Coincident and leading indicators of the stock market

In this paper we have two goals: first, we want to represent monthly stock market fluctuations by constructing a non-linear coincident financial indicator. The indicator is constructed as an unobservable factor whose first moment and conditional volatility are driven by a two-state Markov variable. It can be interpreted as the investors' real-time belief about the state of financial conditions. Second, we want to explore an approach in which investors may use their perceptions of the state of the economy to form forecasts of financial market conditions and possibly of excess returns. To investigate this, we build leading indicators as forecasts of the estimated coincident financial index. The leading indicators yield better within and out-of-sample performance in forecasting, not only the state of the stock market but also of excess stock returns, as compared with the performance obtained using linear methods that have been proposed in the existing literature.     

Return sign forecasts based on conditional risk: Evidence from the UK stock market index

Recent theoretical works have found a link between return sign forecastability and conditional volatility. This paper compares the predictive performance of the conditional country risk and the conditional residual risk in forecasting the direction of change in the return on the UK stock market index. The conditional country risk and the conditional residual risk are estimated using the bivariate BEKK-GARCH technique and the direction of change in the UK stock market index is modelled using the binary logit approach. Both the in-sample and the out-of-sample predictions suggest that, as a predictor, the conditional residual risk is superior to the conditional country risk. Our findings support the residual risk model while contradicting the traditional capital asset pricing model (CAPM). Moreover, our tactical asset allocation simulations show that when the conditional residual risk is used in conjunction with multiple-threshold trading strategies to guide the investment decisions, the actively managed portfolio achieves greater returns than the return on a buy and hold portfolio.     

Generalized DEA model of fundamental analysis and its application to portfolio optimization

Fundamental analysis is used in asset selection for equity portfolio management. In this paper, a generalized data envelopment analysis (DEA) model is developed to analyze a firm’s financial statements over time in order to determine a relative financial strength indicator (RFSI) that is predictive of firm’s stock price returns. RFSI is based on maximizing the correlation between the DEA-based score of financial strength and the stock market performance. This maximization involves a difficult binary nonlinear program that requires iterative re-configuration of parameters of financial statements as inputs and outputs. We utilize a two-step heuristic algorithm that combines random sampling and local search optimization. The proposed approach is tested with 230 firms from various US technology-industries to determine optimized RFSI indicators for stock selection. Then, those selected stocks are used within portfolio optimization models to demonstrate the usefulness of the scheme for portfolio risk management.     

A comparison of alternative bankruptcy prediction models

Early models of bankruptcy prediction employed financial ratios drawn from pre-bankruptcy financial statements and performed well both in-sample and out-of-sample. Since then there has been an ongoing effort in the literature to develop models with even greater predictive performance. A significant innovation in the literature was the introduction into bankruptcy prediction models of capital market data such as excess stock returns and stock return volatility, along with the application of the Black–Scholes–Merton option-pricing model. In this note, we test five key bankruptcy models from the literature using an up-to-date data set and find that they each contain unique information regarding the probability of bankruptcy but that their performance varies over time. We build a new model comprising key variables from each of the five models and add a new variable that proxies for the degree of diversification within the firm. The degree of diversification is shown to be negatively associated with the risk of bankruptcy. This more general model outperforms the existing models in a variety of in-sample and out-of-sample tests.     

Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a high-frequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting; the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias; and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility.     

Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection

We consider a GARCH-MIDAS model with short-term and long-term volatility components, in which the long-term volatility component depends on many macroeconomic and financial variables. We select the variables that exhibit the strongest effects on the long-term stock market volatility via maximizing the penalized log-likelihood function with an Adaptive-Lasso penalty. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single model without estimating a large number of parameters. In the empirical analysis, three variables (namely, housing starts, default spread and realized volatility) are selected from a large set of macroeconomic and financial variables. The recursive out-of-sample forecasting evaluation shows that variable selection significantly improves the predictive ability of the GARCH-MIDAS model for the long-term stock market volatility.     

