This paper This paper This paper This paper presents presents presents a fractionally cointegrata fractionally cointegrata fractionally cointegrat a fractionally cointegrata fractionally cointegrata fractionally cointegrat a fractionally cointegrat a fractionally cointegrat a fractionally cointegrata fractionally cointegrata fractionally cointegrata fractionally cointegrat a fractionally cointegrata fractionally cointegrat a fractionally cointegrated vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression (FCVAR) (FCVAR) (FCVAR) (FCVAR) model to examine to examine to examine to examine to examine to examine to examine various relations various relations various relations various relations various relations between stock returns and downside risk between stock returns and downside risk between stock returns and downside riskbetween stock returns and downside risk between stock returns and downside risk between stock returns and downside risk between stock returns and downside risk between stock returns and downside riskbetween stock returns and downside risk between stock returns and downside risk between stock returns and downside risk between stock returns and downside risk . Evidence from major advance Evidence from major advance Evidence from major advanceEvidence from major advanceEvidence from major advance Evidence from major advanceEvidence from major advance Evidence from major advance Evidence from major advanceEvidence from major advanceEvidence from major advance Evidence from major advance Evidence from major advanced markets markets markets markets markets supports the supports the notion that notion that notion that downside risk measured by measured by measured by measured by measured by measured by measured by value value value-at -risk ( risk (VaRVaRVaR) has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information content content that reflects that reflects that reflects that reflects that reflects lagged long lagged long lagged longlagged long lagged long -run variance and run variance and run variance and run variance and run variance and run variance and run variance and run variance and run variance and higher momentshigher moments higher moments higher moments higher moments higher momentshigher moments of risk for for predict redict ing stock returns. stock returns. stock returns. stock returns. The e The e vidence vidence vidence supports the positive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesis positive tra
{"title":"Downside Risk and Stock Returns: An Empirical Analysis of the Long-Run and Short-Run Dynamics from the G-7 Countries","authors":"C. Chen, T. Chiang, W. Härdle","doi":"10.2139/ssrn.2800951","DOIUrl":"https://doi.org/10.2139/ssrn.2800951","url":null,"abstract":"This paper This paper This paper This paper presents presents presents a fractionally cointegrata fractionally cointegrata fractionally cointegrat a fractionally cointegrata fractionally cointegrata fractionally cointegrat a fractionally cointegrat a fractionally cointegrat a fractionally cointegrata fractionally cointegrata fractionally cointegrata fractionally cointegrat a fractionally cointegrata fractionally cointegrat a fractionally cointegrated vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression (FCVAR) (FCVAR) (FCVAR) (FCVAR) model to examine to examine to examine to examine to examine to examine to examine various relations various relations various relations various relations various relations between stock returns and downside risk between stock returns and downside risk between stock returns and downside riskbetween stock returns and downside risk between stock returns and downside risk between stock returns and downside risk between stock returns and downside risk between stock returns and downside riskbetween stock returns and downside risk between stock returns and downside risk between stock returns and downside risk between stock returns and downside risk . Evidence from major advance Evidence from major advance Evidence from major advanceEvidence from major advanceEvidence from major advance Evidence from major advanceEvidence from major advance Evidence from major advance Evidence from major advanceEvidence from major advanceEvidence from major advance Evidence from major advance Evidence from major advanced markets markets markets markets markets supports the supports the notion that notion that notion that downside risk measured by measured by measured by measured by measured by measured by measured by value value value-at -risk ( risk (VaRVaRVaR) has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information content content that reflects that reflects that reflects that reflects that reflects lagged long lagged long lagged longlagged long lagged long -run variance and run variance and run variance and run variance and run variance and run variance and run variance and run variance and run variance and higher momentshigher moments higher moments higher moments higher moments higher momentshigher moments of risk for for predict redict ing stock returns. stock returns. stock returns. stock returns. The e The e vidence vidence vidence supports the positive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesis positive tra","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82652812","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 find that easy-to-observe price ranges are useful for estimating intraday liquidity. Following the literature on range-based volatility estimators, we go beyond the use of the closing price only and rely on the full range of prices. Based on high, low, opening, and closing (HLOC) prices, we show that a greater intensity in the price discovery process (as measured by the open–close range) and a higher level of price uncertainty (as captured by the High–Low range) lower ex-ante liquidity for small, mid, and large caps. Realized volatility (RV) fails to capture these effects. Although order books have become increasingly difficult to treat, there is some good news: it has never been easier to look at price ranges.
