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The econometrics of financial markets

TLDR
In this paper, Campbell, Lo, and MacKinlay present an attempt by three well-known and well-respected scholars to fill an acknowledged void in the empirical finance literature, a text covering the burgeoning field of empirical finance.
Abstract
This book is an ambitious effort by three well-known and well-respected scholars to fill an acknowledged void in the literature—a text covering the burgeoning field of empirical finance. As the authors note in the preface, there are several excellent books covering financial theory at a level suitable for a Ph.D. class or as a reference for academics and practitioners, but there is little or nothing similar that covers econometric methods and applications. Perhaps the closest existing text is the recent addition to the Wiley Series in Financial and Quantitative Analysis. written by Cuthbertson (1996). The major difference between the books is that Cuthbertson focuses exclusively on asset pricing in the stock, bond, and foreign exchange markets, whereas Campbell, Lo, and MacKinlay (henceforth CLM) consider empirical applications throughout the field of finance, including corporate finance, derivatives markets, and market microstructure. The level of anticipation preceding publication can be partly measured by the fact that at least three reviews (including this one) have appeared since the book arrived. Moreover, in their reviews, both Harvey (1998) and Tiso (1998) comment on the need for such a text, a sentiment that has been echoed by numerous finance academics.

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Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. The U.S. Treasury Yield Curve: 1961 to the Present

TL;DR: In this article, the authors provide a long history of high-frequency yield curve estimates of the Federal Reserve Board at a daily frequency from 1961 to the present, which can be used to compute yields or forward rates for any horizon.
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There is a risk-return trade-off after all ☆

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.
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Efficient Tests of Stock Return Predictability

TL;DR: This article developed a pretest to determine whether the conventional t-test leads to invalid inference and an efficient test of predictability that corrects this problem, finding evidence for predictability with the short rate and the long-short yield spread.
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Oil price risk and emerging stock markets

TL;DR: In this paper, the impact of oil price changes on a large set of emerging stock market returns was investigated using an international multi-factor model that allows for both unconditional and conditional risk factors.
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Non-Parametric Risk Management and Implied Risk Aversion

TL;DR: In this paper, a nonparametric value-at-risk (VaR) measure is proposed that incorporates economic valuation according to the state-price density associated with the underlying price processes.
References
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