Abstract This paper studies the impact of time to build on the term structure of interest rates in an otherwise standard (Cox et al., 1985a; Cox et al., 1985b, CIR) production economy. Due to time to build, production depends not only on the current business condition as in the original CIR, but also on past conditions over the production period. This causes equilibrium quantities, including the short rate, forward rates, and bond returns, to depend on the historical path of the production opportunities. Production delay that accumulates uncertainty over the time to build generates significant time variations in bond risk premia. Bond returns can be predicted by current forward rates, as well as their lagged values, since current market states not only affect the current short rate but also the short rate in a distant future. Due to the path dependence, risk premia cannot be fully spanned by current yields. We find evidence that time to build improves the ability of the CIR in generating empirical facts.
本文在另一种标准下研究了时间构建对利率期限结构的影响(Cox et al.， 1985;Cox et al.， 1985b, CIR)生产经济。由于构建时间的原因，生产不仅取决于原始CIR中当前的业务状况，还取决于生产期间的过去状况。这导致均衡数量，包括短期利率、远期利率和债券回报，取决于生产机会的历史路径。随着建造时间的推移，不确定性不断累积的生产延迟会导致债券风险溢价的显著时间变化。债券收益可以通过当前远期利率及其滞后值来预测，因为当前市场状态不仅影响当前短期利率，还会影响遥远未来的短期利率。由于路径依赖，风险溢价不能被当前收益率完全跨越。我们发现证据表明，构建时间提高了CIR生成经验事实的能力。
{"title":"Time to Build and Bond Risk Premia","authors":"Binbin Guo, F. Huang, Kai Li","doi":"10.2139/ssrn.3709118","DOIUrl":"https://doi.org/10.2139/ssrn.3709118","url":null,"abstract":"Abstract This paper studies the impact of time to build on the term structure of interest rates in an otherwise standard (Cox et al., 1985a; Cox et al., 1985b, CIR) production economy. Due to time to build, production depends not only on the current business condition as in the original CIR, but also on past conditions over the production period. This causes equilibrium quantities, including the short rate, forward rates, and bond returns, to depend on the historical path of the production opportunities. Production delay that accumulates uncertainty over the time to build generates significant time variations in bond risk premia. Bond returns can be predicted by current forward rates, as well as their lagged values, since current market states not only affect the current short rate but also the short rate in a distant future. Due to the path dependence, risk premia cannot be fully spanned by current yields. We find evidence that time to build improves the ability of the CIR in generating empirical facts.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75218542","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}
The Bloomberg sovereign risk function (SRSK) provides quantitative estimates of a sovereign entity's default probability (DP) term structure. The SRSK model was last updated in June 2017. This year we have reviewed and revised the model. This white paper documents the newly updated DP model and analyzes its performance.
{"title":"SRSK - The Bloomberg Sovereign Risk Model","authors":"Lili Cai, Harvey J. Stein","doi":"10.2139/ssrn.3911338","DOIUrl":"https://doi.org/10.2139/ssrn.3911338","url":null,"abstract":"The Bloomberg sovereign risk function (SRSK) provides quantitative estimates of a sovereign entity's default probability (DP) term structure. The SRSK model was last updated in June 2017. This year we have reviewed and revised the model. This white paper documents the newly updated DP model and analyzes its performance.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76910132","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}
Market fragmentation and technological advances increasing the speed of trading altered the functioning and stability of global equity limit order markets. Taking market resiliency as an indicator of market quality, we investigate how resilient are trading venues in a high-frequency environment with cross-venue fragmented order flow. Employing a Hawkes process methodology on high-frequency data for FTSE 100 stocks on LSE, a traditional exchange, and on Chi-X, an alternative venue, we find that when liquidity becomes scarce Chi-X is a less resilient venue than LSE with variations existing across stocks and time. In comparison with LSE, Chi-X has more, longer, and severer liquidity shocks. Whereas the vast majority of liquidity droughts on both venues disappear within less than one minute, the recovery is not lasting, as liquidity shocks spiral over the time dimension. Over half of the shocks on both venues are caused by spiralling. Liquidity shocks tend to spiral more on Chi-X than on LSE for large stocks suggesting that the liquidity supply on Chi-X is thinner than on LSE. Finally, a significant amount of liquidity shocks spill over cross-venue providing supporting evidence for the competition for order flow between LSE and Chi-X.
