In-sample Predictability Research Articles

Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices

Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample period from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability but scarce evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregressive (HAR)-RV model. This lack of systematic evidence of out-of-sample forecasting gains is corroborated by extensive robustness checks, including an in-depth study of the quantiles of the distributions of the RVs and subsample periods that account for increases in the total spillovers among the RVs. We also study an extended model that features the RVs of energy commodities and precious metals, but our conclusions remain unaffected. Besides offering important lessons for future research, our results are interesting for financial market participants, who rely on accurate forecasts of RVs when solving portfolio optimization and derivatives pricing problems, and policymakers, who need accurate forecasts of RVs when designing policies to mitigate the potential adverse effects of a rise in the RVs of agricultural commodity prices and the concomitant economic and political uncertainty.

CLIMATE RISKS AND PREDICTABILITY OF COMMODITY RETURNS AND VOLATILITY: EVIDENCE FROM OVER 750 YEARS OF DATA

We analyze whether metrics of climate risks, as captured primarily by changes in temperature anomaly and its stochastic volatility (SV), can predict returns and volatility of 25 commodities, covering the overall historical period of 1258 to 2021. To this end, we apply a higher-order nonparametric causality-in-quantiles test to not only uncover potential in-sample predictability in the entire conditional distribution of commodity returns and volatility but also to account for nonlinearity and structural breaks which exist between commodity returns and the metrics of climate risks. We find that, unlike in the misspecified linear Granger causality tests, climate risks do predict commodity returns and volatility, though the impact on the latter is stronger, in terms of the coverage of the conditional distribution. Insights from our findings can benefit academics, investors, and policymakers in their decision-making.

Semi-parametric single-index predictive regression models with cointegrated regressors

This paper considers the estimation of a semi-parametric single-index regression model that allows for nonlinear predictive relationships. This model is useful for predicting financial asset returns, whose observed behaviour resembles a stationary process, if the multiple nonstationary predictors are cointegrated. The presence of cointegrated regressors imposes a single-index structure in the model, and this structure not only balances the nonstationarity properties of the multiple predictors with the stationarity properties of asset returns but also avoids the curse of dimensionality associated with nonparametric regression function estimation. An orthogonal series expansion is used to approximate the unknown link function for the single-index component. We consider the constrained nonlinear least squares estimator of the single-index (or the cointegrating) parameters and the plug-in estimator of the link function, and derive their asymptotic properties. In an empirical application, we find some evidence of in-sample nonlinear predictability of U.S. stock returns using cointegrated predictors. We also find that the single-index model in general produces better out-of-sample forecasts than both the historical average benchmark and the linear predictive regression model.

Firm-level business uncertainty and the predictability of the aggregate U.S. stock market volatility during the COVID-19 pandemic

In this paper, we analyze the predictive role of firm-level business expectations and uncertainties derived from a panel survey of U.S. 1750 business executives from 50 states for the realized variance (sum of daily squared log-returns over a month) of the S&P500 index over the monthly period of September 2016 to July 2021. Unlike standard models, our predictive framework adopts a time-varying approach due to the existence of multiple structural breaks in the relationship between volatility and the predictors in the model, which in turn leads to statistically insignificant causal effects in a constant parameter set-up. Our time-varying results suggest that the predictive power of firm-level business uncertainty is concentrated during the early part of the sample associated with the U.S.-China trade war and towards the end of our data coverage in the wake of the outbreak of the COVID-19 pandemic. Since in-sample predictability does not guarantee the same over an out-sample, we also conducted a full-fledged forecasting exercise to show that subjective expectations and uncertainties associated with sales growth rates and employment produce statistically significant predictability gains over January 2020 to July 2021, given an in-sample of September 2016 to December 2019. Our results suggest that subjective measures of business uncertainty at the firm level indeed capture predictive information regarding aggregate stock market uncertainty, which has important implications for investors and economic projections at the policy level.

Gold and tail risks

In this study, we consider as a predictor of gold return predictability, an alternative measure of systematic risk using the tail risk obtained from the four variants (Adaptive, Symmetric absolute value, Asymmetric slope and Indirect GARCH) of the Conditional Autoregressive Value at Risk (CAViaR) of Engle and Manganelli (2004). We conduct distinct analyses for the gold-tail risk nexus for both 1% and 5% VaRs across the in-sample and out-of-sample forecasts. The results of the in-sample predictability indicate contrasting effects of own tail risk and oil tail risk (a proxy for global risk factor) with negative and positive effects, respectively on gold returns reinforcing the safe haven property of the gold market against global risk. Evidence of the out-of-sample predictability supports the inclusion of both own tail risk and oil tail risk over the benchmark model and single-predictor (own tail risk) model for improved out-of-sample forecasts of gold returns. The results leading to these conclusions are robust to alternative proxies for oil price and magnitudes of VaR.

