Crude oil price forecasting based on internet concern using an extreme learning machine

The growing internet concern (IC) over the crude oil market and related events influences market trading, thus creating further instability within the oil market itself. We propose a modeling framework for analyzing the effects of IC on the oil market and for predicting the price volatility of crude oil’s futures market. This novel approach decomposes the original time series into intrinsic modes at different time scales using bivariate empirical mode decomposition (BEMD). The relationship between the oil price volatility and IC at an individual frequency is investigated. By utilizing decomposed intrinsic modes as specified characteristics, we also construct extreme learning machine (ELM) models with variant forecasting schemes. The experimental results illustrate that ELM models that incorporate intrinsic modes and IC outperform the baseline ELM and other benchmarks at distinct horizons. Having the power to improve the accuracy of baseline models, internet searching is a practical way of quantifying investor attention, which can help to predict short-run price fluctuations in the oil market.     

Aggregate volatility expectations and threshold CAPM

We propose a volatility-based capital asset pricing model (V-CAPM) in which asset betas change discretely with respect to changes in investors’ expectations regarding near-term aggregate volatility. Using a novel measure to proxy uncertainty about expected changes in aggregate volatility, i.e. monthly range of the VIX index (RVIX), we find that portfolio betas change significantly when uncertainty about aggregate volatility expectations is beyond a certain threshold level. Due to changes in their market betas, small and value stocks are perceived as riskier than their big and growth counterparts in bad times, when uncertainty about aggregate volatility expectations is high. The proposed model yields a positive and significant market risk premium during periods when investors do not expect significant uncertainty in near-term aggregate volatility. Our findings support a volatility-based time-varying risk explanation.     

The dynamic impact of oil price shocks on the stock market and the USD/RMB exchange rate: Evidence from implied volatility indices

Using daily data from March 16, 2011, to September 9, 2019, we explore the dynamic impact of the oil implied volatility index (OVX) changes on the Chinese stock implied volatility index (VXFXI) changes and on the USD/RMB exchange rate implied volatility index (USDCNYV1M) changes. Through a TVP-VAR model, we analyse the time-varying uncertainty transmission effects across the three markets, measured by the changes in implied volatility indices. The empirical results show that the OVX changes are the dominant factor, which has a positive impact on the USDCNYV1M changes and the VXFXI changes during periods of important political and economic events. Moreover, USDCNYV1M changes are the key factor affecting the impact of OVX changes on VXFXI changes. When the oil crisis, exchange rate reform, and stock market crash occurred during 2014–2016, the positive effects of uncertainty transmission among the oil market, the Chinese stock market, and the bilateral exchange rate are significantly strengthened. Finally, we find that the positive effects are significant in the short term but diminish over time.     

信用交易保证金与股价波动关系研究

通过对境外市场保证金变动对股价波动影响的实证结果进行梳理后发现,保证金的调整对市场波动产生两种相反的效果,即流动性效果和投机性效果,保证金对市场波动的影响取决于两种效果的力量对比,如果流动性效果较强,则调整保证金对股价波动无影响或加剧市场波动,反之,则会减轻市场波动。     

Volatility analysis of precious metals returns and oil returns: An ICSS approach

This study examines volatility persistence on precious metals returns taking into account oil returns and the three world major stock equity indices (Dow Jones Industrial, FTSE 100, and Nikkei 225) using daily data over the sample period January 1995 to May 2008; the aim is to analyze market relationships before the global financial crisis. We first determine when large changes in the volatility of each market returns occur by identifying major global events that would increase fluctuations in these markets. The Iterated Cumulative Sums of Squares (ICSS) algorithm was used to identify the existence of structural breaks or sudden changes in the variance of returns. In each market the standardized residuals were obtained through the GARCH(1,1) mean equation. Our main results identify a clear relationship between precious metals returns and oil returns, while the interaction between precious metals and stock returns seems to be an independent one in the case of gold with mixed results for silver and platinum. In relation to volatility persistence, the results show clear evidence of high volatility persistence between these markets, especially during times when markets were affected by excessive volatility due to economic and financial shocks.     

国际金融市场波动溢出效应与动态相关性

在DCC GARCH、DCC EGARCH、DCC TGARCH方法下,采用中、美、日、德、英等国家1993年1月至2013年12月的金融数据,实证得出如下结论：样本国市场利率和股指波动率呈现尖峰、肥尾、有偏的特征,更为符合t分布。样本国市场利率波动表现出显著的溢出效应、杠杆效应和联动效应。样本国股指波动率对中国股指波动率的溢出效应趋于增强,特别在美国金融危机后。样本国利率波动对中国股指波动率具有一定的溢出效应和杠杆效应,但影响程度非常低。治理世界性金融风险,各国当局应加强政策协调性,合理进行风险分担。     

The impact of option listing on the price volatility and trading volume of underlying OTC stocks

This paper examines the impact of an initial option listing on the price volatility and trading volume of underlying OTC stocks. The sample is divided by market value to determine whether larger firms are impacted differently by option listing than smaller firms. We find relative trading volume increases significantly, with the small and medium market value firms showing the largest gain. However, the tests show no evidence of changes in price volatility following option listing. No significant changes were found in either the firms' betas or variance following option initiation. The results provide further evidence that option listing does not destabilize the market for the underlying stock.     

