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Portfolio Management for Insurers and Pension Funds and COVID-19: Targeting Volatility for Equity, Balanced and Target-Date Funds with Leverage Constraints 保险公司和养老基金的投资组合管理与COVID-19:杠杆约束下股票、平衡和目标日期基金的目标波动率
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2021-01-26 DOI: 10.2139/ssrn.3773495
B. Doan, J. J. Reeves, M. Sherris
{"title":"Portfolio Management for Insurers and Pension Funds and COVID-19: Targeting Volatility for Equity, Balanced and Target-Date Funds with Leverage Constraints","authors":"B. Doan, J. J. Reeves, M. Sherris","doi":"10.2139/ssrn.3773495","DOIUrl":"https://doi.org/10.2139/ssrn.3773495","url":null,"abstract":"\u0000 Insurers and pension funds face the challenges of historically low-interest rates and high volatility in equity markets, that have been accentuated due to the COVID-19 pandemic. Recent advances in equity portfolio management with a target volatility have been shown to deliver improved on average risk-adjusted return, after transaction costs. This paper studies these targeted volatility portfolios in applications to equity, balanced, and target-date funds with varying constraints on leverage. Conservative leverage constraints are particularly relevant to pension funds and insurance companies, with more aggressive leverage levels appropriate for alternative investments. We show substantial improvements in fund performance for differing leverage levels, and of most interest to insurers and pension funds, we show that the highest Sharpe ratios and smallest drawdowns are in targeted volatility-balanced portfolios with equity and bond allocations. Furthermore, we demonstrate the outperformance of targeted volatility portfolios during major stock market crashes, including the crash from the COVID-19 pandemic.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76956303","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}
引用次数: 0
Analysis of Forecasting Models in an Electricity Market under Volatility 波动性下电力市场预测模型分析
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2021-01-13 DOI: 10.2139/ssrn.3807052
G. Uddin, Maziar Sahamkhadam, Farhad Taghizadeh‐Hesary, Muhammad Yahya, O. Tang, P. Cerin, J. Rehme
{"title":"Analysis of Forecasting Models in an Electricity Market under Volatility","authors":"G. Uddin, Maziar Sahamkhadam, Farhad Taghizadeh‐Hesary, Muhammad Yahya, O. Tang, P. Cerin, J. Rehme","doi":"10.2139/ssrn.3807052","DOIUrl":"https://doi.org/10.2139/ssrn.3807052","url":null,"abstract":"Short-term electricity price forecasting has received considerable attention in recent years. Despite this increased interest, the literature lacks a concrete consensus on the most suitable forecasting approach. We conduct an extensive empirical analysis to evaluate the short-term price forecasting dynamics of different regions in the Swedish electricity market (SEM). We utilized several forecasting approaches ranging from standard conditional volatility models to wavelet-based forecasting. In addition, we performed out-of-sample forecasting and back-testing, and we evaluated the performance of these models. Our empirical analysis indicates that an ARMA-GARCH framework with the student’s t-distribution significantly outperforms other frameworks. We only performed wavelet-based forecasting based on the MAPE. The results of the robust forecasting methods are capable of displaying the importance of proper forecasting process design, policy implications for market efficiency, and predictability in the SEM.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79810255","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}
引用次数: 2
Convex Combinations in Judgment Aggregation 判断聚合中的凸组合
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2021-01-08 DOI: 10.2139/ssrn.3732274
Johannes G. Jaspersen
{"title":"Convex Combinations in Judgment Aggregation","authors":"Johannes G. Jaspersen","doi":"10.2139/ssrn.3732274","DOIUrl":"https://doi.org/10.2139/ssrn.3732274","url":null,"abstract":"Judgments are the basis for almost all decisions. They often come from different models and multiple experts. This information is typically aggregated using simple averages, which leads to the well-known shared information problem. A weighted average of the individual judgments based on empirically estimated sophisticated weights is commonly discarded in practice, because the sophisticated weights have large estimation errors. In this paper, we explore mixture weights, which are convex combinations of sophisticated and naive weights. We show analytically that if the data generation process is stable, there always exists a mixture weight which aggregates judgments better than the naive weights. We thus offer a path to alleviate the shared information problem. In contrast to other proposed solutions, we do not require any control over the judgment process. We demonstrate the utility of mixture weights in numerical analyses and in two empirical applications. We also offer heuristic selection algorithms for the correct mixture weight and analyze them in our numerical and empirical settings.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86286017","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}