Connectedness of green investments and uncertainties: new evidence from emerging markets

PurposeThis study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.Design/methodology/approachThis study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.FindingsGreen returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.Originality/valueThis study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

Technology shocks and stock returns: A long-term perspective

Using patent data dating as far back as 1870, we compute local and global technology shocks. United States data reveal strong evidence of in-sample predictability particularly at longer horizons and during economic expansions, principally driven by global technology factors. We also discover strong evidence of time-varying predictability for the United States. We find that the global technology shock is a stronger time-varying predictor of stock returns, predicting returns in as many as 41% of sub-samples of data. Using OECD data for 11 countries, we find evidence of time-varying return predictability for seven countries; however, in-sample and long horizon predictability are, in general, weak. • Historical patent data are used to compute local and global technology shocks. • United States data reveal strong evidence of in-sample predictability. • Predictability is found only at longer horizons and during economic expansions. • Much stronger evidence of time-varying predictability for United States is found. • Seven of the 11 OECD countries reveal strong time-varying return predictability.

Forecasting the Equity Risk Premium in the Korean Stock Market: A Factor Analysis Approach

Purpose: This article investigates stock return predictability in the Korean stock market using the methodology of dynamic factor analysis. Design/methodology/approach: This article collects monthly data on the equity risk premium on the KOSPI and twelve financial and macroeconomic variables spanning from October 2000 to December 2020 and evaluates the forecasting performance of the dynamic factor predictive regression model by comparing in-sample and out-of-sample predictability with those of individual predictors. Findings: The article finds that the dynamic factor predictive regression exhibits statistically and economically significant in-sample predictability for the future equity risk premium for the KOSPI, as strongly as the best individual predictor can do. Also, the dynamic factor approach can outperform the benchmark historical average in out-of-sample predictability. The detailed analysis of the diffusion indexes reveals that each factor captures different information from various financial and macroeconomic variables relevant for return prediction and the diffusion indexes can deliver better forecasts of the future equity risk premium. Research limitations/implications: There exist different regression methods to combine forecasts comparable to the dynamic factor predictive model such as the forecast combination method by Rapach et al. (2010) and the bagging method by Inoue and Kilian (2008) and Jordan et al. (2017). The study proposes to compare the performance of these models with that of the dynamic factor predictive model in the Korean stock market as future research. Originality/value: The article is the first attempt to apply the dynamic factor predictive regression model to a large set of financial and macroeconomic data in Korea and evaluate its in-sample and out-of-sample predictability in comparison to those of individual predictive variables.

A Note on Forecasting the Historical Realized Variance of Oil-Price Movements: The Role of Gold-to-Silver and Gold-to-Platinum Price Ratios

We examine the predictive value of gold-to-silver and gold-to-platinum price ratios, as proxies for global risks affecting the realized variance (RV) of oil-price movements, using monthly data over the longest available periods of 1915:01–2021:03 and 1968:01–2021:03, respectively. Using the two ratios, we find statistically significant evidence of in-sample predictability for increases in RV for both ratios. This finding also translates into statistically significant out-of-sample forecasting gains derived from these two ratios for RV. Given the importance of real-time forecasts of the volatility of oil-price movements, our results have important implications for investors and policymakers.

PALM OIL PRICE–EXCHANGE RATE NEXUS IN INDONESIA AND MALAYSIA

In this study, we extend the literature analyzing the predictive content of commodity prices for exchange rates by examining the role of palm oil price. Our analysis focuses on Indonesia and Malaysia, the two top producers and exporters of palm oil, and utilizes daily data covering the period from December 12, 2011 to March 29, 2021, which is partitioned into two sub-samples based on the COVID-19 pandemic. Relying on a methodology that accommodates some salient features of the variables of interest, we find that on average the in-sample predictability of palm oil price for exchange rate movements is stronger for Indonesia than for Malaysia. While Indonesia’s exchange rate appreciates due to a rise in palm oil price regardless of the choice of predictive model, Malaysia’s exchange rate only appreciates after adjusting for oil price. However, both exchange rates do not seem to be resilient to the COVID-19 pandemic as they depreciate amidst dwindling palm oil price. Similar outcomes are observed for the out-of-sample predictability analysis. We highlight avenues for future research and the implications of our results for portfolio diversification strategies.

Forecasting WTI crude oil futures returns: Does the term structure help?

