This paper proposes a two-regime threshold model for the conditional distribution of stock returns in which returns follow a distinct skewed Student t distribution within each regime: the model allows capturing time variation in the conditional distribution of returns, as well as higher order moments. An application of the model to daily U.S. stock returns illustrates the advantages of the proposed model in comparison to alternative specifications: the model performs well in terms of in-sample fit; it more accurately estimates the conditional volatility; and it produces useful risk assessment as measured by the term structure of value at risk.
{"title":"A Two-Regime Threshold Model with Conditional Skewed Student t Distributions for Stock Returns","authors":"D. Massacci","doi":"10.2139/ssrn.2212627","DOIUrl":"https://doi.org/10.2139/ssrn.2212627","url":null,"abstract":"This paper proposes a two-regime threshold model for the conditional distribution of stock returns in which returns follow a distinct skewed Student t distribution within each regime: the model allows capturing time variation in the conditional distribution of returns, as well as higher order moments. An application of the model to daily U.S. stock returns illustrates the advantages of the proposed model in comparison to alternative specifications: the model performs well in terms of in-sample fit; it more accurately estimates the conditional volatility; and it produces useful risk assessment as measured by the term structure of value at risk.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82143218","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 build a simple ALM model where future scenarios are generated assuming a Markov regime switching framework. Using the Shiller database of monthly equity returns and interest rate data since 1870, two regimes are revealed by the data that clearly correspond to a "normal regime" where returns behave like expected from economic theory, and a "high volatility" regime we may also refer to as a "crisis regime". Given the evidence of the non-stationarity of economic variables, we investigate the added value of reducing risk in the portfolio when the model indicates a high probability of a regime shift. The persistence of each of the regimes is high. This framework gives, each month and for each scenario, the probability of being in one of the two regimes, and hence the multivariate distribution of the simulated variables that pertains to the relevant regime. These variables are 1) equity returns, 2) long term (10-year) interest rates, 3) realized inflation, and 4) short term (6-month) interest rates. We then investigate a number of relevant statistics of the terminal wealth achieved after a 20-year period for two typical portfolios: a long-only portfolio well-diversified over stocks and bonds where the relevant metric is the portfolio’s value (for instance, an endowment fund), and a pension fund’s coverage ratio where the fund’s liabilities are valued by a market interest rate curve. We show that both types of investors greatly benefit from adjusting their exposure to equities and interest rates conditionally on the expected risk regime. Finally, we show the consequence when both the endowment fund manager and the pension fund board members optimize their own reward/risk ratio from their job. We argue that in such a case they seek to minimize the probability of large losses (either in absolute terms or relative to the pension fund’s liabilities), while maximizing the minimum level of wealth (or coverage ratio for the pension fund) achieved with a given (say 95%) confidence level. We quantify the added value of the risk-regime depending allocations for such managers.
{"title":"Simulating Pension's Assets and Liabilities in a Regime Switching Framework","authors":"Samuel de Visser, F. Hamelink","doi":"10.2139/ssrn.2381551","DOIUrl":"https://doi.org/10.2139/ssrn.2381551","url":null,"abstract":"In this paper we build a simple ALM model where future scenarios are generated assuming a Markov regime switching framework. Using the Shiller database of monthly equity returns and interest rate data since 1870, two regimes are revealed by the data that clearly correspond to a \"normal regime\" where returns behave like expected from economic theory, and a \"high volatility\" regime we may also refer to as a \"crisis regime\". Given the evidence of the non-stationarity of economic variables, we investigate the added value of reducing risk in the portfolio when the model indicates a high probability of a regime shift. The persistence of each of the regimes is high. This framework gives, each month and for each scenario, the probability of being in one of the two regimes, and hence the multivariate distribution of the simulated variables that pertains to the relevant regime. These variables are 1) equity returns, 2) long term (10-year) interest rates, 3) realized inflation, and 4) short term (6-month) interest rates. We then investigate a number of relevant statistics of the terminal wealth achieved after a 20-year period for two typical portfolios: a long-only portfolio well-diversified over stocks and bonds where the relevant metric is the portfolio’s value (for instance, an endowment fund), and a pension fund’s coverage ratio where the fund’s liabilities are valued by a market interest rate curve. We show that both types of investors greatly benefit from adjusting their exposure to equities and interest rates conditionally on the expected risk regime. Finally, we show the consequence when both the endowment fund manager and the pension fund board members optimize their own reward/risk ratio from their job. We argue that in such a case they seek to minimize the probability of large losses (either in absolute terms or relative to the pension fund’s liabilities), while maximizing the minimum level of wealth (or coverage ratio for the pension fund) achieved with a given (say 95%) confidence level. We quantify the added value of the risk-regime depending allocations for such managers.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82176597","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}
Contrary to well-known asset pricing models, volatilities implied by equity index options exceed realized stock market volatility and exhibit a pattern known as the volatility skew. We explain both facts using a model that can also account for the mean and volatility of equity returns. Our model assumes a small risk of economic disaster that is calibrated based on international data on large consumption declines. We allow the disaster probability to be stochastic, which turns out to be crucial to the model’s ability both to match equity volatility and to reconcile option prices with macroeconomic data on disasters. This paper was accepted by Lauren Cohen, finance.
