Econometric Modeling: Capital Markets - Forecasting eJournal最新文献

{"title":"Sector Rotation through the Business Cycle: A Machine Learning Regime Approach","authors":"M. Sauer","doi":"10.2139/ssrn.3473907","DOIUrl":"https://doi.org/10.2139/ssrn.3473907","url":null,"abstract":"Sector returns should theoretically differ during business cycle regimes. The notion of cyclical and defensive sectors is clearly established among practitioners and academics alike. On the other hand, the persistence, now- and forecastability of business cycles has been documented by a vast amount of literature. This study tests whether both strands can be merged to construct an investable sector rotation strategy based on the analysis of macroeconomic data. I find that both relationships hold: If one has forward looking information about GDP, outperformance from sector rotation is possible. Furthermore, one can nowcast the current position in the business cycle with some accuracy. While nowcasting accuracy is too small to translate into constant outperformance, the value of the examined methodology lies in the timely identification of major economic crises and provides economically superior performance by significantly reducing drawdowns during such.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73314909","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}

{"title":"Backtesting Global Growth-at-Risk","authors":"C. Brownlees, André B.M. Souza","doi":"10.2139/ssrn.3461214","DOIUrl":"https://doi.org/10.2139/ssrn.3461214","url":null,"abstract":"Abstract We conduct an out-of-sample backtesting exercise of Growth-at-Risk (GaR) predictions for 24 OECD countries. We consider forecasts constructed from quantile regression and GARCH models. The quantile regression forecasts are based on a set of recently proposed measures of downside risks to GDP, including the national financial conditions index. The backtesting results show that quantile regression and GARCH forecasts have a similar performance. If anything, our evidence suggests that standard volatility models such as the GARCH(1,1) are more accurate.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86292972","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}

{"title":"Modelling Yields at the Lower Bound Through Regime Shifts","authors":"Peter Hördahl, O. Tristani","doi":"10.2139/ssrn.3464245","DOIUrl":"https://doi.org/10.2139/ssrn.3464245","url":null,"abstract":"We propose a regime-switching approach to deal with the lower bound on nominal interest rates in dynamic term structure modelling. In the \"lower bound regime\", the short term rate is expected to remain constant at levels close to the effective lower bound; in the \"normal regime\", the short rate interacts with other economic variables in a standard way. State-dependent regime switching probabilities ensure that the likelihood of being in the lower bound regime increases as short rates fall closer to zero. A key advantage of this approach is to capture the gradualism of the monetary policy normalization process following a lower bound episode. The possibility to return to the lower bound regime continues exerting an influence in the early phases of normalization, pulling expected future rates downwards. We apply our model to U.S. data and show that it captures key properties of yields at the lower bound. In spite of its heavier parameterization, the regime-switching model displays a competitive out-of-sample forecasting performance. It can also be used to gauge the risk of a return to the lower bound regime in the future. As of mid-2018, it provides a more benign assessment than alternative measures.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77143200","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}

{"title":"Can the Global Economy Activity Predict Cryptocurrency Returns","authors":"Hui-Pei Cheng, Kuang-Chieh Yen","doi":"10.2139/ssrn.3488987","DOIUrl":"https://doi.org/10.2139/ssrn.3488987","url":null,"abstract":"We investigate whether the global economic activity (GEA) index provided by Kilian (2009) can predict the dynamics of the cryptocurrency. First, we find that the lagged two-month GEA index can predict positively the cryptocurrency monthly returns, especially for Bitcoin. It implies that the investor tends to invest more in the Bitcoin when the economic condition was good two months ago. Furthermore, Bitcoin investors would decrease (increase) their investment when the decline (rise) of the S&P 500 index. In addition, we find the Bitcoin return predictability of the GEA index only exists in the one-month-ahead period.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"183 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85628132","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}

{"title":"Moderate and Extreme Volatility: Do the Magnitude of Returns Matter for Forecasting?","authors":"A. Clements, R. Herrera","doi":"10.2139/ssrn.3443259","DOIUrl":"https://doi.org/10.2139/ssrn.3443259","url":null,"abstract":"This paper proposes a novel decomposition of realized volatility (RV) into moderate and extreme realized volatility estimates. These estimates behave like long and short term components of volatility, and are very different from either realized semi-variance or the continuous and jump components of volatility. Within the standard linear HAR framework, a forecast comparison exercise using index returns shows that employing the new decomposition leads to forecasts that are often superior to the competing forecasts based on existing realized measures.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75285427","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}

{"title":"Cyclical Consumption and Expected Returns: New Evidence - How Long Does it Take to Form the Habit in Habit Model?","authors":"Yulong Sun","doi":"10.2139/ssrn.3486543","DOIUrl":"https://doi.org/10.2139/ssrn.3486543","url":null,"abstract":"Atanasov, Møller, and Priestley (2019) find that cyclical consumption at 5-7 year frequency can predict (excess) returns at market level and they argue that low-frequency fluctuations in consumption capture slow-moving counter-cyclical variations in expected returns. Based on cross-sectional evidence, I find that their results are mainly driven by the large-capitalization stocks and cannot be extended to other sorted portfolios. Meanwhile, all firms' returns can be predicted by cyclical consumption at 1-2 year frequency and it suggests cyclical consumption may capture the risk premia at shorter business cycle frequency. I also find cyclical consumption growth is persistent and the persistence increases with time horizon and the future dividend-price ratio can be predicted by cyclical consumption. To rationalize the stylized facts, I modify the Campbell-Cochrane habit model by allowing persistent consumption growth and finite-horizon habit formation. The modified model can reproduce the inverse relation between cyclical consumption and future expected stock returns, consistent with empirical findings.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89019377","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}