An empirical examination of jump risk in asset pricing and volatility forecasting in China's equity and bond markets

Understanding jump risk is important in risk management and option pricing. This study examines the characteristics of jump risk and the volatility forecasting power of the jump component in a panel of high-frequency intraday stock returns and four index returns from Shanghai Stock Exchange. Across portfolio indexes, jump returns on average account for 45% to 64% of total returns when jumps occur. Market systematic jump risk is an important pricing factor for daily returns. The average jump beta is 62% of the average continuous beta for individual stocks. However, the contribution of jump risk to total risk is limited, indicating that statistically significant jumps in the stochastic process of asset price are rare events but have tremendous impacts on the prices of common stocks in China. We further document that accounting for jump components improves the performance of volatility forecasting for some equity and bond portfolios in China, which is confirmed by in-the-sample and out-of-sample forecasting performance analysis.     

Stock market volatility spillovers and portfolio hedging: BRICS and the financial crisis

The paper investigates the dynamic risk–return properties of the BRICS (Brazil, Russia, India, China, South Africa) capital markets and models potential time-varying correlations and volatility spillover effects with the US stock market. A VAR(1)–GARCH(1,1) framework contributes useful insight into US–BRICS market interactions and expands on a thin past empirical literature. A disaggregated approach pays attention to critical US–BRICS business sectors, namely the industrial and financial sectors. Significant return and volatility transmission dynamics are identified between the US and BRICS stock markets and business sectors. This is a critical input that can affect efficient global portfolio diversification and risk management strategies. Based on this empirical evidence, the study proceeds to assess effective portfolio hedge ratios and to construct optimal portfolio weights for diversified asset allocation to US–BRICS markets and business sectors.     

A neural network enhanced volatility component model

Volatility prediction, a central issue in financial econometrics, attracts increasing attention in the data science literature as advances in computational methods enable us to develop models with great forecasting precision. In this paper, we draw upon both strands of the literature and develop a novel two-component volatility model. The realized volatility is decomposed by a nonparametric filter into long- and short-run components, which are modeled by an artificial neural network and an ARMA process, respectively. We use intraday data on four major exchange rates and a Chinese stock index to construct daily realized volatility and perform out-of-sample evaluation of volatility forecasts generated by our model and well-established alternatives. Empirical results show that our model outperforms alternative models across all statistical metrics and over different forecasting horizons. Furthermore, volatility forecasts from our model offer economic gain to a mean-variance utility investor with higher portfolio returns and Sharpe ratio.     

Less is more? New evidence from stock market volatility predictability

The complex nature of stock market volatility has motivated researchers to apply a variety of predictors to obtain reliable predictive information for precise forecasting. This study seeks to examine the effectiveness of the novel Global Financial Uncertainty (GFU) indices, comprising of only five sub-indices, in predicting stock market volatility using the widely used mixed-data sampling (MIDAS) model. The results demonstrate the remarkable and stable predictive power of GFU, even during crises and global financial uncertainty shocks. Specifically, the financial uncertainty index from Europe plays a significant role in our analysis. Importantly, we find that the GFU index outperforms a large number of other indicators in stock volatility forecasting. The statistical and economic significance of the predictive power of GFU is remarkable. Our study provides significant insights for market participants and policymakers that highlight the need to prioritize global financial uncertainty.     