{"title":"Resiliency: Cross-Venue Dynamics with Hawkes Processes","authors":"L. Pelizzon, Satchit Sagade, Katia Vozian","doi":"10.2139/ssrn.3711976","DOIUrl":"https://doi.org/10.2139/ssrn.3711976","url":null,"abstract":"Market fragmentation and technological advances increasing the speed of trading altered the functioning and stability of global equity limit order markets. Taking market resiliency as an indicator of market quality, we investigate how resilient are trading venues in a high-frequency environment with cross-venue fragmented order flow. Employing a Hawkes process methodology on high-frequency data for FTSE 100 stocks on LSE, a traditional exchange, and on Chi-X, an alternative venue, we find that when liquidity becomes scarce Chi-X is a less resilient venue than LSE with variations existing across stocks and time. In comparison with LSE, Chi-X has more, longer, and severer liquidity shocks. Whereas the vast majority of liquidity droughts on both venues disappear within less than one minute, the recovery is not lasting, as liquidity shocks spiral over the time dimension. Over half of the shocks on both venues are caused by spiralling. Liquidity shocks tend to spiral more on Chi-X than on LSE for large stocks suggesting that the liquidity supply on Chi-X is thinner than on LSE. Finally, a significant amount of liquidity shocks spill over cross-venue providing supporting evidence for the competition for order flow between LSE and Chi-X.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":"176 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78558558","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 article provides a reasonable explanation for Exxonmobils unprecedented expulsion from the Dow Jones Industrial Average on August 31, 2020 based on the Kondratieff Wave Concept and Capital Overaccumulation Theory. Authors of this paper suggest that the crisis in the oil market in 2020, triggered by the coronavirus, was expected and could be explained by the economic origins of development, namely, the change of the 5th technological mode to the 6th one. Taking into account the fact that oil is the main energy source of the 5th technological mode, it is obvious that its change will affect the oil market. At the same time, the process of the oil market losing its positions is not spontaneous, since the intensive development of new technologies and a sharp increase in renewable energy investments have led to a decrease in oil consumption.
{"title":"Fundamental Reasons for Exxonmobil's Expulsion from the Dow Jones Industrial Average on Aug 31, 2020: Lessons and Implications for the Global Oil Market and Kazakhstan","authors":"Zhaksybek A. Kulekeyev, Yevgeniya Pak","doi":"10.2139/ssrn.3700733","DOIUrl":"https://doi.org/10.2139/ssrn.3700733","url":null,"abstract":"This article provides a reasonable explanation for Exxonmobils unprecedented expulsion from the Dow Jones Industrial Average on August 31, 2020 based on the Kondratieff Wave Concept and Capital Overaccumulation Theory. \u0000Authors of this paper suggest that the crisis in the oil market in 2020, triggered by the coronavirus, was expected and could be explained by the economic origins of development, namely, the change of the 5th technological mode to the 6th one. Taking into account the fact that oil is the main energy source of the 5th technological mode, it is obvious that its change will affect the oil market. At the same time, the process of the oil market losing its positions is not spontaneous, since the intensive development of new technologies and a sharp increase in renewable energy investments have led to a decrease in oil consumption.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":"122 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75958871","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 a stock market crash is defined as the period from an index's prior peak until its recovery, crashes demonstrate empirical regularities in their scale and timing. For instance, measures of the duration, maximum decline, and lost value of crashes are very highly correlated. These correlations suggest that crashes belong to well-defined categories based on their size and become increasingly predictable as they progress. Accordingly, I advance four stock market crash categories, which are logarithmic in size. Crashes then range from small scale market disturbances like 'flash crashes' in Category 1 to the Wall Street Crash of 1929, America's sole Category 4. Furthermore, I find that U.S. stock markets are bimodal, switching between crashes and booms, and that this switching is regular. Specifically, I find that either a Category 2 or 3 crash occurs every four years, with a variance of just two years. Moreover, by definition, growth during a crash is close to zero. During boom periods, however, the average annual growth rate is 21.5%. Together, these results suggest a new foundation for examining patterns of returns and other characteristics of stock markets.
{"title":"Empirical Regularities in Stock Market Crashes","authors":"Edward J. Egan","doi":"10.2139/ssrn.3679630","DOIUrl":"https://doi.org/10.2139/ssrn.3679630","url":null,"abstract":"When a stock market crash is defined as the period from an index's prior peak until its recovery, crashes demonstrate empirical regularities in their scale and timing. For instance, measures of the duration, maximum decline, and lost value of crashes are very highly correlated. These correlations suggest that crashes belong to well-defined categories based on their size and become increasingly predictable as they progress. Accordingly, I advance four stock market crash categories, which are logarithmic in size. Crashes then range from small scale market disturbances like 'flash crashes' in Category 1 to the Wall Street Crash of 1929, America's sole Category 4. Furthermore, I find that U.S. stock markets are bimodal, switching between crashes and booms, and that this switching is regular. Specifically, I find that either a Category 2 or 3 crash occurs every four years, with a variance of just two years. Moreover, by definition, growth during a crash is close to zero. During boom periods, however, the average annual growth rate is 21.5%. Together, these results suggest a new foundation for examining patterns of returns and other characteristics of stock markets.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72675092","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}
We study the effects of corporate social responsibility on credit risk for U.S. and European firms over the period 2003 to 2018. Differentiating between the various facets of corporate social responsibility shows that only environmental aspects reduce different measures of credit risk for U.S. firms, whereas both environmental and social aspects do so for European firms. Surprisingly, we find that credit ratings do not reflect these credit-risk reducing effects of corporate social responsibility. Our results are robust against different estimation methods.