引用次数: 0
Option-Implied Network Measures of Tail Contagion and Stock Return Predictability 尾部传染与股票收益可预测性的期权隐含网络测度
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2021-01-01 DOI: 10.2139/ssrn.3791467
Manuela Pedio
{"title":"Option-Implied Network Measures of Tail Contagion and Stock Return Predictability","authors":"Manuela Pedio","doi":"10.2139/ssrn.3791467","DOIUrl":"https://doi.org/10.2139/ssrn.3791467","url":null,"abstract":"The Great Financial Crisis of 2008 – 2009 has raised the attention of policy-makers and researchers about the interconnectedness among the volatility of the returns of financial assets as a potential source of risk that extends beyond the usual changes in correlations and include transmission channels that operate through the higher order co-moments of returns. In this paper, we investigate whether a newly developed, forward-looking measure of volatility spillover risk based on option implied volatilities shows any predictive power for stock returns. We also compare the predictive performance of this measure with that of the volatility spillover index proposed by Diebold and Yilmaz (2008, 2012), which is based on realized, backward-looking volatilities instead. While both measures show evidence of in-sample predictive power, only the option-implied measure is able to produce out-of-sample forecasts that outperform a simple historical mean benchmark.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75311332","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}
引用次数: 1
Predictive Regressions for Aggregate Stock Market Volatility with Machine Learning 基于机器学习的股票市场总波动预测回归
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-12-01 DOI: 10.2139/ssrn.3824789
Juan D. Díaz, Erwin Hansen, Gabriel Cabrera
{"title":"Predictive Regressions for Aggregate Stock Market Volatility with Machine Learning","authors":"Juan D. Díaz, Erwin Hansen, Gabriel Cabrera","doi":"10.2139/ssrn.3824789","DOIUrl":"https://doi.org/10.2139/ssrn.3824789","url":null,"abstract":"We investigate whether machine learning techniques and a large set of financial and macroeconomic variables can be used to predict future S&P realized volatility. We evaluate the aggregate volatility predictions of regularization methods (Ridge, Lasso, and Elastic Net), tree-based methods (Random forest and Gradient boosting), and forecast combination methods. We find that the machine learning algorithms outperform autoregressive benchmark models, both statistically and economically, and that the tree-based methods perform the best. In addition to its past realizations, our analysis reveals that the main drivers of aggregate volatility are several financial and macroeconomic uncertainty proxies.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90427912","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}
引用次数: 0
From Large to Vast: Composite Forecasting of Vast-Dimensional Realized Covariance Matrices Using Factor State-Space Models 从大到广:利用因子状态空间模型对大维度已实现协方差矩阵进行复合预测
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-11-04 DOI: 10.2139/ssrn.3721110
Jan Patrick Hartkopf
{"title":"From Large to Vast: Composite Forecasting of Vast-Dimensional Realized Covariance Matrices Using Factor State-Space Models","authors":"Jan Patrick Hartkopf","doi":"10.2139/ssrn.3721110","DOIUrl":"https://doi.org/10.2139/ssrn.3721110","url":null,"abstract":"We propose a dynamic factor state-space model for the prediction of high-dimensional realized covariance matrices of asset returns. Using a block LDL decomposition of the joint covariance matrix of assets and factors, we express the realized covariance matrix of the individual assets similar to an approximate factor model. We model the individual parts, i.e., the factor and residual covariances as well as the factor loadings, independently via a tractable state-space approach. This results in closed-form Matrix-F predictive densities for the distinct covariance elements and Student's t predictive densities for the factor loadings. In an out-of-sample forecasting and portfolio selection exercise we compare the performance of the proposed factor model under different specifications of the residual dynamics. These includes block diagonal residuals based on the GICS sector classifications and strict diagonality assumptions as well as combinations of both using linear shrinkage. We find that the proposed model performs very well in an empirical application to realized covariance matrices for 225 NYSE traded stocks using the well-known Fama-French factors and sector-specific factors represented by Exchange Traded Funds (ETFs).","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"349 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73936518","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}
引用次数: 1