{"title":"Option Prices in a Model with Stochastic Disaster Risk","authors":"S. Seo, Jessica A. Wachter","doi":"10.2139/ssrn.2555700","DOIUrl":"https://doi.org/10.2139/ssrn.2555700","url":null,"abstract":"Contrary to well-known asset pricing models, volatilities implied by equity index options exceed realized stock market volatility and exhibit a pattern known as the volatility skew. We explain both facts using a model that can also account for the mean and volatility of equity returns. Our model assumes a small risk of economic disaster that is calibrated based on international data on large consumption declines. We allow the disaster probability to be stochastic, which turns out to be crucial to the model’s ability both to match equity volatility and to reconcile option prices with macroeconomic data on disasters. This paper was accepted by Lauren Cohen, finance.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88933859","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}
Currently, the world is facing a continuous process of integration in di fferent aspects and fi nancial markets are no exception to this development. Despite the fact that global integration is gradual, one can fi nd some specfi c events that might help to accelerate this trend. This paper shows that after the fi nancial crisis of 2008, which mainly occurred in the United States, the Latin American stock markets exhibit a higher level of convergence, measured by the correlation between the annual returns of their stock market indices. Additionally, we find convergence in the coe ficient of sensitivity between Latin American and U.S. stock markets, using dynamic linear models at the regional level. In particular, we uncover consistent movements in the levels of sensitivity between the daily annual returns of the Latin American indices and the S&P index after the fi nancial crisis. This kind of convergence might be a positive sign to accelerate the integration process in Latin America stock markets, which has had a slow development since its beginning a few years ago.
{"title":"Co-Movements between Latin American and U.S. Stock Markets: Convergence after the Financial Crisis?","authors":"Andrés Ramírez Hassan, Javier Pantoja","doi":"10.2139/ssrn.2347030","DOIUrl":"https://doi.org/10.2139/ssrn.2347030","url":null,"abstract":"Currently, the world is facing a continuous process of integration in di fferent aspects and fi nancial markets are no exception to this development. Despite the fact that global integration is gradual, one can fi nd some specfi c events that might help to accelerate this trend. This paper shows that after the fi nancial crisis of 2008, which mainly occurred in the United States, the Latin American stock markets exhibit a higher level of convergence, measured by the correlation between the annual returns of their stock market indices. Additionally, we find convergence in the coe ficient of sensitivity between Latin American and U.S. stock markets, using dynamic linear models at the regional level. In particular, we uncover consistent movements in the levels of sensitivity between the daily annual returns of the Latin American indices and the S&P index after the fi nancial crisis. This kind of convergence might be a positive sign to accelerate the integration process in Latin America stock markets, which has had a slow development since its beginning a few years ago.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90043883","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 develop a generalization of the Baker and Wurgler (2012) signaling model where investors are loss-averse to dividend cuts. We apply our framework to study how a firm's characteristics and manager's incentives affect payout policy properties. In equilibrium firms with riskier earnings are less likely to pay dividends, however, those that pay, payout more. Similarly, firms whose managers have a higher share of stock options in their compensation package are less likely to pay positive dividends. There is a clientele effect. Investors’ preferences and choices affect the payout policy and two otherwise identical firms can greatly differ in how they pay dividends. Finally, we relate our model's predictions to the disappearing dividend puzzle.
