The increase in trading frequency of Exchanged Traded Funds (ETFs) presents a positive externality for financial risk management when the price of the ETF is available at a higher frequency than the price of the component stocks. The positive spillover consists in improving the accuracy of pre-estimators of the integrated covariance of the stocks included in the ETF. The proposed Beta Adjusted Covariance (BAC) equals the pre-estimator plus a minimal adjustment matrix such that the covariance-implied stock-ETF beta equals a target beta. We focus on the Hayashi and Yoshida (2005) pre-estimator and derive the asymptotic distribution of its implied stock-ETF beta. The simulation study confirms that the accuracy gains are substantial in all cases considered. In the empirical part of the paper, we show the gains in tracking error efficiency when using the BAC adjustment for constructing portfolios that replicate a broad index using a subset of stocks.
{"title":"Beta-Adjusted Covariance Estimation","authors":"Kris Boudt, K. Dragun, Orimar Sauri, S. Vanduffel","doi":"10.2139/ssrn.3768326","DOIUrl":"https://doi.org/10.2139/ssrn.3768326","url":null,"abstract":"The increase in trading frequency of Exchanged Traded Funds (ETFs) presents a positive externality for financial risk management when the price of the ETF is available at a higher frequency than the price of the component stocks. The positive spillover consists in improving the accuracy of pre-estimators of the integrated covariance of the stocks included in the ETF. The proposed Beta Adjusted Covariance (BAC) equals the pre-estimator plus a minimal adjustment matrix such that the covariance-implied stock-ETF beta equals a target beta. We focus on the Hayashi and Yoshida (2005) pre-estimator and derive the asymptotic distribution of its implied stock-ETF beta. The simulation study confirms that the accuracy gains are substantial in all cases considered. In the empirical part of the paper, we show the gains in tracking error efficiency when using the BAC adjustment for constructing portfolios that replicate a broad index using a subset of stocks.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126025486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Estimation and testing of factor models in asset pricing requires choosing a set of test assets. The choice of test assets determines how well different factor risk premia can be identified: if only few assets are exposed to a factor, that factor is weak, which makes standard estimation and inference incorrect. In other words, the strength of a factor is not an inherent property of the factor: it is a property of the cross-section used in the analysis. We propose a novel way to select assets from a universe of test assets and estimate the risk premium of a factor of interest, as well as the entire stochastic discount factor, that explicitly accounts for weak factors and test assets with highly correlated risk exposures. We refer to our methodology as supervised principal component analysis (SPCA), because it iterates an asset selection step and a principal-component estimation step. We provide the asymptotic properties of our estimator, and compare its limiting behavior with that of alternative estimators proposed in the recent literature, which rely on PCA, Ridge, Lasso, and Partial Least Squares (PLS). We find that the SPCA is superior in the presence of weak factors, both in theory and in finite samples. We illustrate the use of SPCA by using it to estimate the risk premia of several tradable and nontradable factors.
{"title":"Test Assets and Weak Factors","authors":"Stefano Giglio, D. Xiu, Dake Zhang","doi":"10.2139/SSRN.3768081","DOIUrl":"https://doi.org/10.2139/SSRN.3768081","url":null,"abstract":"Estimation and testing of factor models in asset pricing requires choosing a set of test assets. The choice of test assets determines how well different factor risk premia can be identified: if only few assets are exposed to a factor, that factor is weak, which makes standard estimation and inference incorrect. In other words, the strength of a factor is not an inherent property of the factor: it is a property of the cross-section used in the analysis. We propose a novel way to select assets from a universe of test assets and estimate the risk premium of a factor of interest, as well as the entire stochastic discount factor, that explicitly accounts for weak factors and test assets with highly correlated risk exposures. We refer to our methodology as supervised principal component analysis (SPCA), because it iterates an asset selection step and a principal-component estimation step. We provide the asymptotic properties of our estimator, and compare its limiting behavior with that of alternative estimators proposed in the recent literature, which rely on PCA, Ridge, Lasso, and Partial Least Squares (PLS). We find that the SPCA is superior in the presence of weak factors, both in theory and in finite samples. We illustrate the use of SPCA by using it to estimate the risk premia of several tradable and nontradable factors.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133575897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper generalizes Ross (2015) recovery theory to accommodate growth, including the Black-Scholes and stochastic volatility option models. The new theory recovers information about equity risk premia and variance risk premia from options prices. In the Heston (1993) stochastic volatility model, the theory predicts an exact (negative) value for the variance risk premium as a function of the equity premium. Recovery theory also predicts that the stochastic discount factor is the reciprocal return on a model-free portfolio of index options. This paper tests the theory on returns from one-month VIX and three-month VIX3M option portfolios from 2007-2018. Recovery theory links the equity pre- mium to the values of both conditional and unconditional variance premia. It also predicts how VIX3M option variance is a biased predictor of future one-month VIX variance. Empirically, recovery theory simultaneously matches the average S&P 500 equity premium, the average variance premium, and observed biases in the variance expectations hypothesis. Autocorrelation properties of VIX indices imply a 12% annual equity premium.