{"title":"On the Usefulness of Intraday Price Ranges to Gauge Liquidity in Cap-Based Portfolios","authors":"P. Mazza, M. Petitjean","doi":"10.2139/ssrn.2294444","DOIUrl":"https://doi.org/10.2139/ssrn.2294444","url":null,"abstract":"We find that easy-to-observe price ranges are useful for estimating intraday liquidity. Following the literature on range-based volatility estimators, we go beyond the use of the closing price only and rely on the full range of prices. Based on high, low, opening, and closing (HLOC) prices, we show that a greater intensity in the price discovery process (as measured by the open–close range) and a higher level of price uncertainty (as captured by the High–Low range) lower ex-ante liquidity for small, mid, and large caps. Realized volatility (RV) fails to capture these effects. Although order books have become increasingly difficult to treat, there is some good news: it has never been easier to look at price ranges.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82985024","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}
In this paper, we look at the effect of the financial crisis from an angle overlooked to date in the finance literature by investigating composition effects arising from the financial crisis. A composition effect is a change in the market risk of a sector that is caused not by a direct change in that sector but by a change in another sector that affects the composition of the stock market. In the paper we investigate the pre and during crisis market risk of the industrial, banking and utilities sectors. Amongst other results, we find a positive relationship across the G12 countries between the increase in the market risk of industrials during the crisis and both the pre-crisis market risk of the banking sector and the scale of the systemic crisis in a country. The six G12 countries that experienced a major systematic banking crisis are amongst the seven countries with the largest increases in the market risk for industrials. Results drawn from our detailed analysis using US data are consistent with these findings. Finally, we show how the results add to our understanding of the linkages between the financial and real sector and conclude that composition effects of the financial crisis could have a significant chilling effect on investment in industrials, which is in addition to the effect of other linkages already documented.
{"title":"Stock Market Risk in the Financial Crisis","authors":"P. Grout, A. Zalewska","doi":"10.2139/ssrn.2569057","DOIUrl":"https://doi.org/10.2139/ssrn.2569057","url":null,"abstract":"In this paper, we look at the effect of the financial crisis from an angle overlooked to date in the finance literature by investigating composition effects arising from the financial crisis. A composition effect is a change in the market risk of a sector that is caused not by a direct change in that sector but by a change in another sector that affects the composition of the stock market. In the paper we investigate the pre and during crisis market risk of the industrial, banking and utilities sectors. Amongst other results, we find a positive relationship across the G12 countries between the increase in the market risk of industrials during the crisis and both the pre-crisis market risk of the banking sector and the scale of the systemic crisis in a country. The six G12 countries that experienced a major systematic banking crisis are amongst the seven countries with the largest increases in the market risk for industrials. Results drawn from our detailed analysis using US data are consistent with these findings. Finally, we show how the results add to our understanding of the linkages between the financial and real sector and conclude that composition effects of the financial crisis could have a significant chilling effect on investment in industrials, which is in addition to the effect of other linkages already documented.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80873144","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}
Peter G. Klein, D. Purdy, Isaac Schweigert, Alexander Vedrashko
We analyze the risk and return characteristics of Canadian hedge funds based on a comprehensive database we compiled. We find that Canadian hedge funds have higher risk-adjusted performance and different distributional characteristics relative to the global hedge fund indices. We investigate market timing by Canadian hedge funds and find that they do not time the Canadian or global stock and bond markets, but hedge funds in the Managed Futures strategy group time the commodity market. These results are robust to parameter instability and structural changes in the model. We also illustrate the impact of using local and global risk factors to analyze the performance of local investment firms.
{"title":"The Canadian Hedge Fund Industry: Performance and Market Timing","authors":"Peter G. Klein, D. Purdy, Isaac Schweigert, Alexander Vedrashko","doi":"10.2139/ssrn.2002649","DOIUrl":"https://doi.org/10.2139/ssrn.2002649","url":null,"abstract":"We analyze the risk and return characteristics of Canadian hedge funds based on a comprehensive database we compiled. We find that Canadian hedge funds have higher risk-adjusted performance and different distributional characteristics relative to the global hedge fund indices. We investigate market timing by Canadian hedge funds and find that they do not time the Canadian or global stock and bond markets, but hedge funds in the Managed Futures strategy group time the commodity market. These results are robust to parameter instability and structural changes in the model. We also illustrate the impact of using local and global risk factors to analyze the performance of local investment firms.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"187 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72794645","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}
If two investments have the same payoff covariance with the market but one has higher expected payoff, which asset according to the CAPM has most risk? One answer is that as far as risk goes the two assets are the same, because they have the same covariance with the market. The correct answer, pointed out nearly four decades ago by Eugene Fama, but long overlooked, is that investments have the same risk, the same returns beta and the same CAPM discount rate if and only if they have the same ratio of ex ante payoff covariance to payoff mean. This insight clarifies much of the conventional wisdom that surrounds capital budgeting and "risk adjusted" discount rates, while also displaying the mechanics by which information arrival affects the CAPM cost of capital.