Nelson-Siegel (NS) factors extracted from the term structure of WTI oil futures are shown to predict subsequent WTI holding period returns in-sample. This in-sample predictability is not diminished by augmenting with macroeconomic indicators or oil market specific predictors. Allowing the decay factor in the Nelson-Siegel model to vary over time improves in-sample predictions at medium horizon return forecasts. We conduct out-of-sample forecasting exercises on models that use NS factors, such as a simple two factor model that uses a composite leading indicator along with the NS decay factor, and a LASSO model that combines NS factors with macroeconomic indicators and oil market specific predictors. These models significantly reduce forecast errors relative to a no change benchmark across a range of return horizons and futures contract maturities. We also find consistent evidence that models that use the NS factors result in trading strategies with higher Sharpe ratios and better skewness properties than buy and hold strategies and historical mean strategies.

Predicting the Oil Market

We study the performance of many traditional and novel, text-based variables for in-sample and out-of-sample forecasting of oil spot, futures, and energy company stock returns, and changes in oil volatility, production, and inventories. After controlling for small-sample biases, we find evidence of in-sample predictability. Our text measures, derived using energy news articles, hold their own against traditional variables. While we cannot identify ex-ante rules for selecting successful out-of-sample forecasters, an analysis of all possible two-variable models reveals out-of-sample performance above that expected under random variation. Our findings provide new directions for identifying robust forecasting models for oil markets, and beyond.

HISTORICAL FORECASTING OF INTEREST RATE MEAN AND VOLATILITY OF THE UNITED STATES: IS THERE A ROLE OF UNCERTAINTY?

Uncertainty is known to have negative impact on financial markets through its effects on investors’ decisions. In the wake of the “Great Recession”, quite a few recent studies have highlighted the role of uncertainty in predicting in-sample movements of interest rates. Since in-sample predictability does not guarantee out-of-sample forecasting gains, in this paper, we used historical daily and monthly data for the US to forecast mean and volatility of interest rate. The results show that changes in uncertainty measure movements fail to add any significant statistical gains to the forecast of interest rates for the US. In other words, policy makers in the US are not likely to improve their accuracy of future movements of the policy rate’s mean and volatility by incorporating information derived from changes in metrics of uncertainty.

Predictability of GCC stock returns: The role of geopolitical risk and crude oil returns

Stock return predictability has always been one of the central themes of finance literature, given its crucial implications for investment decisions, risk management, and financial and monetary policymaking. This paper evaluates the in-sample and out-of-sample stock return predictive power of the global and Saudi geopolitical risk indices and crude oil returns in the context of six Gulf Cooperation Council (GCC) countries. Monthly data from February 2007 to December 2019 and the feasible generalized least square (FGLS) estimator for predictive modelling by Westerlund and Narayan (2012, 2015) are used. Global and Saudi GPR indices show weak evidence of in-sample predictability of excess stock returns. However, the out-of-sample forecasts show that only the global geopolitical risk index provides superior prediction in the context of Kuwaiti and Omani stock markets, compared to the historical average benchmark model. Crude oil prices are shown to be a better predictor in most cases, in both in-sample and out-of-sample forecast models The results imply that crude oil returns can be used for active prediction of GCC stock market returns, once econometric issues are accounted for. The findings remain mostly unaffected when excess risk adjusted returns are used.

An observation regarding Hamilton’s recent criticisms of Kilian’s global real economic activity index

Hamilton (2019) offers several criticisms of the global real economic activity (REA) index suggested in Kilian (2009). This study deals with Hamilton (2019)’s arguments regarding the ability of the index to forecast commodity prices. Using in-sample population-level predictability tests, Hamilton (2019) argues that world industrial production index growth rate performs better than Kilian’s REA Index. We show that from an out-of-sample population-level predictability perspective, the REA index performs just as well if not better than world industrial production index. The Clark and West (2007) no population-level predictability null hypothesis is rejected at the same rate if not higher for the REA index, especially as the forecast horizon increases.

Predicting stock returns using crude oil prices: A firm level analysis of Nigeria's oil and gas sector

The decision over asset holding is traditionally premised on the double-edge objective of returns maximization and risk minimization. While the class of assets held by a typical investor depends on his attitude towards risks, an optimal investment portfolio requires a strategic combination of alternative assets (commodities, T-bills, stocks etc). To this end, our paper analyzes the role of crude oil prices in predicting stock returns, in addition to the traditional factors, particularly the returns on risk-free assets (such as, T-bills) as enunciated by the Capital Asset Pricing Model (CAPM). We also consider the possibility of nonlinearities in the nexus between crude oil prices and stock returns of nine major oil and gas companies that are currently listed on the Nigerian Stock Exchange over the period of January 2014 to November 2019. Our results show significant in-sample predictability of stock returns using crude oil prices, thereby affirming our argument that oil price matters in the predictability of stock returns for some listed oil and gas firms in Nigeria. We also offer evidence for the role of asymmetries in the predictability of stock returns for the majority of the listed oil and gas companies in Nigeria. By implication, the increasing exposure of the earnings, vis-à-vis, the share prices of some major oil and gas companies to negative changes in global oil prices suggests the need for diversification of their scope of operations.

