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Pedro Santa-Clara

Bio: Pedro Santa-Clara is an academic researcher from Universidade Nova de Lisboa. The author has contributed to research in topics: Portfolio & Capital asset pricing model. The author has an hindex of 39, co-authored 82 publications receiving 9868 citations. Previous affiliations of Pedro Santa-Clara include Saint Petersburg State University & National Bureau of Economic Research.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors take a new look at the predictability of stock market returns with risk measures and find a signi cant positive relation between average stock variance (largely idiosyncratic) and the return on the market.
Abstract: This paper takes a new look at the predictability of stock market returns with risk measures. We ¢nd a signi¢cant positive relation between average stock variance (largely idiosyncratic) and the return on the market. In contrast, the variance of the market has no forecasting power for the market return. These relations persist after we control for macroeconomic variables known to forecast the stock market. The evidence is consistent with models of timevarying risk premia based on background risk and investor heterogeneity. Alternatively, our ¢ndings can be justi¢ed by the option value of equity in the capital structure of the ¢rms. MOSTASSET PRICING MODELS, starting with Merton’s (1973) ICAPM, suggest a positive relation between risk and return for the aggregate stock market. There is a long empirical literature that has tried to establish the existence of such a tradeoi between risk and return for stock market indices. 1 Unfortunately, the results have been inconclusive. Often the relation between risk and return has been found insigni¢cant, and sometimes even negative. The innovation in this paper is to look at average stock risk in addition to market risk.We measure average stock risk in each month similarly to Campbell et al. (2001; hereafter CLMX), as the cross-sectional average of the variances of all the stocks traded in that month.We then run predictive regressions of market returns on this variance measure as well as the variance of the market. Consistent with some previous studies, we ¢nd that market variance has no forecasting power for the market return. However, we do ¢nd a signi¢cant positive relation between average stock variance and the return on the market.

861 citations

Journal ArticleDOI
TL;DR: In this paper, a new estimator that forecasts monthly variance with past daily squared returns is introduced, the Mixed Data Sampling (or MIDAS) approach, which finds that there is a significantly positive relation between risk and return in the stock market.

703 citations

Journal ArticleDOI
TL;DR: In this paper, a new estimator that forecasts monthly variance with past daily squared returns is introduced, the Mixed Data Sampling (or MIDAS) approach, which finds that there is a significantly positive relation between risk and return in the stock market.
Abstract: This paper studies the ICAPM intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns -- the Mixed Data Sampling (or MIDAS) approach. Using MIDAS, we find that there is a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process, and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the ICAPM based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional variance.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

661 citations

Journal ArticleDOI
TL;DR: In this paper, the authors consider various mixed data sampling (MIDAS) regressions to predict volatility and find that daily realized power (involving 5-min absolute returns) is the best predictor of future volatility and outperforms models based on realized volatility.

608 citations

Journal ArticleDOI
TL;DR: The authors used the mixed data sampling approach to study regressions of future realized volatility at low-frequency horizons (one to four weeks) on lagged daily and intra-daily (1) squared returns, (2) absolute return, (3) realized volatility, (4) realized power and (5) return ranges.
Abstract: We use the MIDAS (Mixed Data Sampling) approach to study regressions of future realized volatility at low-frequency horizons (one to four weeks) on lagged daily and intra-daily (1) squared returns, (2) absolute returns, (3) realized volatility, (4) realized power and (5) return ranges. We document first of all that daily realized power and daily range are surprisingly good predictors of future realized volatility and outperform models based on realized volatility. Moreover, MIDAS models with daily data - range, realized power, realized volatility - require a polynomial with at least 30 days. We document that high-frequency absolute returns are also better at forecasting future low frequency realized volatility than high-frequency squared returns. We also discuss many issues that are encountered in practice, such as long memory and seasonality. All the results are based on a commonly used FX data set.

607 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper found that the majority of managers would avoid initiating a positive NPV project if it meant falling short of the current quarter's consensus earnings, and more than three-fourths of the surveyed executives would give up economic value in exchange for smooth earnings.

4,341 citations

Posted Content
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

4,252 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined the pricing of aggregate volatility risk in the cross-section of stock returns and found that stocks with high sensitivities to innovations in aggregate volatility have low average returns.
Abstract: 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

2,936 citations

Journal ArticleDOI
TL;DR: In this article, the authors evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio.
Abstract: We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio. Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1-N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more than offset by estimation error. Based on parameters calibrated to the US equity market, our analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1-N benchmark is around 3000 months for a portfolio with 25 assets and about 6000 months for a portfolio with 50 assets. This suggests that there are still many "miles to go" before the gains promised by optimal portfolio choice can actually be realized out of sample. The Author 2007. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

2,809 citations

Journal ArticleDOI
TL;DR: In this paper, a new approach for approximating the value of American options by simulation is presented, using least squares to estimate the conditional expected payoff to the optionholder from continuation.
Abstract: This article presents a simple yet powerful new approach for approximating the value of American options by simulation. The key to this approach is the use of least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily applicable in path-dependent and multifactor situations where traditional finite difference techniques cannot be used. We illustrate this technique with several realistic examples including valuing an option when the underlying asset follows a jump-diffusion process and valuing an American swaption in a 20-factor string model of the term structure.

2,612 citations