{"title":"When are Investment Projects in the Same Risk Class?","authors":"D. Johnstone","doi":"10.1111/acfi.12157","DOIUrl":"https://doi.org/10.1111/acfi.12157","url":null,"abstract":"If two investments have the same payoff covariance with the market but one has higher expected payoff, which asset according to the CAPM has most risk? One answer is that as far as risk goes the two assets are the same, because they have the same covariance with the market. The correct answer, pointed out nearly four decades ago by Eugene Fama, but long overlooked, is that investments have the same risk, the same returns beta and the same CAPM discount rate if and only if they have the same ratio of ex ante payoff covariance to payoff mean. This insight clarifies much of the conventional wisdom that surrounds capital budgeting and \"risk adjusted\" discount rates, while also displaying the mechanics by which information arrival affects the CAPM cost of capital.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91204340","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}
Applying the variance decomposition developed by Asdrubali et al. (1996), the paper explores for the first time the role and the extent of smoothing channels at a micro level using a sample of UK households. Our empirical analysis of British Household Panel Survey (BHPS) data concludes that the bulk of risk-sharing in the UK is driven by the savings channel. By allowing for risk aversion heterogeneity, we detect an inverted U-shaped relationship between risk aversion and the extent of smoothing achieved through financial markets. We also analyze the issue of risk-sharing by income, education levels and by region. We find that risk-sharing is more effective (higher) for individuals whose savings are more flexible, while it is less effective (lower) for individuals characterized by relatively more stable savings rate (like the Scottish population), regardless of their economic conditions.
{"title":"Channels of Risk-Sharing at Micro Level: Savings, Investments and the Risk Aversion Heterogeneity","authors":"Faruk Balli, E. Pierucci, F. Pericoli","doi":"10.2139/ssrn.2546107","DOIUrl":"https://doi.org/10.2139/ssrn.2546107","url":null,"abstract":"Applying the variance decomposition developed by Asdrubali et al. (1996), the paper explores for the first time the role and the extent of smoothing channels at a micro level using a sample of UK households. Our empirical analysis of British Household Panel Survey (BHPS) data concludes that the bulk of risk-sharing in the UK is driven by the savings channel. By allowing for risk aversion heterogeneity, we detect an inverted U-shaped relationship between risk aversion and the extent of smoothing achieved through financial markets. We also analyze the issue of risk-sharing by income, education levels and by region. We find that risk-sharing is more effective (higher) for individuals whose savings are more flexible, while it is less effective (lower) for individuals characterized by relatively more stable savings rate (like the Scottish population), regardless of their economic conditions.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78605065","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 spread risk premium component of credit default swap (CDS) spreads represents a compensation demanded by protection sellers for future changes in CDS spreads caused by unpredictable fluctuations in the reference entity’s risk-neutral default intensity. This paper defines and estimates a measure of the spread risk premium component in CDS spreads of a sample of European investment-grade firms by using a stochastic intensity credit model. Our results show that, on average, investors demand a positive premium for such mark-tomarket risks. After controlling for CDS market conditions, like liquidity and supply/demand effects, a panel data analysis of the estimated spread risk premia reveals a positive impact of event risk captured by the overall stock market volatility and a negative impact of investors’ appetite for exposure to credit markets as reflected by the overall CDS market.
{"title":"Spread Risk Premia in Corporate Credit Default Swap Markets","authors":"Oliver Entrop, Richard Schiemert, Marco Wilkens","doi":"10.2139/ssrn.1961197","DOIUrl":"https://doi.org/10.2139/ssrn.1961197","url":null,"abstract":"The spread risk premium component of credit default swap (CDS) spreads represents a compensation demanded by protection sellers for future changes in CDS spreads caused by unpredictable fluctuations in the reference entity’s risk-neutral default intensity. This paper defines and estimates a measure of the spread risk premium component in CDS spreads of a sample of European investment-grade firms by using a stochastic intensity credit model. Our results show that, on average, investors demand a positive premium for such mark-tomarket risks. After controlling for CDS market conditions, like liquidity and supply/demand effects, a panel data analysis of the estimated spread risk premia reveals a positive impact of event risk captured by the overall stock market volatility and a negative impact of investors’ appetite for exposure to credit markets as reflected by the overall CDS market.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86229620","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}
Return reversals depend on de facto market making by active informed investors as well as uninformed market makers. Accordingly, we find that reversals are higher following declines in the number of active institutional investors. Price declines over the past quarter, which serve as a proxy for declines in active investors, lead to stronger reversals across the subsequent two months; indeed reversals are concentrated primarily in past quarter losers. We provide evidence that price pressure induced by fire sales in response to past stock price drops cannot fully account for our results. Further, the evidence is consistent with market makers reacting more quickly to changes in the number of informed investors in the more recent period, particularly for large firms.