The British Stock Market under the Structure of Market Capitalization Value: New Evidence on its Predictive Content

Purpose: The aim of our paper is twofold. First, we examine the predictive ability of log book-market, dividend-price, earnings-price and dividend-earnings ratios on the most recent data set of the strongest securities in the UK economy; unlike the majority of the studies in this data set, our analysis is not limited on returns but further investigates dividend and earnings growth predictability under the presence of the most recent global financial recession. Second, we exploit the long-run equilibrium relationship in two systems, [p_t,d_t,e_t] and [p_t,b_t,e_t] and examine the predictive ability of our newly formed variables, namely 〖pde〗_t and 〖pbe〗_t. Design/methodology/approach: In this study, we examine the most recent data set of Financial Times Stock Exchange 100 (FTSE 100) and analyze it based on the formation of size portfolios. The main focus is placed on the index’s returns, dividend and earnings growth rates and the predictive ability of the four financial ratios we have selected following their reputation as strong predictors. We also formulate two extra ratios based on their long-run equilibrium relationship. Finding: Our study’s main findings can be summarized as following. First, we retrieve evidence that in-sample return predictability is evident in the medium and large-sized portfolios and is better captured by 〖pde〗_t at 35% and 47% equivalently. Second, forecasts on dividend growth are even more linked to the size criterion we employ. Third, in-sample regressions of continuously compounded earnings growth rate show that most predictive benefits are obtained by 〖dp〗_t in the medium portfolio with an R^2 of 45%. Research limitations/implications: A first constraint is the forecasters we employ; we have used the most indicative ones due to their popularity in similar data sets but there are other macroeconomic variables such as spreads and interest rates that could be tested in future research. Also, we could examine the sensitivity of our results on whether we use nominal, excess or real returns and then, attempt to alter our data’s frequency so as to address the seasonality effect observed mainly in dividends and earnings. Originality/value: We believe that our paper contributes to the ongoing debate of the traits that make return predictable and the information included in either dividends or earnings to explain that predictability. Finally, the novelty of this paper lies in the links it tries to retrieve among market capitalization value and predictability in a market whose predictive components have not been entirely explored. Our paper may prove informative to investors focused on short-term forecasting and interested in the effects of size in portfolio formation.

Mining for Oil Forecasts

We study the performance of many traditional and novel, text-based variables for in-sample and out-of-sample forecasting of oil spot, futures, and energy company stock returns, and changes in oil volatility, production, and inventories. After controlling for small-sample biases, we find evidence of in-sample predictability. Our text measures, derived using energy news articles, hold their own against traditional variables. While we cannot identify ex-ante rules for selecting successful out-of-sample forecasters, an analysis of all possible two-variable models reveals out-of-sample performance above that expected under random variation. Our findings provide new directions for identifying robust forecasting models for oil markets, and beyond.

Can Crude Oil Price be a Predictor of Stock Index Return? Evidence from Vietnamese Stock Market

This paper tests the predictive power of crude oil price returns in forecasting Vietnamese stock index returns. We used the VN index and HNX index to calculate stock index returns and WTI and BRENT oil prices. Using a daily sample from 4th January 2006 to 31st December 2017, our analysis focused on both in-sample and out-of-sample predictability by applying the Westerlund and Narayan (2015) feasible generalized least square (FGLS) estimator which corrects persistency heteroskedasticity and endogeneity problems. We showed that the crude oil prices are reliable predictors of Vietnamese stock index returns. In terms of in-sample predictability, thirteen out of sixteen predictive regression were significant. We found that the BRENT crude oil index is slightly more powerful than the WTI crude oil price in predicting Vietnam stock index returns with seven out of eight regressions being significant compared to six out of eight from that of the WTI oil price. In terms of out-of-sample predictability, our results were also complemented by a robustness test, i.e. competing with a constant return model which used the historical average as the predictive value.

Data-Mining Bootstrap Procedure with Potential Predictors in Forecasting Models: Evidence from Eight Countries in the Asia-Pacific Stock Markets

Data-Mining Bootstrap Procedure with Potential Predictors in Forecasting Models: Evidence from Eight Countries in the Asia-Pacific Stock Markets-Data-Mining, Bootstrap, In-Sample Predictability, Out-of-Sample Predictability, Forecasting