{"title":"Dividends as Signaling Device and the Disappearing Dividend Puzzle","authors":"Dmitry A. Shapiro, Anan Zhuang","doi":"10.2139/ssrn.2235107","DOIUrl":"https://doi.org/10.2139/ssrn.2235107","url":null,"abstract":"In this paper we develop a generalization of the Baker and Wurgler (2012) signaling model where investors are loss-averse to dividend cuts. We apply our framework to study how a firm's characteristics and manager's incentives affect payout policy properties. In equilibrium firms with riskier earnings are less likely to pay dividends, however, those that pay, payout more. Similarly, firms whose managers have a higher share of stock options in their compensation package are less likely to pay positive dividends. There is a clientele effect. Investors’ preferences and choices affect the payout policy and two otherwise identical firms can greatly differ in how they pay dividends. Finally, we relate our model's predictions to the disappearing dividend puzzle.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79228756","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 introduce three new families of reward-risk ratios, study their properties and compare them to existing examples. All ratios in the three families are monotonic and quasi-concave, which means that they prefer more to less and encourage diversification. Members of the second family are also scale invariant. The third family is a subset of the second one, and all its members only depend on the distribution of a return. In the second part of the paper we provide an overview of existing reward-risk ratios and discuss their properties. For instance, we show that, like the Sharpe ratio, every reward-deviation ratio violates the monotonicity property.
{"title":"Reward-Risk Ratios","authors":"Patrick Cheridito, Eduard Kromer","doi":"10.2139/ssrn.2144185","DOIUrl":"https://doi.org/10.2139/ssrn.2144185","url":null,"abstract":"We introduce three new families of reward-risk ratios, study their properties and compare them to existing examples. All ratios in the three families are monotonic and quasi-concave, which means that they prefer more to less and encourage diversification. Members of the second family are also scale invariant. The third family is a subset of the second one, and all its members only depend on the distribution of a return. In the second part of the paper we provide an overview of existing reward-risk ratios and discuss their properties. For instance, we show that, like the Sharpe ratio, every reward-deviation ratio violates the monotonicity property.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87306317","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 : 2013-09-10DOI: 10.1016/J.JBANKFIN.2013.10.010
Yinggang Zhou
{"title":"Modeling the Joint Dynamics of Risk-Neutral Stock Index and Bond Yield Volatilities","authors":"Yinggang Zhou","doi":"10.1016/J.JBANKFIN.2013.10.010","DOIUrl":"https://doi.org/10.1016/J.JBANKFIN.2013.10.010","url":null,"abstract":"","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85634759","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 quantify investors’ preferences over the dynamics of shocks by deriving frequency-specific risk prices that capture the price of risk of consumption fluctuations at each frequency. The frequency-specific risk prices are derived analytically for leading models. The decomposition helps measure the importance of economic fluctuations at different frequencies. We precisely quantify the meaning of "long-run" in the context of Epstein-Zin preferences – centuries – and measure the exact relevance of business-cycle fluctuations. Finally, we estimate frequency-specific risk prices and show that cycles longer than the business cycle – long-run risks – are significantly priced in the equity market. Received January 13, 2015; accepted February 23, 2016 by Editor Leonid Kogan.
{"title":"Asset Pricing in the Frequency Domain: Theory and Empirics","authors":"Ian Dew-Becker, Stefano Giglio","doi":"10.2139/ssrn.2642879","DOIUrl":"https://doi.org/10.2139/ssrn.2642879","url":null,"abstract":"We quantify investors’ preferences over the dynamics of shocks by deriving frequency-specific risk prices that capture the price of risk of consumption fluctuations at each frequency. The frequency-specific risk prices are derived analytically for leading models. The decomposition helps measure the importance of economic fluctuations at different frequencies. We precisely quantify the meaning of \"long-run\" in the context of Epstein-Zin preferences – centuries – and measure the exact relevance of business-cycle fluctuations. Finally, we estimate frequency-specific risk prices and show that cycles longer than the business cycle – long-run risks – are significantly priced in the equity market. Received January 13, 2015; accepted February 23, 2016 by Editor Leonid Kogan.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88024060","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 thesis investigates models of market risk assessment based on genetic algorithms, with specific reference to asset portfolio choice under volatile market conditions. It does so by developing computational simulations of asset portfolios, which are then subjected to stressful price events. A genetic algorithm functions as an optimising process, allowing portfolios to evolve towards a structure that is – on average – less fragile against asset shocks. The importance of this research is dictated by the grave outcomes of, for instance, the 2008 financial crisis: 371 commercial banks failed between 1/1/2008 and 1/7/2011 in the United States alone. Such events highlighted the need for the renovation of the financial risk framework supposed to at least partially shield banks from unexpected adverse events.A synthetic, computational model is constructed, where asset portfolios are structured so as to invest in four main asset categories: sovereign bonds, financial institutions bonds, corporate bonds and real estate. Each of the categories has an underlying non-normal probability distribution of prices, empirically derived by the literature. These are used to simulate volatile and adverse scenarios affecting the value of the portfolios. A genetic algorithm is designed to select, crossover and mutate, at each generation, the portfolios that best perform under the simulated conditions. After a number of generations, it is expected that one or more portfolios structures will be highlighted as the ones that best perform under adverse scenarios.The model is run three times with different sets of optimization constraints, each specifying the minimum relative proportion of portfolios to be dedicated to each asset category. All versions of the model indicate that the best performing portfolios structures under volatile conditions are the ones that are mainly composed by the asset category featuring less fat tails. The results of the model are checked for their robustness, by running versions with different sets of simulated scenarios and additional numbers of synthetic asset categories. Limitations of the design of the study are identified. The model lacks a simulation of the liability side of financial institutions, and its results are not tested on a systemic level, thus not shedding light on what consequences the indicated portfolio strategy for a single bank would have on the network of banks. Such issues will be addressed in future research.
