AbstractThe following sections are included:Equity Risk Premiums: Importance and DeterminantsWhy Does the Equity Risk Premium Matter?A price for riskExpected returns and discount ratesInvestment and policy implicationsWhat are the Determinants of Equity Risk Premiums?Risk aversion and consumption preferencesEconomic riskInformationLiquidityCatastrophic riskGovernment policyThe behavioral/irrational componentThe Equity Risk Premium PuzzleEstimation ApproachesSurvey PremiumsInvestorsManagersAcademicsAcademicsEstimation questions and consequencesEstimates for the United StatesGlobal estimatesThe survivor biasHistorical Premium PlusSmall cap and other risk premiumsThe CAPM and market capitalizationThe Small Cap PremiumPerils of the approachCountry risk premiumsThe arguments for no country risk premiumThe arguments for a country risk premiumEstimating a Country Risk PremiumMeasuring Country RiskChoosing between the approachesImplied Equity PremiumsA Stable Growth DDM PremiumA Generalized Model: Implied Equity Risk PremiumImplied Equity Risk Premium: S&P 500Implied Equity Risk Premiums: Annual Estimates from 2008 to 2012A Term Structure for Equity Risk Premiums?Time Series Behavior for S&P 500 Implied PremiumImplied Equity Risk Premiums during a Market Crisis and BeyondDeterminants of Implied PremiumsImplied ERP and Interest ratesImplied ERP and Macroeconomic variablesImplied ERP, Earnings Yields and Dividend YieldsImplied ERP and Technical IndicatorsOther Equity MarketsSector premiumsFirm CharacteristicsChoosing an Equity Risk PremiumWhy Do the Approaches Yield Different Values?Which Approach is the “Best” Approach?Five Myths About Equity Risk PremiumsSummaryReferences
{"title":"Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2012 Edition","authors":"A. Damodaran","doi":"10.2139/ssrn.2027211","DOIUrl":"https://doi.org/10.2139/ssrn.2027211","url":null,"abstract":"AbstractThe following sections are included:Equity Risk Premiums: Importance and DeterminantsWhy Does the Equity Risk Premium Matter?A price for riskExpected returns and discount ratesInvestment and policy implicationsWhat are the Determinants of Equity Risk Premiums?Risk aversion and consumption preferencesEconomic riskInformationLiquidityCatastrophic riskGovernment policyThe behavioral/irrational componentThe Equity Risk Premium PuzzleEstimation ApproachesSurvey PremiumsInvestorsManagersAcademicsAcademicsEstimation questions and consequencesEstimates for the United StatesGlobal estimatesThe survivor biasHistorical Premium PlusSmall cap and other risk premiumsThe CAPM and market capitalizationThe Small Cap PremiumPerils of the approachCountry risk premiumsThe arguments for no country risk premiumThe arguments for a country risk premiumEstimating a Country Risk PremiumMeasuring Country RiskChoosing between the approachesImplied Equity PremiumsA Stable Growth DDM PremiumA Generalized Model: Implied Equity Risk PremiumImplied Equity Risk Premium: S&P 500Implied Equity Risk Premiums: Annual Estimates from 2008 to 2012A Term Structure for Equity Risk Premiums?Time Series Behavior for S&P 500 Implied PremiumImplied Equity Risk Premiums during a Market Crisis and BeyondDeterminants of Implied PremiumsImplied ERP and Interest ratesImplied ERP and Macroeconomic variablesImplied ERP, Earnings Yields and Dividend YieldsImplied ERP and Technical IndicatorsOther Equity MarketsSector premiumsFirm CharacteristicsChoosing an Equity Risk PremiumWhy Do the Approaches Yield Different Values?Which Approach is the “Best” Approach?Five Myths About Equity Risk PremiumsSummaryReferences","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"128 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87633035","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 test the predictive ability of investor sentiment on the return and volatility at the aggregate market level in the U.S., four largest European countries and three Asia-Pacific countries. We find that in the U.S., France and Italy periods of high consumer confidence levels are followed by low market returns. In Japan both the level and the change in consumer confidence boost the market return in the next month. Further, shifts in sentiment significantly move conditional volatility in most of the countries, and in Italy such impacts lead to an increase in returns by 4.7% in the next month.