Retail Investors’ Trading Activity and the Predictability of Stock Return Correlations 散户投资者的交易活动与股票收益相关性的可预测性
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-10-13 DOI: 10.2139/ssrn.3709775
Daniele Ballinari
{"title":"Retail Investors’ Trading Activity and the Predictability of Stock Return Correlations","authors":"Daniele Ballinari","doi":"10.2139/ssrn.3709775","DOIUrl":"https://doi.org/10.2139/ssrn.3709775","url":null,"abstract":"Considerable theoretical and empirical evidence links price comovements with the behavior of retail investors. Nevertheless, when predicting stock return correlations, research has focused on the leverage effect. We propose a new model of realized covariances that allows exogenous predictors to influence the correlation dynamics while ensuring the predicted matrices' positive definiteness. Using this model, the predictive power of retail investors' sentiment and attention for the correlations of 35 Dow Jones stocks is analyzed. We find retail investors' attention to have predictive power for return correlations, especially for longer forecasting horizons and during the COVID-19 pandemic. Value-at-risk forecasts confirm these results.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84937241","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}
引用次数: 0
Forecasting Value at Risk and Conditional Value at Risk using Option Market Data 利用期权市场数据预测风险价值和条件风险价值
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-09-10 DOI: 10.2139/ssrn.3690471
Annalisa Molino, Carlo Sala
{"title":"Forecasting Value at Risk and Conditional Value at Risk using Option Market Data","authors":"Annalisa Molino, Carlo Sala","doi":"10.2139/ssrn.3690471","DOIUrl":"https://doi.org/10.2139/ssrn.3690471","url":null,"abstract":"We forecast monthly Value at Risk (VaR) and Conditional Value at Risk (CVaR) using option market data and four different econometric techniques. Independently from the econometric approach used, all models produce quick to estimate forward-looking risk measures that do not depend from the amount of historical data used and that, through the implied moments of options, better reflect the ever-changing market scenario. All proposed option-based approaches outperform or are equally good to different “traditional” forecasts that use historical returns as input. The extensive robustness of our results shows that the real driver of the better forecasts is the use of option market data as inputs for the analysis, more than the type of econometric approach implemented.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83159842","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}
引用次数: 5
Forecasting U.S. Economic Growth in Downturns Using Cross-Country Data 利用跨国数据预测美国经济衰退中的增长
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-08-20 DOI: 10.2139/ssrn.3690671
Yifei Lyu, Jun Nie, Shu-Kuei X. Yang
{"title":"Forecasting U.S. Economic Growth in Downturns Using Cross-Country Data","authors":"Yifei Lyu, Jun Nie, Shu-Kuei X. Yang","doi":"10.2139/ssrn.3690671","DOIUrl":"https://doi.org/10.2139/ssrn.3690671","url":null,"abstract":"The Covid-19 pandemic has created tremendous downward pressure on economic activity and revived interest in forecasting economic growth during severe downturns However, most dynamic factor models used to forecast GDP growth include only domestic data We construct a large data set of 77 countries representing over 90 percent of global GDP and show that including cross-country data helps produce more accurate forecasts of US GDP growth during economic downturns, but is less helpful in normal times We provide explanations why this is the case","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83408233","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}
引用次数: 0
The Lasso and the Factor Zoo - Expected Returns in the Cross-Section 套索与因子动物园-横断面的预期收益
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-08-09 DOI: 10.2139/ssrn.2930436
Marcial Messmer, F. Audrino
{"title":"The Lasso and the Factor Zoo - Expected Returns in the Cross-Section","authors":"Marcial Messmer, F. Audrino","doi":"10.2139/ssrn.2930436","DOIUrl":"https://doi.org/10.2139/ssrn.2930436","url":null,"abstract":"We document that cross-sectional return predictions based on OLS and Lasso type linear methods contain no predictive power for large cap stocks over the last decades. Small and micro cap stocks are highly predictable throughout the entire sample. Based on the 68 firm characteristics (FC) included in our analysis, the variable selection step suggests a highly multi-dimensional return process. Additionally, our Monte Carlo simulations indicate advantages of Lasso type predictions over OLS in panel specifications with a low signal-to-noise ratio. The results are robust to various assumptions.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"113 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79778649","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}
引用次数: 1
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