International market links and volatility transmission

This paper gauges volatility transmission between stock markets by testing conditional independence of their volatility measures. In particular, we check whether the conditional density of the volatility changes if we further condition on the volatility of another market. We employ nonparametric methods to estimate the conditional densities and model-free realized measures of volatility, allowing for both microstructure noise and jumps. We establish the asymptotic normality of the test statistic as well as the first-order validity of the bootstrap analog. Finally, we uncover significant volatility spillovers between the stock markets in China, Japan, UK and US.     

Efficient predictability of stock return volatility: The role of stock market implied volatility

This study examines the predictability of stock market implied volatility on stock volatility in five developed economies (the US, Japan, Germany, France, and the UK) using monthly volatility data for the period 2000 to 2017. We utilize a simple linear autoregressive model to capture predictive relationships between stock market implied volatility and stock volatility. Our in-sample results show there exists very significant Granger causality from stock market implied volatility to stock volatility. The out-of-sample results also indicate that stock market implied volatility is significantly more powerful for stock volatility than the oil price volatility in five developed economies.     

Heterogeneous relationship between IPO return and risk across idiosyncratic variance characteristics

This paper analyzes the levels and changes in the post-IPO stock return volatility and provides insights into market responses to the presence of firm-specific risk. First, we document a negative relation between initial idiosyncratic volatility level and the post-IPO volatility change in that initially low volatility firms have more volatility increase and vice verse. This evidence suggests fundamental firm-specific changes after the IPO. Further, we find that underpricing and short-run post-IPO returns are positively related to the initial and corresponding idiosyncratic risk level. This finding suggests that underpricing compensates investors for acquiring costly information and firm-specific risk information is being incorporated into offer prices. Finally, we find that higher long-run post-IPO performance is related to both lower initial risk level and decreasing risk in the first year after the IPO.     

Modeling volatility in sector index returns with GARCH models using an iterated algorithm

Financial market participants are interested in knowing what events can alter the volatility pattern of financial assets and how unanticipated shocks determine the persistence of volatility over time. The present paper studies these issues by detecting time periods of sudden changes in volatility by using the iterated cumulated sums of squares (ICSS) algorithm. Examining five major sectors from January 1992 to August 2003, we found that accounting for volatility shifts in the standard GARCH model considerably reduces the estimated volatility persistence. Our results have important implications regarding asset pricing, risk management, and portfolio selection. (JEL G110, G120)     

Sovereign rating changes—Do they provide new information for stock markets?

The purpose of this paper is to examine the impact of sovereign rating changes on international financial markets using a comprehensive database of 42 countries, covering the major regions in the world over the period 1995–2003. In general, we find that rating agencies provide stock and foreign exchange markets with new tradable information. Specifically, rating upgrades (downgrades) significantly increased (decreased) USD denominated stock market returns and decreased (increased) volatility. Whereas the mean response is contributed evenly by the local currency stock returns and exchange rate changes that make up the USD returns, only the foreign exchange volatility was behind the USD denominated return volatility. In addition, we find significant asymmetric effects of rating announcements. The market responses – both return and volatility – are more pronounced in the cases of downgrades, foreign currency debt, emerging market debt, and during crisis periods. This study has important policy implications for international investors’ asset allocation plans and for regulatory bodies such as the Basel Committee who increasingly rely upon Moody's, Standard and Poor's and Fitch's ratings for their regulatory regimes.     

涨跌停板制度对期货市场价格发现过程及波动性的影响--基于上海期货交易所的实证研究

本文通过检验在出现涨跌停板之后一个交易日的期货价格及其波动性的变化情况，研究了涨跌停板制度对上海期货交易所期货价格变动的影响。研究结果显示，对不同的期货品种，涨跌停板制度的影响存在一定的差异，但总体而言，涨跌停板制度并没有起到防范价格过度反应和降低市场波动性的作用。相反，在一定程度上延缓了期货市场价格发现功能的发挥，增大了市场的波动性。     

Dynamic volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday

We examine the volatility spillovers among various industries during the COVID-19 pandemic period. We measure volatility spillovers by defining the volatility of each sector in the S&P 500 index and implement a static and rolling-window analysis following the Diebold and Yilmaz (2012) approach. We find that the pandemic enhanced volatility spillovers, which reveals the financial contagion effects on the US stock market. Second, there were sudden, large changes in the dynamic volatility spillovers on Black Monday (March 9, 2020), much of it due to the energy sector shock. These findings have important implications for portfolio managers and policymakers.     

