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Essays in Financial Econometrics.

Custovic, Anessa.

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Essays in Financial Econometrics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays in Financial Econometrics./
作者:	Custovic, Anessa.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	117 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-01, Section: A.
Contained By:	Dissertations Abstracts International82-01A.
標題:	Economics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27831728
ISBN:	9798641218342

Essays in Financial Econometrics.
Custovic, Anessa.

Essays in Financial Econometrics. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 117 p.

Source: Dissertations Abstracts International, Volume: 82-01, Section: A.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2020.

This item must not be sold to any third party vendors.

The first essay evaluates the predictive ability of a high frequency dividend price ratio. I show that we can uncover both dividend growth and return predictability across equity markets by weighting the dividends in each month differently through the application of MIDAS regressions. For US equity markets, I find strong predictability of long horizon real dividend growth with the theoretically correct sign and show that there is some predictability with nominal dividend growth as well. Out-of-sample I find that the high frequency constructed dividend price ratio can result in superior predictability for returns over the annual single frequency dividend price ratio. When combined with other predictors, the high frequency constructed dividend price ratio results in even better out-of-sample predictability for both returns and the equity premium. The out-of-sample performance is robust to horizons as well.In the second essay, a joint work with Eric Ghysels and Christian Conrad, we use the GARCH- MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility. The finding is atypical for volatility co-movements across financial markets. Moreover, we find that the S&P 500 volatility risk premium has a significantly positive effect on long-term Bitcoin volatility. Finally, we find a strong positive association between the Baltic dry index and long-term Bitcoin volatility. This result shows that Bitcoin volatility is closely linked to global economic activity. Overall, our findings can be used to construct improved forecasts of long-term Bitcoin volatility.In the third essay, a joint work with Mike Aguilar, economic forecasts are often disseminated via a survey of professionals (i.e. "Consensus"). In this essay we compare and contrast the Consensus with a crowdsourced alternative wherein anyone may submit a forecast. We focus on U.S. Nonfarm Payrolls and find that, on average, Consensus is more accurate, but the best crowdsourced forecasters are superior to the best Consensus forecasters. We also find that information plays a key role. When the Consensus is uncertain and herds together, the crowdsourced forecasts appear to be more accurate. Our findings provide evidence that crowdsourcing might provide a valuable supplement to traditional macroeconomic forecasts.

ISBN: 9798641218342Subjects--Topical Terms:

517137
Economics.
Subjects--Index Terms:

Cryptocurrencies

Essays in Financial Econometrics.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 117 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-01, Section: A.
500 $a Advisor: Ghysels, Eric.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a The first essay evaluates the predictive ability of a high frequency dividend price ratio. I show that we can uncover both dividend growth and return predictability across equity markets by weighting the dividends in each month differently through the application of MIDAS regressions. For US equity markets, I find strong predictability of long horizon real dividend growth with the theoretically correct sign and show that there is some predictability with nominal dividend growth as well. Out-of-sample I find that the high frequency constructed dividend price ratio can result in superior predictability for returns over the annual single frequency dividend price ratio. When combined with other predictors, the high frequency constructed dividend price ratio results in even better out-of-sample predictability for both returns and the equity premium. The out-of-sample performance is robust to horizons as well.In the second essay, a joint work with Eric Ghysels and Christian Conrad, we use the GARCH- MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility. The finding is atypical for volatility co-movements across financial markets. Moreover, we find that the S&P 500 volatility risk premium has a significantly positive effect on long-term Bitcoin volatility. Finally, we find a strong positive association between the Baltic dry index and long-term Bitcoin volatility. This result shows that Bitcoin volatility is closely linked to global economic activity. Overall, our findings can be used to construct improved forecasts of long-term Bitcoin volatility.In the third essay, a joint work with Mike Aguilar, economic forecasts are often disseminated via a survey of professionals (i.e. "Consensus"). In this essay we compare and contrast the Consensus with a crowdsourced alternative wherein anyone may submit a forecast. We focus on U.S. Nonfarm Payrolls and find that, on average, Consensus is more accurate, but the best crowdsourced forecasters are superior to the best Consensus forecasters. We also find that information plays a key role. When the Consensus is uncertain and herds together, the crowdsourced forecasts appear to be more accurate. Our findings provide evidence that crowdsourcing might provide a valuable supplement to traditional macroeconomic forecasts.
590 $a School code: 0153.
650 4 $a Economics. $3 517137
650 4 $a Economic theory. $3 1556984
650 4 $a Finance. $3 542899
653 $a Cryptocurrencies
653 $a Equity
653 $a Forecasting
690 $a 0501
690 $a 0511
690 $a 0508
710 2 $a The University of North Carolina at Chapel Hill. $b Economics. $3 1022423
773 0 $t Dissertations Abstracts International $g 82-01A.
790 $a 0153
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27831728