{"title":"Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility","authors":"T. S. Kleppe, R. Liesenfeld, G. V. Moura, Atle Oglend","doi":"10.2139/ssrn.3441097","DOIUrl":"https://doi.org/10.2139/ssrn.3441097","url":null,"abstract":"We propose a factor state-space approach with stochastic volatility to model and forecast the term structure of future contracts on commodities. Our approach builds upon the dynamic 3-factor Nelson-Siegel model and its 4-factor Svensson extension and assumes for the latent level, slope and curvature factors a Gaussian vector autoregression with a multivariate Wishart stochastic volatility process. Exploiting the conjugacy of the Wishart and the Gaussian distribution, we develop a computationally fast and easy to implement MCMC algorithm for the Bayesian posterior analysis. An empirical application to daily prices for contracts on crude oil with stipulated delivery dates ranging from one to 24 months ahead show that the estimated 4-factor Svensson model with two curvature factors provides a good parsimonious representation of the serial correlation in the individual prices and their volatility. It also shows that this model has a good out-of-sample forecast performance.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85543828","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}

{"title":"Real-time Recession Probability with Hidden Markov Model and Unemployment Momentum","authors":"Stephen H-T. Lihn","doi":"10.2139/ssrn.3435667","DOIUrl":"https://doi.org/10.2139/ssrn.3435667","url":null,"abstract":"We show how to construct a composite Hidden Markov Model (HMM) to calculate real-time recession probability, using the jubilee and ldhmm packages in R. The input data is the unemployment rate (UNRATE) which is released monthly by the U.S. government. There are two sub-models: The one-year momentum model and the 6-month acceleration model. The product of the two generates the recession probability. Our model demonstrates that positive momentum in unemployment kicks off a recession. The momentum accelerates during the recession. And eventually the rapid deceleration marks the end of it.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80964162","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}

{"title":"Assessing the Predictability of Oil Prices","authors":"Don Charles","doi":"10.2139/ssrn.3620576","DOIUrl":"https://doi.org/10.2139/ssrn.3620576","url":null,"abstract":"Oil prices are volatile. They fluctuate due to several demand and supply characteristics. Several macroeconomic factors, may be used to assess the direction of oil prices. However, the data on these variables are often annual, and cannot be used for short term forecasting. As a result, speculators and retail traders often rely upon econometric time series models to produce forecasts. Early models for univariate forecasting include, the Autoregressive Integrated Moving Average (ARIMA), and the Exponential Generalized Autoregressive Conditional Heterscedasticity (EGARCH). These models are often criticized for their linearity. Recent machine learning models have become popular in the forecasting discipline. In fact, the Artificial Neural Network (ANN), and the Wavelet Transform have been increasingly used for forecasting. This study uses the ARIMA, EGARCH, ANN, and Wavelet Transform (Daubechies level 2 order 3)-ARMA models to forecast oil prices. Data on oil prices over the Jan 02, 1986 to June 10, 2019 period is considered. <br>To complement the analysis, fundamental analysis is also used to forecast the direction of oil prices. Surprisingly, the fundamentals, based on the US oil inventories seem to have a higher predictive accuracy than the aforementioned models. <br>","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87142005","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}

{"title":"A Cash-Flow Focus for Endowments and Trusts","authors":"James P. Garland","doi":"10.2139/ssrn.3474526","DOIUrl":"https://doi.org/10.2139/ssrn.3474526","url":null,"abstract":"The primary objective of perpetual endowment funds and long-lived trust funds is to generate spendable cash. Ideally, these cash disbursements would be stable from one year to the next and would grow to keep pace with inflation.<br><br>Too-high disbursements today would lead to too-low disbursements tomorrow, and vice versa. Setting a proper spending rate is difficult. Trustees often set percentage spending rates based on the real returns they expect to earn from their investments and then link those spending rates to their funds’ market values. But linking spending to market values causes problems.<br><br>One problem is that market values of common asset classes, such as stocks and bonds, are volatile. Trustees fight this volatility by averaging market values over time, but averaging does not work very well.<br><br>Another problem is that trustees who base spending on market values often understandably come to believe that market values themselves determine spending. In other words, if market values increase (or fall) by a significant amount, then trustees feel justified in increasing (or cutting) spending by similar amounts. This belief is misguided. For equities, the predominant asset class in most endowment and trust funds, the source of returns is not market values but, rather, corporate profits.<br><br>This brief argues that, counter to common practice, trustees should turn their backs on market values and instead focus on the real cash flows that their assets can generate. For bonds, this would mean their real interest rate. For equities, this would mean their underlying profits. This focus on asset cash flows, rather than on asset market values, is a better way to go. This brief offers two spending rules based on cash flows. One looks at corporate dividends, and the other at corporate profits.<br><br>Trustees who base spending on market values usually include bonds in their funds to dampen market value swings. A 30% bond allocation is not uncommon. Yet the cash-flow spending rules described here lead to less volatile spending, even when applied to a 100% equity portfolio, than that of a 30% bond/70% equity portfolio whose spending is based on market values.<br><br>In addition, spending rules based on cash flows free trustees from fretting about market values. Diversification can still be beneficial, but no longer do trustees need to diversify primarily to dampen market downturns. When equity market values decline, as they invariably will from time to time, trustees may be able to say, “We don’t care.”<br><br>Furthermore, spending rules based on cash flows enable trustees to keep score. Trustees of perpetual endowment funds and of long-lived personal trust funds often feel obligated to be intergenerationally equitable — that is, to treat current and future beneficiaries the same. The near-universal way to evaluate intergenerational equity is to look at market values. Instead, a spending rule based on cash flows works better.<br><br>Finally","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72747637","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}