Forecasting risk in the US Dollar exchange rate under volatility shifts

Recent evidence suggests that volatility shifts (i.e. structural breaks in volatility) in returns increases kurtosis which significantly contributes to the observed non-normality in market returns. In this paper, we endogenously detect significant shifts in the volatility of US Dollar exchange rate and incorporate this information to estimate Value-at-Risk (VaR) to forecast large declines in the US Dollar exchange rate. Our out-of-sample performance results indicate that a GARCH model with volatility shifts produces the most accurate VaR forecast relative to several benchmark methods. Our contribution is important as changes in US Dollar exchange rate have a substantial impact on the global economy and financial markets.     

The impacts of investor sentiment on returns and conditional volatility of international stock markets

One of the main arguments of behavioral finance is that some properties of asset prices are most probably regarded as deviations from fundamental value and they are generated by the participation of traders who are not fully rational, thus called noise traders. Noise trader theory postulates that sentiment traders have greater impact during high-sentiment periods than during low-sentiment periods, and sentiment traders miscalculate the variance of returns undermining the mean-variance relation. The main objective of this research is to construct a model to evaluate the returns and conditional volatility of various stock market indexes considering the changes in the investor sentiment by measuring the effects of noise trader demand shocks on returns and volatility. EGARCH model is used to determine whether earning shocks have more influence on the conditional volatility in high sentiment periods weakening the mean–variance relation. This paper takes an international approach using weekly market index returns of U.S., Japan, Hong Kong, U.K., France, Germany, and Turkey. Weekly trading volumes of these indexes are regressed against a group of macroeconomic variables and the residuals are used as proxies for investor sentiment and significant evidence is found that there is asymmetric volatility in these market indexes and earning shocks have more influence on conditional volatility when the sentiment is high.     

The effects of listing changes between NASDAQ market segments

This study analyzes the effects of listing changes within NASDAQ market segments during the period of 1998 to 2005. We find that firms phased up from the NASDAQ Small Capital Market (SmallCap) to the NASDAQ National Market (NNM) experienced significant declines in bid-ask spreads, the volatility of returns, and the probability of informed trading, and firms that phased down from NNM to the SmallCap experienced decreases in bid-ask spreads, but insignificant changes in the volatility of returns and the probability of informed trading. We also estimate simultaneous equations models of bid-ask spreads, return volatility, and trading volume for both groups of firms. The results confirm that improved liquidity is associated with the listing changes for the phase-up firms. However, the simultaneous equations model suggests that the decreases in bid-ask spreads for the phase-down firms are caused by the changes in share prices.     

An analysis of firm and market volatility

In this paper, using time series data for the period 2 January 1998 to 31 December 2008 for 560 firms listed on the NYSE, we examine whether firm volatility is related to market volatility. The main contribution of this paper is that we develop an analytical framework motivating the firm-market volatility relationship. We present three new findings on volatility. First, we discover significant evidence of common volatility; for 12 out of 14 sectors, market volatility has a statistically significant effect on firm volatility for at least 50 percent of firms. Second, we discover significant evidence of size effects: for small-sized firms, there is weak evidence of commonality in volatility, while for large-sized firms there is high evidence (for as much as 75 percent of firms) of commonality in volatility. Third, we find that market volatility predicts firm volatility for firms belonging to five of the 14 sectors.     

Forecasting oil futures market volatility in a financialized world: Why speculative activities matter

We analyze the relation between volatility and speculative activities in the crude oil futures market and provide short-term forecasts accordingly. By incorporating trading volume and opening interest (speculative ratio) into the volatility dynamics, we document the subtle interaction between the two measures of which the volatility-averse behavior of speculative activities plays a considerable role in the market. Moreover, by accounting for structural changes, we find significant evidence that this behavior currently becomes weaker than in the past, which implies the oil futures market is less informative and/or less risk-averse in recent time period. Our forecasts based on these features perform very well under the predictive preferences that are consistent with the volatility-averse behavior in the oil futures market. We provide discussions and policy inferences.     

Shifts in volatility driven by large stock market shocks

This paper presents an extension of the stochastic volatility model which allows for level shifts in volatility of stock market returns, known as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are identified from the data as being bigger in absolute terms than the values of two threshold parameters of the model: one for the negative shocks and one for the positive shocks. The model can be employed to investigate different sources of stock market volatility shifts driven by market news, without relying on exogenous information. In addition to this, it has a number of interesting features which enable us to study the effects of large return shocks on future levels of market volatility. The above properties of the model are shown based on a study for the US stock market volatility.