Dynamic correlations and volatility linkages between stocks and sukuk: Evidence from international markets

An understanding of volatility and co-movements in financial markets is important for portfolio allocation and risk management practices. The current financial crisis caused a shrinkage in values of most assets, an increased volatility and a threat to the survival of several institutional investors. Managing risks and returns within the classic portfolio theory, when correlations across securities soar, is increasingly challenging. In this paper, we investigate the volatility behavior and the co-movements between sukuk and international stock indexes. Symmetric multivariate GARCH models with dynamic conditional correlations (DCC) were estimated under Student-t distribution. We provide evidence of high correlations between sukuk and US and EU stock markets, without finding the well-known flight to quality behavior affecting Islamic bonds. We also show that volatility linkages between sukuk and regional market indexes are higher during financial crisis. We argue that investors could obtain diversification benefits including sukuk in a well-diversified equity portfolio, given their lower volatility compared to equity. But higher volatility linkages and dynamic correlations during financial crises show that they are hybrid instruments between bonds and equity. Our findings are relevant for institutional investors and asset managers that include Islamic bonds in a diversified portfolio.     

波动溢出网络视角的金融风险传染研究

当前各类经济风险交叉关联,金融系统的风险溢出效应备受关注,为刻画我国金融系统性风险传染的路径特征,本文从波动溢出网络的视角分析金融系统内部的风险传染机制。首先使用广义动态因子模型对收益波动的共同波动率成分和特质性波动率成分进行区分。然后,根据货币市场、资本市场、大宗商品交易市场、外汇市场、房地产市场和黄金市场之间的特质性波动溢出效应,利用基于TVP-VAR模型的方差分解溢出指数分析金融系统波动溢出的动态联动性和风险传递机制。在分析方向性波动溢出效应的基础上,采用方差分解网络方法构建起信息溢出复杂网络,从网络视角分析金融系统内部的风险传染特征。实证研究发现,房地产市场和外汇市场的净溢出效应绝对值相较于其他市场更大,其受其他市场风险冲击的影响强于对外风险溢出效应,而股票市场的单向对外风险溢出效应强度最大。在波动溢出的基础上,进一步考虑股市波动率指数与其他市场波动率指数进行投资组合的资产配置权重,计算了波动率指数投资组合的最优组合权重和对冲策略。研究结论有助于更好地理解我国金融系统的风险传染机制,对监管机构加强宏观审慎监管、投资者规避投资风险具有重要意义。     

PREDICTING SYSTEMATIC RISK WITH MACROECONOMIC AND FINANCIAL VARIABLES

We analyze the predictive power of several macroeconomic and financial indicators in forecasting quarterly realized betas of 30 industry and 25 size and book-to-market portfolios. We model realized betas as autoregressive processes of order 1 and include lagged values of macroeconomic and financial indicators as exogenous predictor variables. In out-of-sample forecasting exercises, forecasts using bond market variables as exogenous predictors statistically outperform forecasts from a benchmark model without any exogenous predictors. These forecasts based on bond market variables also economically outperform benchmark forecasts by providing better performance in hedging the market risk of portfolios.     

Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models

This paper investigates how geopolitical risks influence the prediction performance on the US stock market volatility with machine learning models. Further, it compares the predictive performance of individual and combination forecast methods. With SHAP algorithm, it could identify which factor has a great impact and fully extract the information of geopolitical risks in predicting. Empirical results show that military build-ups and escalation of war have great importance on predicting realized volatility among various geopolitical risks. The research further emphasizes the superior performance of machine learning and forecast combination methods, especially SVR method and trimmed mean combination. In addition, by allocating portfolio according to the machine learning-based volatility forecasts, particularly elastic net and random forest, a mean-variance investor can achieve sizeable financial benefits. Our paper provides substantial implications for political risk management and volatility forecasting.     

Investor sentiment and stock volatility: New evidence

This study investigates the predictability of sentiment measure on stock realized volatility. We propose a new investor sentiment index (NISI) based on the partial least squares method. This sentiment index outperforms many existing sentiment indicators in three aspects. First, in-sample result shows that the NISI has greater predictive power relative to the others. Most sentiment indicators show predictability in the non-crisis period only while the NISI is also effective in the crisis period. Furthermore, the NISI exhibits more prominent superiority in longer horizons forecasting. Second, further analysis indicates that the NISI has robust predictability before and after the Chinese stock market turbulence periods while the others not. Importantly, the NISI is still effective significantly after considering leverage effect while most of the others not. Finally, out-of-sample analysis demonstrates that the NISI is more powerful than other sentiment measures. This result is reproducible in different robustness checks.     