{"title":"Predicting Stock Market Returns and Volatility with Investor Sentiment: Evidence from Eight Developed Countries","authors":"Jerry C. Ho, C. Hung","doi":"10.2139/ssrn.2279339","DOIUrl":"https://doi.org/10.2139/ssrn.2279339","url":null,"abstract":"We test the predictive ability of investor sentiment on the return and volatility at the aggregate market level in the U.S., four largest European countries and three Asia-Pacific countries. We find that in the U.S., France and Italy periods of high consumer confidence levels are followed by low market returns. In Japan both the level and the change in consumer confidence boost the market return in the next month. Further, shifts in sentiment significantly move conditional volatility in most of the countries, and in Italy such impacts lead to an increase in returns by 4.7% in the next month.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91525049","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 relative success of Australian and Canadian banks in weathering the Global Financial Crisis (GFC) has been noted by a number of commentators. Their earnings, capital levels and credit ratings have all been a source of envy for regulators of banks in Europe, America and the United Kingdom. The G-20 and the European Union have tried to identify the features of the Canadian and Australian financial systems which have underpinned this success in order to use them in shaping a revised international regulatory framework. Despite this perceived success, the impaired assets (also known as non-performing loans) of banks in both countries increased several fold over the GFC, and we investigate the determinants of this, using impaired assets as our measure of bank risk. Previous studies in other countries have tended to focus on the impact of bank specific factors, such as size and return on equity, in explaining bank risk. Our approach involves including those traditional variables, plus Distance to Default (DD), and a novel contagion variable, which is the effect of major global bank DD on Australian and Canadian banks. Using panel data regression over the period 1999-2008, we find that various balance sheet and income statement factors are not good explanatory variables for bank risk. In contrast, the contagion variable is significant in explaining Canadian and Australian bank risk, which suggests that prudential regulators should look to specifically allocate a portion of regulatory capital to deal with contagion effects.Classification-JEL:
{"title":"Survival of the Fittest: Contagion as a Determinant of Canadian and Australian Bank Risk","authors":"D. Allen, R. Boffey, R. Powell","doi":"10.2139/ssrn.1948288","DOIUrl":"https://doi.org/10.2139/ssrn.1948288","url":null,"abstract":"The relative success of Australian and Canadian banks in weathering the Global Financial Crisis (GFC) has been noted by a number of commentators. Their earnings, capital levels and credit ratings have all been a source of envy for regulators of banks in Europe, America and the United Kingdom. The G-20 and the European Union have tried to identify the features of the Canadian and Australian financial systems which have underpinned this success in order to use them in shaping a revised international regulatory framework. Despite this perceived success, the impaired assets (also known as non-performing loans) of banks in both countries increased several fold over the GFC, and we investigate the determinants of this, using impaired assets as our measure of bank risk. Previous studies in other countries have tended to focus on the impact of bank specific factors, such as size and return on equity, in explaining bank risk. Our approach involves including those traditional variables, plus Distance to Default (DD), and a novel contagion variable, which is the effect of major global bank DD on Australian and Canadian banks. Using panel data regression over the period 1999-2008, we find that various balance sheet and income statement factors are not good explanatory variables for bank risk. In contrast, the contagion variable is significant in explaining Canadian and Australian bank risk, which suggests that prudential regulators should look to specifically allocate a portion of regulatory capital to deal with contagion effects.Classification-JEL:","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90513053","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}
Using a broad data set of 20 US dollar exchange rates and order flow of institutional investors over 14 years, we construct a measure of global liquidity risk in the foreign exchange (FX) market. Our FX liquidity measure may be seen as the analog of the well-known Pastor–Stambaugh liquidity measure for the US stock market. We show that this measure has reasonable properties, and that there is a strong common component in liquidity across currencies. Finally, we provide evidence that liquidity risk is priced in the cross-section of currency returns, and estimate the liquidity risk premium in the FX market around 4.7 percent per annum.
