Forecasting the aggregate stock market volatility in a data-rich world

ABSTRACT

In this article, we utilize the basic lasso and elastic net models to revisit the predictive performance of aggregate stock market volatility in a data-rich world. Motivated by the existing literature, we determine several candidate predictors that have 22 technical indicators and 14 macroeconomic and financial variables. Our out-of-sample results reveal several noteworthy findings. First, few macroeconomic and financial variables and most of technical indicators have superior performance relative to the benchmark model. Second, combination forecasts are able to significantly beat the benchmark and some signal predictors Third, the lasso and elastic models with all predictors can generate more accurate forecasts than the benchmark and some other predictors in both the statistical and economic sense. Fourth, the lasso and elastic models exhibit higher forecast accuracy during periods of expansions and recessions. Finally, our findings are robust to several tests, such as different forecasting windows, forecasting models, and forecasting evaluations.     

Combining Opinion Surveys with Time-series Data to Forecast the Japanese Economy

We explore the usefulness of combining opinion surveys with time-series data to forecast Japanese economy. We demonstrate that the businessmen's judgemental variables might summarize contemporaneous information beyond that of actual series. The combined models do yield substantially more accurate one-period or multi-period-ahead forecasts and can predict the turning points better than pure time-series models.
JEL Classification Number: C32.     

Do market prices aggregate information about macroeconomic uncertainty (or risk)?

This article examines several economic variables that represent either confidence in future economic conditions or the degree of risk/uncertainty about future conditions in order to determine which contain more information about future employment and output. Some of these variables are prices, while others are from surveys. Causality tests, historical decompositions within a VAR and out-of-sample forecasts are among the tools used. This article concludes that monthly stock returns contain much more information about future economic conditions than the other variables. The spread between Moody’s BAA and AAA bonds, the spread between the constant maturity 10-year government bond and the federal funds rate, as well as uncertainty in future economic conditions as measured by the Federal Reserve Bank of Philadelphia’s Business Outlook Survey also provide information about future economic conditions. Notably, this article finds that monthly stock returns contain more information about future economic conditions than does the vix and that variables based on market prices provide more information than survey data. This result provides some support for the notion that market prices aggregate information.     

Forecasting European economic policy uncertainty

Forecasting the economic policy uncertainty in Europe is of paramount importance given the ongoing sovereign debt crisis. This paper evaluates monthly economic policy uncertainty index forecasts and examines whether ultra‐high frequency information from asset market volatilities and global economic uncertainty can improve the forecasts relatively to the no‐change forecast. The results show that the global economic policy uncertainty provides the highest predictive gains, followed by the European and US stock market realized volatilities. In addition, the European stock market implied volatility index is shown to be an important predictor of the economic policy uncertainty.     

Does non-linearity help us understand,model and forecast UK stock and bond returns: evidence from the BEYR

The usefulness of non-linear models to provide accurate estimates and forecasts remains an open empirical debate. This paper examines the nature of the estimated relationships and forecasting power of smooth-transition models for UK stock and bond returns using a range of financial and macroeconomic variables as predictors. Notably, evidence of non-linearity is stronger when the bond-equity yield ratio is used as the transition variable. This ratio measures whether stocks are over (under)-valued relative to bonds and can act as a signal for portfolio managers. In-sample results reveal noticeable differences regarding the nature of relationships between the linear and non-linear setting, while results of a recursive forecasting exercise reveal both statistical and economic improvement over a linear model. Overall, these results support the view that non-linear estimates and forecasts can provide useful information for stock market traders, portfolio managers and policy-makers.     

The nonlinear multidimensional relationship between stock returns and the macroeconomy

We use nonparametric dimension-reduction methods to extract from a set of 15 macroeconomic variables the risk factors that are priced in the stock market. The dominant factor moves with the business cycle but, because it is a nonlinear function of observed macroeconomic variables, it captures a rich set of interactions. Low-credit risk and low-inflationary expectations have a greater positive effect on stock returns when leading macroeconomic indicators are high relative to current economic activity, i.e. early in the business cycle as the economy emerges from recession. High-stock returns also arise in periods when the economy is booming relative to its leading indicators, but such periods tend to portend crashes.     

Forecasting stock market volatility and information content of implied volatility index

This study investigates the incremental information content of implied volatility index relative to the GARCH family models in forecasting volatility of the three Asia-Pacific stock markets, namely India, Australia and Hong Kong. To examine the in-sample information content, the conditional variance equations of GARCH family models are augmented by incorporating implied volatility index as an explanatory variable. The return-based realized variance and the range-based realized variance constructed from 5-min data are used as proxy for latent volatility. To assess the out-of-sample forecast performance, we generate one-day-ahead rolling forecasts and employ the Mincer–Zarnowitz regression and encompassing regression. We find that the inclusion of implied volatility index in the conditional variance equation of GARCH family model reduces volatility persistence and improves model fitness. The significant and positive coefficient of implied volatility index in the augmented GARCH family models suggests that it contains relevant information in describing the volatility process. The study finds that volatility index is a biased forecast but possesses relevant information in explaining future realized volatility. The results of encompassing regression suggest that implied volatility index contains additional information relevant for forecasting stock market volatility beyond the information contained in the GARCH family model forecasts.     

