 1990s, THE U.S. EQUITY and corporate bond markets behaved very di¡erently. As displayed in Figure 1, stock prices rose strongly, while at the same time, corporate bonds performed poorly. The proximate cause of the low returns on corporate bonds was a tendency for the yields on both seasoned and newly issued corporate bonds to increase relative to the yields of U.S.Treasury securities. These increases in corporate^Treasury yield spreads are striking because they occurred at a time when stock prices were rising; the optimism of stock market investors did not seem to be shared by investors in the corporate bond market.