{"title":"Recovering the Variance Premium","authors":"S. Heston","doi":"10.2139/ssrn.3763575","DOIUrl":"https://doi.org/10.2139/ssrn.3763575","url":null,"abstract":"This paper generalizes Ross (2015) recovery theory to accommodate growth, including the Black-Scholes and stochastic volatility option models. The new theory recovers information about equity risk premia and variance risk premia from options prices. In the Heston (1993) stochastic volatility model, the theory predicts an exact (negative) value for the variance risk premium as a function of the equity premium. Recovery theory also predicts that the stochastic discount factor is the reciprocal return on a model-free portfolio of index options. \u0000 \u0000This paper tests the theory on returns from one-month VIX and three-month VIX3M option portfolios from 2007-2018. Recovery theory links the equity pre- mium to the values of both conditional and unconditional variance premia. It also predicts how VIX3M option variance is a biased predictor of future one-month VIX variance. Empirically, recovery theory simultaneously matches the average S&P 500 equity premium, the average variance premium, and observed biases in the variance expectations hypothesis. Autocorrelation properties of VIX indices imply a 12% annual equity premium.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114355317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When investors disagree and trade on their views about asset returns, market prices reflect the wealth/consumption share weighted average belief about risk premia, where more accurate, risk tolerant, or patient investors carry a larger weight. We explore the properties of this market view, and show that many puzzling properties of survey measures can be reconciled within disagreement models. For instance, a model with disagreement about output growth matches the negative correlation between statistical and survey-based measures of the risk premium, the higher variance and lower persistence of statistical measures of the risk premium and the appearance of return extrapolation.
{"title":"The Market View","authors":"C. Heyerdahl-Larsen, P. Illeditsch","doi":"10.2139/ssrn.3762259","DOIUrl":"https://doi.org/10.2139/ssrn.3762259","url":null,"abstract":"When investors disagree and trade on their views about asset returns, market prices reflect the wealth/consumption share weighted average belief about risk premia, where more accurate, risk tolerant, or patient investors carry a larger weight. We explore the properties of this market view, and show that many puzzling properties of survey measures can be reconciled within disagreement models. For instance, a model with disagreement about output growth matches the negative correlation between statistical and survey-based measures of the risk premium, the higher variance and lower persistence of statistical measures of the risk premium and the appearance of return extrapolation.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123391140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper uses the co-movement of gold mining shares with the price of gold to assess the strength of flight to quality by distinguishing between flight to physical gold and flight to gold mining company shares. The analysis of a global sample of gold mining companies reveals that flights to quality are very different across financial shocks with the bankruptcy of Lehman Brothers and the Brexit vote being the most extreme at opposite ends of the spectrum. We also find evidence that a flight from gold mining shares to gold leads to a stronger price reaction and thus to a stronger safe haven effect of gold bullion. The analysis demonstrates that gold mining companies can enrich our understanding of the flight to quality phenomenon.