{"title":"Global Liquidity Risk in the Foreign Exchange Market","authors":"C. Banti, Kate Phylaktis, Lucio Sarno","doi":"10.2139/ssrn.1954749","DOIUrl":"https://doi.org/10.2139/ssrn.1954749","url":null,"abstract":"Using a broad data set of 20 US dollar exchange rates and order flow of institutional investors over 14 years, we construct a measure of global liquidity risk in the foreign exchange (FX) market. Our FX liquidity measure may be seen as the analog of the well-known Pastor–Stambaugh liquidity measure for the US stock market. We show that this measure has reasonable properties, and that there is a strong common component in liquidity across currencies. Finally, we provide evidence that liquidity risk is priced in the cross-section of currency returns, and estimate the liquidity risk premium in the FX market around 4.7 percent per annum.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81606475","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}
Pub Date : 2011-10-02DOI: 10.1142/S0219024912500598
Tim Leung, Peng Liu
This paper studies the optimal timing to liquidate credit derivatives in a general intensity-based credit risk model under stochastic interest rate. We incorporate the potential price discrepancy between the market and investors, which is characterized by risk-neutral valuation under different default risk premia specifications. We quantify the value of optimally timing to sell through the concept of delayed liquidation premium, and analyze the associated probabilistic representation and variational inequality. We illustrate the optimal liquidation policy for both single-name and multi-name credit derivatives. Our model is extended to study the sequential buying and selling problem with and without short-sale constraint.
{"title":"Risk Premia and Optimal Liquidation of Credit Derivatives","authors":"Tim Leung, Peng Liu","doi":"10.1142/S0219024912500598","DOIUrl":"https://doi.org/10.1142/S0219024912500598","url":null,"abstract":"This paper studies the optimal timing to liquidate credit derivatives in a general intensity-based credit risk model under stochastic interest rate. We incorporate the potential price discrepancy between the market and investors, which is characterized by risk-neutral valuation under different default risk premia specifications. We quantify the value of optimally timing to sell through the concept of delayed liquidation premium, and analyze the associated probabilistic representation and variational inequality. We illustrate the optimal liquidation policy for both single-name and multi-name credit derivatives. Our model is extended to study the sequential buying and selling problem with and without short-sale constraint.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83797874","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}
Many practitioners annualize VaR just like the standard deviation. We show that this approach is incorrect, and a more sophisticated formula should be used for deriving a periodic VaR from parameters of the daily returns distribution. Another problem addressed here is the distribution of daily and periodic returns and its effect on VaR. While a fat-tailed distribution is more appropriate for modeling daily returns, we show that using the log-normal distribution is still a reasonable choice for modeling periodic returns and calculating a periodic VaR for holding periods of one month and longer.
{"title":"Periodic Value at Risk","authors":"V. Khokhlov","doi":"10.2139/ssrn.1976213","DOIUrl":"https://doi.org/10.2139/ssrn.1976213","url":null,"abstract":"Many practitioners annualize VaR just like the standard deviation. We show that this approach is incorrect, and a more sophisticated formula should be used for deriving a periodic VaR from parameters of the daily returns distribution. Another problem addressed here is the distribution of daily and periodic returns and its effect on VaR. While a fat-tailed distribution is more appropriate for modeling daily returns, we show that using the log-normal distribution is still a reasonable choice for modeling periodic returns and calculating a periodic VaR for holding periods of one month and longer.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73675085","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 define risk spillover as the dependence of a given asset variance on the past covariances and variances of other assets. Building on this idea, we propose the use of a highly flexible and tractable model to forecast the volatility of an international equity portfolio. According to the risk management strategy proposed, portfolio risk is seen as a specific combination of daily realized variances and covariances extracted from a high frequency dataset, which includes equities and currencies. In this framework, we focus on the risk spillovers across equities within the same sector (sector spillover), and from currencies to international equities (currency spillover). We compare these specific risk spillovers to a more general framework (full spillover) whereby we allow for lagged dependence across all variances and covariances. The forecasting analysis shows that considering only sector- and currency-risk spillovers, rather than full spillovers, improves performance, both in economic and statistical terms.