{"title":"Short-Term Reversals: The Effects of Institutional Exits and Past Returns","authors":"S. Cheng, A. Hameed, A. Subrahmanyam, S. Titman","doi":"10.2139/ssrn.2389408","DOIUrl":"https://doi.org/10.2139/ssrn.2389408","url":null,"abstract":"Return reversals depend on de facto market making by active informed investors as well as uninformed market makers. Accordingly, we find that reversals are higher following declines in the number of active institutional investors. Price declines over the past quarter, which serve as a proxy for declines in active investors, lead to stronger reversals across the subsequent two months; indeed reversals are concentrated primarily in past quarter losers. We provide evidence that price pressure induced by fire sales in response to past stock price drops cannot fully account for our results. Further, the evidence is consistent with market makers reacting more quickly to changes in the number of informed investors in the more recent period, particularly for large firms.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75195324","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 investigate the predictability of stock returns in the financial market for a large panel of developed countries using investor sentiment, business-cycle variables and financial indicators within two panel regime-switching models, with threshold and smooth transition between regimes. We find strong evidence of predictability of long-term returns following the business cycles, but much weaker results for the short-run returns. During crisis times, investor sentiment and inflation become key factors in predicting stock returns. Different tests and goodness of fit measures point out that the use of regime-switching models is more appropriate than linear models. To our knowledge, this study is the first to examine the impact of investor sentiment on future returns for a large number of countries, the existing literature being mainly focused on the U.S. stock market.
{"title":"Return Predictability in International Financial Markets and the Role of Investor Sentiment","authors":"Anjeza Kadilli","doi":"10.2139/ssrn.2291237","DOIUrl":"https://doi.org/10.2139/ssrn.2291237","url":null,"abstract":"We investigate the predictability of stock returns in the financial market for a large panel of developed countries using investor sentiment, business-cycle variables and financial indicators within two panel regime-switching models, with threshold and smooth transition between regimes. We find strong evidence of predictability of long-term returns following the business cycles, but much weaker results for the short-run returns. During crisis times, investor sentiment and inflation become key factors in predicting stock returns. Different tests and goodness of fit measures point out that the use of regime-switching models is more appropriate than linear models. To our knowledge, this study is the first to examine the impact of investor sentiment on future returns for a large number of countries, the existing literature being mainly focused on the U.S. stock market.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78279075","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 investigates the impact of economic growth risk on stock market performance in 70 countries. Based on the analysis of the full sample, on average, 1% increase in economic growth risk is associated with 0.23% (p = 0.058) increase in stock market return. Looking at stock market return calculated using January returns only, on average, 1% increase in economic growth risk is associated with 0.647% (p = 0.013) increase in stock market return. On average, 1% increase in economic growth risk is associated with 0.469% (p = 0.025) increase in stock market January return across developed markets. Likewise, on average, 1% increase in economic growth risk is associated with 0.695% (p = 0.069) increase in stock market January return across frontier markets. Finally, economic growth risk does not appear to have any impact on stock market return across emerging markets.
{"title":"Economic Growth Risk and Stock Market Performance: Cross-Sectional Evidence from 70 Countries","authors":"V. Sum, An Wang","doi":"10.2139/ssrn.2434832","DOIUrl":"https://doi.org/10.2139/ssrn.2434832","url":null,"abstract":"This study investigates the impact of economic growth risk on stock market performance in 70 countries. Based on the analysis of the full sample, on average, 1% increase in economic growth risk is associated with 0.23% (p = 0.058) increase in stock market return. Looking at stock market return calculated using January returns only, on average, 1% increase in economic growth risk is associated with 0.647% (p = 0.013) increase in stock market return. On average, 1% increase in economic growth risk is associated with 0.469% (p = 0.025) increase in stock market January return across developed markets. Likewise, on average, 1% increase in economic growth risk is associated with 0.695% (p = 0.069) increase in stock market January return across frontier markets. Finally, economic growth risk does not appear to have any impact on stock market return across emerging markets.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73609542","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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