{"title":"Flight to Quality - Gold Mining Shares versus Gold Bullion","authors":"D. Baur, Philipp Prange, Karsten Schweikert","doi":"10.2139/ssrn.3488633","DOIUrl":"https://doi.org/10.2139/ssrn.3488633","url":null,"abstract":"This paper uses the co-movement of gold mining shares with the price of gold to assess the strength of flight to quality by distinguishing between flight to physical gold and flight to gold mining company shares. The analysis of a global sample of gold mining companies reveals that flights to quality are very different across financial shocks with the bankruptcy of Lehman Brothers and the Brexit vote being the most extreme at opposite ends of the spectrum. We also find evidence that a flight from gold mining shares to gold leads to a stronger price reaction and thus to a stronger safe haven effect of gold bullion. The analysis demonstrates that gold mining companies can enrich our understanding of the flight to quality phenomenon.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129745610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates stock and option market reactions to events in the United States Supreme Court (SC) relating to cases where at least one party involved is a public firm. Typically, cases that reach the SC level would have passed through multiple lower courts. Consequently, much of the information content of these cases would be publicly known. If the financial market had perfectly anticipated that the SC would grant the writ of certiorari (a rare event of accepting a case for review), the tone of the subsequent legal arguments, and the final decision, then there should be no reaction to any of these events, as and when they unfold. Using a comprehensive dataset of more than 500 SC cases from 1948 to 2018, we find that the stock market reacts to both the grant of certiorari and to the announcement of the final decision, suggesting that the stock market could not anticipate the SC actions. We also find that case-specific characteristics, such as parties involved, the type of legal issue, and press coverage explain some of the cross-sectional variations in the stock returns across cases. Our tests also indicate that there is no information leakage prior to the events, and no stock price drift after the events. We also find some evidence that the option market anticipates the final decision as early as the date certiorari is granted, reinforcing the theory that smart money comes early to the option market.
{"title":"Do Financial Markets Anticipate Corporate-Related Decisions of the United States Supreme Court?","authors":"Yehuda Davis, S. Govindaraj, Kate Suslava","doi":"10.2139/ssrn.3761235","DOIUrl":"https://doi.org/10.2139/ssrn.3761235","url":null,"abstract":"This paper investigates stock and option market reactions to events in the United States Supreme Court (SC) relating to cases where at least one party involved is a public firm. Typically, cases that reach the SC level would have passed through multiple lower courts. Consequently, much of the information content of these cases would be publicly known. If the financial market had perfectly anticipated that the SC would grant the writ of certiorari (a rare event of accepting a case for review), the tone of the subsequent legal arguments, and the final decision, then there should be no reaction to any of these events, as and when they unfold. Using a comprehensive dataset of more than 500 SC cases from 1948 to 2018, we find that the stock market reacts to both the grant of certiorari and to the announcement of the final decision, suggesting that the stock market could not anticipate the SC actions. We also find that case-specific characteristics, such as parties involved, the type of legal issue, and press coverage explain some of the cross-sectional variations in the stock returns across cases. Our tests also indicate that there is no information leakage prior to the events, and no stock price drift after the events. We also find some evidence that the option market anticipates the final decision as early as the date certiorari is granted, reinforcing the theory that smart money comes early to the option market.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127857647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hedging short gamma exposure requires trading in the direction of price movements, thereby creating price momentum. Using intraday returns on over 60 futures on equities, bonds, commodities, and currencies between 1974 and 2020, we document strong “market intraday momentum” everywhere. The return during the last 30 minutes before the market close is positively predicted by the return during the rest of the day (from previous market close to the last 30 minutes). The predictive power is economically and statistically highly significant, and reverts over the next days. We provide novel evidence that links market intraday momentum to the gamma hedging demand from market participants such as market makers of options and leveraged ETFs.
{"title":"Hedging Demand and Market Intraday Momentum","authors":"","doi":"10.2139/ssrn.3760365","DOIUrl":"https://doi.org/10.2139/ssrn.3760365","url":null,"abstract":"Hedging short gamma exposure requires trading in the direction of price movements, \u0000thereby creating price momentum. Using intraday returns on over 60 futures on equities, \u0000bonds, commodities, and currencies between 1974 and 2020, we document strong “market \u0000intraday momentum” everywhere. The return during the last 30 minutes before the market \u0000close is positively predicted by the return during the rest of the day (from previous market \u0000close to the last 30 minutes). The predictive power is economically and statistically highly \u0000significant, and reverts over the next days. We provide novel evidence that links market \u0000intraday momentum to the gamma hedging demand from market participants such as market \u0000makers of options and leveraged ETFs.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124178301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper performs an out-of-sample comparison of linear factor asset pricing models from an economic perspective under predictability. I assess the economic value added of several factor models when a Bayesian investor is faced with a portfolio allocation problem whereby each model imposes cross-sectional restrictions on the parameters of a predictive stock return regression. The empirical framework explicitly accounts for investor skepticism about the model, i.e., mispricing uncertainty. Using several US portfolios as test assets, I find that the q5 model of Hou et al. (2020), as well as the behavioral factor models of Stambaugh and Yuan (2017) and Daniel et al. (2020) outperform competing models across investment horizons. At the longest evaluated horizon (one year), a benchmark portfolio built using historical data produces larger portfolio gains than all the factor models, but in the short run (at the one-month horizon), their performance is comparable.