{"title":"Risk Spillovers in International Equity Portfolios","authors":"M. Bonato, M. Caporin, A. Ranaldo","doi":"10.2139/ssrn.1887624","DOIUrl":"https://doi.org/10.2139/ssrn.1887624","url":null,"abstract":"We define risk spillover as the dependence of a given asset variance on the past covariances and variances of other assets. Building on this idea, we propose the use of a highly flexible and tractable model to forecast the volatility of an international equity portfolio. According to the risk management strategy proposed, portfolio risk is seen as a specific combination of daily realized variances and covariances extracted from a high frequency dataset, which includes equities and currencies. In this framework, we focus on the risk spillovers across equities within the same sector (sector spillover), and from currencies to international equities (currency spillover). We compare these specific risk spillovers to a more general framework (full spillover) whereby we allow for lagged dependence across all variances and covariances. The forecasting analysis shows that considering only sector- and currency-risk spillovers, rather than full spillovers, improves performance, both in economic and statistical terms.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84463348","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 examines the role of idiosyncratic volatility in explaining the cross-sectional variation of size- and value-sorted portfolio returns. We show that the premium for bearing idiosyncratic volatility varies inversely with the number of stocks included in the portfolios. This conclusion is robust within various multifactor models based on size, value, past performance, liquidity and total volatility and also holds within an ICAPM specification of the risk–return relationship. Our findings thus indicate that investors demand an additional return for bearing the idiosyncratic volatility of poorly-diversified portfolios.
{"title":"Idiosyncratic Volatility and the Pricing of Poorly-Diversified Portfolios","authors":"J. Miffre, Chris Brooks, Xiafei Li","doi":"10.2139/ssrn.1855944","DOIUrl":"https://doi.org/10.2139/ssrn.1855944","url":null,"abstract":"This article examines the role of idiosyncratic volatility in explaining the cross-sectional variation of size- and value-sorted portfolio returns. We show that the premium for bearing idiosyncratic volatility varies inversely with the number of stocks included in the portfolios. This conclusion is robust within various multifactor models based on size, value, past performance, liquidity and total volatility and also holds within an ICAPM specification of the risk–return relationship. Our findings thus indicate that investors demand an additional return for bearing the idiosyncratic volatility of poorly-diversified portfolios.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80817591","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 demonstrates that liquidity risk as measured by the covariation of fund returns with unexpected changes in aggregate liquidity is an important predictor of hedge-fund performance. The results show that funds that significantly load on liquidity risk subsequently outperform low-loading funds by about 6.5% annually, on average, over the period 1994-2009, while negative performance is observed during liquidity crises. The returns are independent of share restriction, pointing to a possible imbalance between the liquidity a fund offers its investors and the liquidity of its underlying positions. Liquidity risk seems to account for a substantial part of hedge-fund performance. The results suggest several practical implications for risk management and manager selection.
{"title":"Hedge-Fund Performance and Liquidity Risk","authors":"Ronnie Sadka","doi":"10.2139/ssrn.1917118","DOIUrl":"https://doi.org/10.2139/ssrn.1917118","url":null,"abstract":"This paper demonstrates that liquidity risk as measured by the covariation of fund returns with unexpected changes in aggregate liquidity is an important predictor of hedge-fund performance. The results show that funds that significantly load on liquidity risk subsequently outperform low-loading funds by about 6.5% annually, on average, over the period 1994-2009, while negative performance is observed during liquidity crises. The returns are independent of share restriction, pointing to a possible imbalance between the liquidity a fund offers its investors and the liquidity of its underlying positions. Liquidity risk seems to account for a substantial part of hedge-fund performance. The results suggest several practical implications for risk management and manager selection.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91054262","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}
Pub Date : 2010-10-04DOI: 10.1504/IJMEF.2010.035595
I. Onour, B. Sergi
Using time-varying systematic risk model, the paper estimates risk in a number of stock markets in the Gulf Cooperation Council (GCC) countries, including Saudi, Kuwait, Dubai and Abu-Dhabi markets. The results in the paper indicate that Saudi market is the most perilous in the group, as it shows wider range of systematic risk. The paper also shows that the effect of S&P 500 is very minimal on GCC markets volatility, implying that internal factors are more important in the short term than external factors in volatility dynamics.
{"title":"GCC Stock Markets: How Risky are they?","authors":"I. Onour, B. Sergi","doi":"10.1504/IJMEF.2010.035595","DOIUrl":"https://doi.org/10.1504/IJMEF.2010.035595","url":null,"abstract":"Using time-varying systematic risk model, the paper estimates risk in a number of stock markets in the Gulf Cooperation Council (GCC) countries, including Saudi, Kuwait, Dubai and Abu-Dhabi markets. The results in the paper indicate that Saudi market is the most perilous in the group, as it shows wider range of systematic risk. The paper also shows that the effect of S&P 500 is very minimal on GCC markets volatility, implying that internal factors are more important in the short term than external factors in volatility dynamics.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"86 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2010-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80678848","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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