经济不确定衡量及对股票市场的影响——基于网络信息检索技术

基于网络信息检索频率的基础上,提出了一种新的衡量经济不确定程度的方法。文章的研究动机来自于经济心理学的研究成果,即经济主体往往会通过信息检索的增加来反映不确定程度的增加。网络信息检索的优点主要体现在检索面广和即时性上,并且与文字相比,网络信息检索更能够客观地反映人们的行为。研究结论显示,以网络信息检索为基础的衡量经济不确定程度的方法与其他若干相关指标相比具有明显的优势,并且这种方法与整个股票市场的收益率和波动率都有着紧密的联系。     

Equity market valuation,systematic risk and monetary policy

This study examines the relationship between equity market valuation and risk indicators that portend economic downswings. The indicators are implied options volatility, Treasury-Eurodollar (TED) spread and exchange rate. While implied volatility captures market risk in that it reflects the fear factor embedded in the price of an option, TED spread reflects the default risk premium that is priced into a key short-term credit instrument. Equity markets often show a tendency to reflect the incidence of these risk factors. And because they provide valuable information about the health of the economy, many have argued that equity market valuation be taken into account in the formulation of monetary policy. Results of this study not only show a statistically significant inverse relationship between the stock market and these risk factors, but also evidence of a cointegration. In a variance decomposition of the series, we find that equity valuation is a major contributor to the forecast error variances of each of the risk indicators, a finding that lends tacit support to the argument that risk indicators associated with the equity market be considered in monetary policy decisions.     

Economic policy uncertainty and the Chinese stock market volatility: Novel evidence

In this study, we investigate the impact of global economic policy uncertainty (GEPU) on Chinese stock market volatility. More importantly, for the first time, we explore the effects of directional GEPU based on the changing directions of GEPU and Chinese economic policy uncertainty (EPU). We make several noteworthy findings. First, the in-sample estimated results show that up and down GEPU can lead to substantially high stock market volatility for China. Second, the out-of-sample estimated results support the contention that the GEPU index is helpful for predicting volatility. Moreover, compared to GEPU alone, directional GEPU can provide more useful information that can increase the forecast accuracy. Third, we empirically find that directional GEPU is more effective in predicting Chinese stock market volatility when GEPU and EPU rise in the same month.     

Which types of commodity price information are more useful for predicting US stock market volatility?

This study aims to investigate which types of commodity price information are more useful for predicting US stock market realized volatility (RV) in a data-rich word. The standard predictive regression framework and monthly RV data are used to explore the RV predictability of commodity futures for the next-month RV on S&P 500 spot index. We utilize principal component analysis (PCA) and factor analysis (FA) to extract the common factors for each type and all types of commodity futures. Our results indicate that the futures volatility information of grains and softs has a significant predictive ability in forecasting the RV of the S&P 500. In addition, the FA method can yield better forecasts than the PCA and average methods in most cases. Further analysis shows that the volatility information of grains and softs exhibits higher informativeness during recessions and pre-crises. Finally, the forecasts of the five combination methods and different out-of-sample periods confirm our results are robust.     

Predicting stock realized variance based on an asymmetric robust regression approach

This paper introduces an asymmetric robust weighted least squares (ARLS) approach to improve the forecasting performance of the heterogeneous autoregressive model for realized volatility. The ARLS approach down-weights extreme observations to limit the bad influence of outliers on the estimated parameters. Compared with existing robust regression methods, our model further takes into account the asymmetry of outliers using a class of kernel functions. Out-of-sample results show the ARLS approach can generate more accurate forecasts of the S&P 500 index realized volatility in the statistical and economic senses. The model that considers the asymmetry of outliers gains superior performance among various robust regression competitors. The forecasting improvements also hold in other international stock markets. More importantly, the source of the predictive ability of the ARLS model comes from the less biased and more efficient parameter estimation.     

Home is Where You Know Your Volatility – Local Investor Sentiment and Stock Market Volatility

Using a new variable to measure investor sentiment we show that the sentiment of German and European investors matters for return volatility in local stock markets. A flexible empirical similarity (ES) approach is used to emulate the dynamics of the volatility process by a time‐varying parameter that is created via the similarity of realized volatility and investor sentiment. Out‐of‐sample results show that the ES model produces significantly better volatility forecasts than various benchmark models for DAX and EUROSTOXX. Regarding other international markets no significant difference between the forecasts can be observed.     

Empirical research on time-varying characteristics and efficiency of the Chinese economy and monetary policy: evidence from the MI-TVP-VAR model

Based on the general time-varying parameter vector autoregressive model and data mining technology, this study proposes a new extension mixed innovation time-varying parameter stochastic volatility vector autoregressive model and investigates time-varying characteristics and efficiencies of different shock effects on China’s monetary policy towards inflation and GDP. Using sample monthly data for 1979–2014, we utilize typical time points to illustrate the mechanisms between different economic variables via the Markov Chain Monte Carlo method and impulse response function. The empirical results show that the monetary transmission mechanism in China can be effective in the real economy, but with delay and efficiency leakage. The average delay and maximum efficiency can be measured through the MI model, which can capture accurate information of economic variables, effectively improving the precision of macroeconomic regulation and control. Meanwhile, the difference between the impacts of different channels is obvious; while the impact of interest rates is not significant, the impact of stock market is significant. The action mechanism between GDP and the inflation rate undergoes a gradual structural change, evidently displaying time-varying characteristics and a gradually weakening impact over time.     