Volatility Forecasting in the Hang Seng Index using the GARCH Approach

The aim of this paper is to add to the literature on volatility forecasting using data from the Hong Kong stock market to determine if forecasts from GARCH based models can outperform simple historical averaging models. Overall, unlike previous studies we find that the GARCH models with non-Normal distributions show a robust volatility forecasting performance in comparison to the historical models. The results indicate that although not all models outperform simple historical averaging, the EGARCH based models, with non-normal conditional volatility, tend to produce more accurate out-of-sample forecasts using both standard measures of forecast accuracy and financial loss functions. In addition we test for asymmetric adjustment in the Hang Seng, finding strong evidence of asymmetries due to the domination of financial and property firms in this market.     

Benefits of international diversification with investment constraints: An over-time perspective

This paper investigates the benefits and asset allocation of the optimal international diversification for the U.S.A. investor while considering various portfolio constraints. Although the global financial market is becoming more integrated, the findings suggest that adding lower and upper weighting bounds reduces, but does not completely eliminate, the potential economic value of international investment. The addition of investment constraints makes asset allocation more feasible and decreases the volatility in portfolio return. The time-variation in the optimal asset allocation implies that fund managers should rebalance international portfolios dynamically. The out-of-sample test suggests that the Markowitz model with constraints realizes trivial improvement in mean-variance efficiency but still demonstrates significant reduction in risk.     

Illiquidity,return and risk in G7 stock markets: Interdependencies and spillovers

Trading activity in G7 stock markets reflects not only the macroeconomic and financial impact of these G7 economies in international economic growth, but also their financial interdependence. While this nexus of major stock markets has been explored in terms of volatility and return spillovers, there has been no combined analysis of return, volatility and illiquidity spillovers. We study illiquidity spillovers because they are transmissions of trading activity and, thereof, transmissions of information and market sentiment. We find that the dynamics of international stock markets are characterized by persistent illiquidity and also that illiquidity shocks are significantly correlated across markets. Furthermore, we discover Granger causal associations between risk, return and illiquidity across G7 stock market and also within each stock market. Our findings bear significance for the regulation of international financial markets and also for international portfolio diversification.     

Forecasting European stock volatility: The role of the UK

This study investigates the lead–lag relationships of volatility among European stock markets. Using weakly realized variance measures, we examine volatility spillover dynamics between the UK and other major stock markets in Europe, thereby identifying a long-run leading role for the UK market portfolio. Lagged UK volatility can significantly predict volatilities in non-UK countries, whereas lagged non-UK volatility has a limited association with UK volatility. Moreover, pairwise Granger causality estimations, predictive regression specifications, and out-of-sample validations reveal that volatility shocks in the UK are gradually reflected in market fluctuations across Europe with varying market-specific delays. Our findings support the limited attention explanation for the volatility predictability of the lagged UK equity index.     

International stock return predictability: Evidence from new statistical tests

We investigate whether stock returns of international markets are predictable from a range of fundamentals including key financial ratios (dividend-price ratio, dividend-yield, earnings-price ratio, dividend-payout ratio), technical indicators (price pressure, change in volume), and short-term interest rates. We adopt two new alternative testing and estimation methods: the improved augmented regression method and wild bootstrapping of predictive model based on a restricted VAR form. Both methods take explicit account of endogeneity of predictors, providing bias-reduced estimation and improved statistical inference in small samples. From monthly data of 16 Asia-Pacific (including U.S.) and 21 European stock markets from 2000 to 2014, we find that the financial ratios show weak predictive ability with small effect sizes and poor out-of-sample forecasting performances. In contrast, the price pressure and interest rate are found to be strong predictors for stock return with large effect sizes and satisfactory out-of-sample forecasting performance.