{"title":"Corporate Social Responsibility and Credit Risk","authors":"C. Bannier, Yannik Bofinger, Björn Rock","doi":"10.2139/ssrn.3686025","DOIUrl":"https://doi.org/10.2139/ssrn.3686025","url":null,"abstract":"We study the effects of corporate social responsibility on credit risk for U.S. and European firms over the period 2003 to 2018. Differentiating between the various facets of corporate social responsibility shows that only environmental aspects reduce different measures of credit risk for U.S. firms, whereas both environmental and social aspects do so for European firms. Surprisingly, we find that credit ratings do not reflect these credit-risk reducing effects of corporate social responsibility. Our results are robust against different estimation methods.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80590192","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 analyzes how systemic risk structurally evolved between 2007 and 2017. The main contributions of the paper to the literature include the methodology, analysis and potential use for macroprudential policies. The methodology, known as network analysis, comprises direct (credit and liquidity risk) and indirect (concentration risk) contagion channels as well as other specificities that improve the methodologies exploited so far in the literature. Using a consolidated sample, which varies between 14 and 17 banks over the period 2007–17, we show that the structural systemic risk of the Portuguese banking system reduced between 2007 and 2017. Further, in line with most of the literature, this paper highlights that direct contagion is not significant compared with contagion that stems from banks’ common exposures to asset classes. Finally, this paper supports the role played by capital in mitigating structural systemic risk, and the model behind the analysis can be used to perform stress tests with a macroprudential dimension as well as to calibrate structural capital buffers such as the other systemically important institutions and systemic risk buffers.
{"title":"Structural Systemic Risk: Evolution and Main Drivers","authors":"Nuno Azevedo, Vítor Oliveira","doi":"10.21314/jntf.2019.059","DOIUrl":"https://doi.org/10.21314/jntf.2019.059","url":null,"abstract":"This paper analyzes how systemic risk structurally evolved between 2007 and 2017. The main contributions of the paper to the literature include the methodology, analysis and potential use for macroprudential policies. The methodology, known as network analysis, comprises direct (credit and liquidity risk) and indirect (concentration risk) contagion channels as well as other specificities that improve the methodologies exploited so far in the literature. Using a consolidated sample, which varies between 14 and 17 banks over the period 2007–17, we show that the structural systemic risk of the Portuguese banking system reduced between 2007 and 2017. Further, in line with most of the literature, this paper highlights that direct contagion is not significant compared with contagion that stems from banks’ common exposures to asset classes. Finally, this paper supports the role played by capital in mitigating structural systemic risk, and the model behind the analysis can be used to perform stress tests with a macroprudential dimension as well as to calibrate structural capital buffers such as the other systemically important institutions and systemic risk buffers.<br>","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81826004","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}
Erdinç Akyıldırım, S. Corbet, D. K. Nguyen, A. Şensoy
Abstract We estimate the time-varying long-run correlations of European sovereign bond markets to identify specific effects that are attributed to changing European regulatory and political dynamics over the last twenty years. Our empirical results from using the DCC-MIDAS methodology indicate that regulatory changes in Europe have created significant and negative impact on the long-run correlations within the month where the regulation is decided to be taken into action. This impact still remains in the following months and robust with respect to the trend component of the long-run correlations. A direct implication is that the more regulations the EU attempts to put in place, the lower the long-run convergence process of sovereign bond markets is. We then analyse the structural shifts in the long-run correlation dynamics with penalized contrasts methodology and try to find out the reasons of these severe changes. Accordingly, some of the structural shifts overlap with the dates of a limited number of regulatory changes, in addition to the major global economic and political events.