Excessive Volatility is Also a Feature of Individual Level Forecasts

The excessive volatility of prices in financial markets is one of the most pressing puzzles in social science. It has led many to question economic theory, which attributes beneficial effects to markets in the allocation of risks and the aggregation of information. In exploring its causes, we investigated to what extent excessive volatility can be observed at the individual level. Economists claim that securities prices are forecasts of future outcomes. Here, we report on a simple experiment in which participants were rewarded to make the most accurate possible forecast of a canonical financial time series. We discovered excessive volatility in individual-level forecasts, paralleling the finding at the market level. Assuming that participants updated their beliefs based on reinforcement learning, we show that excess volatility emerged because of a combination of three factors. First, we found that submitted forecasts were noisy perturbations of participants’ revealed beliefs. Second, beliefs were updated using a prediction error based on submitted forecast rather than revealed past beliefs. Third, in updating beliefs, participants maladaptively decreased learning speed with prediction risk. Our results reveal formerly undocumented features in individual-level forecasting that may be critical to understand the inherent instability of financial markets and inform regulatory policy.     

Services,innovation and the new economy

Several distinctive stylized facts form the new economy, an information technology service sector organized in network forms of organization, an inflation rate below its fundamentals, an increase in stock market volatility, high rates of economic growth, but apart from a small information technology manufacturing sector low productivity rates. This paper presents a model where the innovation of new service varieties can explain all of these facts. First, productivity gains are no longer realized within but between firms, as the increase in variety increases value-added per employee. Whilst service innovators, such as the information technology manufacturers, get ever more productive, individual service firms will exhibit low productivity. It is this unmeasured sectoral productivity gain, which is the key element in understanding the intrinsic inertia in the price index. Finally, the paper shows that variety in the service sector enables service providers to realize rents, where fluctuations of these rents are the basis of more than proportional changes in the firms' stock market value.     

Accruals quality,managers’ incentives and stock market reaction: evidence from Europe

We investigate if accruals quality is a valuable indicator of earnings quality for stock market investors. Our particular focus is on the incremental informative value of taking into account managers’ incentives for using accruals. We propose a market-based approach for assessing the usefulness of this indicator to improve investors’ decisions. Specifically, we examine the association between accruals quality and information asymmetry among stock market participants. Our empirical study uses data on European firms and our results are consistent with a positive association between poor earnings quality and high information asymmetry. However, given some previous studies suggesting that accruals-based measures may be noisy indicators of earnings quality, we develop a method to increase the informational content of the accruals quality measure. Based on our results, we find that combining accruals quality with the dispersion in analysts’ forecasts provides a better indicator of earnings quality rather than only accruals quality.     

Empirical of the Taiwan stock index option price forecasting model – applied artificial neural network

This work presents a novel neural network model for forecasting option prices using past volatilities and other options market factors. Out of different approaches to estimating volatility in the option pricing model, this study uses backpropagation neural network to forecast prices for Taiwanese stock index options. The ability to develop accurate forecasts of grey prediction volatility enables practitioners to establish an appropriate hedging strategy at in-the-money option.     

宏观经济环境对并购活动的影响——基于时间序列数据的经验研究

基于宏观经济动因,研究经济发展水平、利率与股票价格指数对我国总体性并购活动的影响,实证检验结果表明：除利率之外,经济发展水平及股票价格均与总体性并购活动存在长期的均衡关系,并且为总体性并购活动的Granger原因;在短期内,经济发展水平波动与股票价格波动对并购活动波动均存在正向影响,而利率波动对并购活动波动存在负向影响。这说明经济发展水平与股票价格是我国并购浪潮的重要驱动因素。     

Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs

We investigate the ability of small- and medium-scale Bayesian VARs (BVARs) to produce accurate macroeconomic (output and inflation) and credit (loans and lending rate) out-of-sample forecasts during the latest Greek crisis. We implement recently proposed Bayesian shrinkage techniques based on Bayesian hierarchical modeling, and we evaluate the information content of forty-two (42) monthly macroeconomic and financial variables in terms of point and density forecasting. Alternative competing models employed in the study include Bayesian autoregressions (BARs) and time-varying parameter VARs with stochastic volatility, among others. The empirical results reveal that, overall, medium-scale BVARs enriched with economy-wide variables can considerably and consistently improve short-term inflation forecasts. The information content of financial variables, on the other hand, proves to be beneficial for the lending rate density forecasts across forecasting horizons. Both of the above-mentioned results are robust to alternative specification choices, while for the rest of the variables smaller-scale BVARs, or even univariate BARs, produce superior forecasts. Finally, we find that the popular, data-driven, shrinkage methods produce, on average, inferior forecasts compared to the theoretically grounded method considered here.