{"title":"Strengthening Banks' Portfolio Against Asset Shocks: A Genetic Computational Approach","authors":"S. Gurciullo","doi":"10.2139/ssrn.2333471","DOIUrl":"https://doi.org/10.2139/ssrn.2333471","url":null,"abstract":"This thesis investigates models of market risk assessment based on genetic algorithms, with specific reference to asset portfolio choice under volatile market conditions. It does so by developing computational simulations of asset portfolios, which are then subjected to stressful price events. A genetic algorithm functions as an optimising process, allowing portfolios to evolve towards a structure that is – on average – less fragile against asset shocks. The importance of this research is dictated by the grave outcomes of, for instance, the 2008 financial crisis: 371 commercial banks failed between 1/1/2008 and 1/7/2011 in the United States alone. Such events highlighted the need for the renovation of the financial risk framework supposed to at least partially shield banks from unexpected adverse events.A synthetic, computational model is constructed, where asset portfolios are structured so as to invest in four main asset categories: sovereign bonds, financial institutions bonds, corporate bonds and real estate. Each of the categories has an underlying non-normal probability distribution of prices, empirically derived by the literature. These are used to simulate volatile and adverse scenarios affecting the value of the portfolios. A genetic algorithm is designed to select, crossover and mutate, at each generation, the portfolios that best perform under the simulated conditions. After a number of generations, it is expected that one or more portfolios structures will be highlighted as the ones that best perform under adverse scenarios.The model is run three times with different sets of optimization constraints, each specifying the minimum relative proportion of portfolios to be dedicated to each asset category. All versions of the model indicate that the best performing portfolios structures under volatile conditions are the ones that are mainly composed by the asset category featuring less fat tails. The results of the model are checked for their robustness, by running versions with different sets of simulated scenarios and additional numbers of synthetic asset categories. Limitations of the design of the study are identified. The model lacks a simulation of the liability side of financial institutions, and its results are not tested on a systemic level, thus not shedding light on what consequences the indicated portfolio strategy for a single bank would have on the network of banks. Such issues will be addressed in future research.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79589541","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 proposes semi-parametric least squares estimation of parametric risk-return relationships, i.e. parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of unobservable parametric factors. A distinctive feature of our estimator is that it does not require a parametric model for the conditional mean and variance. We establish consistency and asymptotic normality of the estimates. The theory is non-standard due to the presence of estimated factors. We provide simple sufficient conditions for the estimated factors not to have an impact in the asymptotic standard error of estimators. A simulation study investigates the nite sample performance of the estimates. Finally, an application to the CRSP value-weighted excess returns highlights the merits of our approach. In contrast to most previous studies using non-parametric estimates, we find a positive and significant price of risk in our semi-parametric setting.
{"title":"Semi-Parametric Estimation of Risk-Return Relationships","authors":"J. Escanciano, J. Pardo-Fernández, I. Keilegom","doi":"10.2139/ssrn.2320768","DOIUrl":"https://doi.org/10.2139/ssrn.2320768","url":null,"abstract":"This article proposes semi-parametric least squares estimation of parametric risk-return relationships, i.e. parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of unobservable parametric factors. A distinctive feature of our estimator is that it does not require a parametric model for the conditional mean and variance. We establish consistency and asymptotic normality of the estimates. The theory is non-standard due to the presence of estimated factors. We provide simple sufficient conditions for the estimated factors not to have an impact in the asymptotic standard error of estimators. A simulation study investigates the nite sample performance of the estimates. Finally, an application to the CRSP value-weighted excess returns highlights the merits of our approach. In contrast to most previous studies using non-parametric estimates, we find a positive and significant price of risk in our semi-parametric setting.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88750493","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：