{"title":"Economic Evaluation of Asset Pricing Models Under Predictability","authors":"Erwin Hansen","doi":"10.2139/ssrn.3852305","DOIUrl":"https://doi.org/10.2139/ssrn.3852305","url":null,"abstract":"This paper performs an out-of-sample comparison of linear factor asset pricing models from an economic perspective under predictability. I assess the economic value added of several factor models when a Bayesian investor is faced with a portfolio allocation problem whereby each model imposes cross-sectional restrictions on the parameters of a predictive stock return regression. The empirical framework explicitly accounts for investor skepticism about the model, i.e., mispricing uncertainty. Using several US portfolios as test assets, I find that the q5 model of Hou et al. (2020), as well as the behavioral factor models of Stambaugh and Yuan (2017) and Daniel et al. (2020) outperform competing models across investment horizons. At the longest evaluated horizon (one year), a benchmark portfolio built using historical data produces larger portfolio gains than all the factor models, but in the short run (at the one-month horizon), their performance is comparable.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114946053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adam Zaremba, Nusret Cakici, R. Bianchi, Huaigang Long
We document a new cross-sectional anomaly that links international government bond and equity markets. Using a unique long-run dataset of 61 countries for the years 1900–2019, we demonstrate that past bond yield changes predict future stock index returns in the cross-section. The quintile of countries with the largest decline (or smallest increase) in government bond yields outperforms the quintile of countries with the smallest decline (or largest increase) by 0.63% per month. Our findings support the behavioral roots of this effect, suggesting that investors underreact to yield changes, and slow-moving capital prevents arbitrageurs from eliminating the anomaly. Global investors can employ this bond yield change effect to enhance international asset allocation decisions.
{"title":"Yield Curve Shifts and the Cross-Section of Global Equity Returns","authors":"Adam Zaremba, Nusret Cakici, R. Bianchi, Huaigang Long","doi":"10.2139/ssrn.3756047","DOIUrl":"https://doi.org/10.2139/ssrn.3756047","url":null,"abstract":"We document a new cross-sectional anomaly that links international government bond and equity markets. Using a unique long-run dataset of 61 countries for the years 1900–2019, we demonstrate that past bond yield changes predict future stock index returns in the cross-section. The quintile of countries with the largest decline (or smallest increase) in government bond yields outperforms the quintile of countries with the smallest decline (or largest increase) by 0.63% per month. Our findings support the behavioral roots of this effect, suggesting that investors underreact to yield changes, and slow-moving capital prevents arbitrageurs from eliminating the anomaly. Global investors can employ this bond yield change effect to enhance international asset allocation decisions.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123303009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Open-end corporate bond mutual funds invest in illiquid assets while providing liquid claims to shareholders. Does such liquidity transformation introduce fragility to the corporate bond market? To address this question, we create a novel bond-level latent fragility measure based on asset illiquidity of mutual funds holding the bond. We find that corporate bonds bearing higher fragility subsequently experience higher return volatility and more outflows-induced mutual fund selling over the period of 2006–2019. Using the COVID-19 crisis as a natural experiment, we find that bonds with higher precrisis fragility experienced more negative returns and larger reversals around March 2020.
{"title":"Does Mutual Fund Illiquidity Introduce Fragility into Asset Prices? Evidence from the Corporate Bond Market","authors":"Hao Jiang, Yi Li, Zheng Sun, Ashley Wang","doi":"10.2139/ssrn.3501969","DOIUrl":"https://doi.org/10.2139/ssrn.3501969","url":null,"abstract":"Abstract Open-end corporate bond mutual funds invest in illiquid assets while providing liquid claims to shareholders. Does such liquidity transformation introduce fragility to the corporate bond market? To address this question, we create a novel bond-level latent fragility measure based on asset illiquidity of mutual funds holding the bond. We find that corporate bonds bearing higher fragility subsequently experience higher return volatility and more outflows-induced mutual fund selling over the period of 2006–2019. Using the COVID-19 crisis as a natural experiment, we find that bonds with higher precrisis fragility experienced more negative returns and larger reversals around March 2020.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116486762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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