{"title":"Regulatory Changes and Long-run Relationships of the EMU Sovereign Debt Markets: Implications for Future Policy Framework","authors":"Erdinç Akyıldırım, S. Corbet, D. K. Nguyen, A. Şensoy","doi":"10.2139/ssrn.3758499","DOIUrl":"https://doi.org/10.2139/ssrn.3758499","url":null,"abstract":"Abstract We estimate the time-varying long-run correlations of European sovereign bond markets to identify specific effects that are attributed to changing European regulatory and political dynamics over the last twenty years. Our empirical results from using the DCC-MIDAS methodology indicate that regulatory changes in Europe have created significant and negative impact on the long-run correlations within the month where the regulation is decided to be taken into action. This impact still remains in the following months and robust with respect to the trend component of the long-run correlations. A direct implication is that the more regulations the EU attempts to put in place, the lower the long-run convergence process of sovereign bond markets is. We then analyse the structural shifts in the long-run correlation dynamics with penalized contrasts methodology and try to find out the reasons of these severe changes. Accordingly, some of the structural shifts overlap with the dates of a limited number of regulatory changes, in addition to the major global economic and political events.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85040139","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 study aims to study the effect of global markets and macroeconomics on joint stock price movements. This research was conducted at the Indonesia Stock Exchange with the period 2014-2018. The model used in this study uses the VAR/VECM method with the results of the DJIA Variable there is a significant influence on the movement of the CSPI, this means that an increase in the Dow Jones index will have an effect on increasing the value of the CSPI. Significantly, there was no influence between the NIKKEI225 variable on the movement of the CSPI because t-statistics were greater than t-tables at the coefficient level. The results of the STI index influence, the value of t-statistics in the short term the effect of the STI shows that the STI has a significant effect on the CSPI, this is indicated by t-statistics smaller than the t-table. Inflation research results, in the short term, there is a significant influence between inflation variables on the movement of the CSPI. BiRate has a significant influence on the CSPI with the t-statistic value in the short term is smaller than the t-table, meaning that in the short term the BiRate increase of 1% will affect the movement of the Composite Stock Price Index (CSPI). The t-statistic value in the short term variable USD/IDR exchange rate has a positive effect on the movement of the CSPI. This means that an increase in the exchange rate (IDR/USD) will have an effect on increasing the value of the CSPI and conversely a decrease in the exchange rate (IDR/USD) will have an effect of reducing the value of the CSPI.
{"title":"Dynamic Movement of Indonesian Stock Exchanges: Analysis of Global Stock Exchanges and Macroeconomic Variables","authors":"Endri Endri","doi":"10.2139/ssrn.3669773","DOIUrl":"https://doi.org/10.2139/ssrn.3669773","url":null,"abstract":"This study aims to study the effect of global markets and macroeconomics on joint stock price movements. This research was conducted at the Indonesia Stock Exchange with the period 2014-2018. The model used in this study uses the VAR/VECM method with the results of the DJIA Variable there is a significant influence on the movement of the CSPI, this means that an increase in the Dow Jones index will have an effect on increasing the value of the CSPI. Significantly, there was no influence between the NIKKEI225 variable on the movement of the CSPI because t-statistics were greater than t-tables at the coefficient level. The results of the STI index influence, the value of t-statistics in the short term the effect of the STI shows that the STI has a significant effect on the CSPI, this is indicated by t-statistics smaller than the t-table. Inflation research results, in the short term, there is a significant influence between inflation variables on the movement of the CSPI. BiRate has a significant influence on the CSPI with the t-statistic value in the short term is smaller than the t-table, meaning that in the short term the BiRate increase of 1% will affect the movement of the Composite Stock Price Index (CSPI). The t-statistic value in the short term variable USD/IDR exchange rate has a positive effect on the movement of the CSPI. This means that an increase in the exchange rate (IDR/USD) will have an effect on increasing the value of the CSPI and conversely a decrease in the exchange rate (IDR/USD) will have an effect of reducing the value of the CSPI.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89919993","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}
A one-factor model based on long-run consumption growth explains the risk premiums on corporate bond portfolios sorted on credit rating, credit spreads, downside risk, idiosyncratic volatility, long-term reversals, maturity, and sensitivity to the financial intermediary capital factor. The estimated risk-aversion coefficient is lower when we use the consumption growth of wealthy households over a longer horizon as a risk factor, and a model with a 20-quarter horizon yields a risk-aversion coefficient of 15, a value similar to the one estimated from equity portfolios. This paper was accepted by Bruno Biais, finance. Funding: Y. Nozawa acknowledges funding from the Center for Investing at the Hong Kong University and Science and Technology. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4784 .
{"title":"A One-Factor Model of Corporate Bond Premia","authors":"Redouane Elkamhi, Chanik Jo, Yoshio Nozawa","doi":"10.2139/ssrn.3669068","DOIUrl":"https://doi.org/10.2139/ssrn.3669068","url":null,"abstract":"A one-factor model based on long-run consumption growth explains the risk premiums on corporate bond portfolios sorted on credit rating, credit spreads, downside risk, idiosyncratic volatility, long-term reversals, maturity, and sensitivity to the financial intermediary capital factor. The estimated risk-aversion coefficient is lower when we use the consumption growth of wealthy households over a longer horizon as a risk factor, and a model with a 20-quarter horizon yields a risk-aversion coefficient of 15, a value similar to the one estimated from equity portfolios. This paper was accepted by Bruno Biais, finance. Funding: Y. Nozawa acknowledges funding from the Center for Investing at the Hong Kong University and Science and Technology. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4784 .","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79874706","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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