TIPS and the VIX: Spillovers from Financial Panic to Breakeven Inflation in an Automated,Nonlinear Modeling Framework

This paper examines the determinants of the breakeven inflation rate (BEI) on U.S. Treasury Inflation Protected Securities. After controlling for several measures of liquidity, inflation expectations and inflation uncertainty; financial fear itself (proxied with the Volatility Index or VIX) remains a primary influence on BEI. To delve into the mechanism underlying this association, the VIX is decomposed, using intraday data, into conditional variance and the variance premium capturing risk aversion. Aside from the 2008 crisis, most of the effect emanated from the variance premium. Following the crisis, indicators of bank insolvency risk gain prominence as well. Lastly, an automated nonlinear model finds convex effects of variance, and diminishing returns to insolvency risk and liquidity.     

Variance swaps,non-normality and macroeconomic and financial risks

This paper studies the determinants of the variance risk premium and discusses the hedging possibilities offered by variance swaps. We start by showing that the variance risk premium responds to changes in higher order moments of the distribution of market returns. But the uncertainty that determines the variance risk premium – the fear by investors to deviations from normality in returns – is also strongly related to a variety of macroeconomic and financial risks associated with default, employment growth, consumption growth, stock market and market illiquidity risks. We conclude that the variance risk premium reflects the market willingness to pay for hedging against these financial and macroeconomic sources of risk. An out-of-sample asset allocation exercise shows that the inclusion of the variance swap reduces the modified value-at-risk with respect to a portfolio holding exclusively the equity market portfolio.     

Tail risk and investors’ concerns: Evidence from Brazil

I estimate tail risk for Brazil from January 2001 to July 2020 and investigate the origins of tail risk variation. The tail risk measure peaks at stock market crashes, financial crises, political shocks and disaster events such as the coronavirus pandemic. Moreover, I find that tail risk is countercyclical, has strong predictive power for market returns and negatively predicts real economic activity. In order to identify the investors’ concerns associated with tail risk, I extract daily news from the largest financial newspaper in Brazil. The co-movement between news and tail risk indicates that tail risk variation is mainly driven by disaster concerns, followed by economic and government uncertainty. While economic uncertainty explains the countercyclical property of tail risk, investors only require compensation for bearing tail risk implied by disaster concerns. Similarly, tail risk negatively impacts real outcomes because of the disaster concerns that it identifies. These findings support recent models explaining asset pricing puzzles with time-varying disaster risk.     

Stock returns and volatility: Another look

Most empirical work examining the intertemporal mean-variance relationship in stock returns has tended to use relatively simple specifications of the mean and especially of the conditional variance. We augment the information set to include economic variables that other researchers have found to be important and use GARCH-M models to explore the relation between volatility and expected stock returns. We find that the additional variables have little impact on the conditional variance and that any intertemporal relationship between volatility and stock returns is weak or unstable. Our results signal the need for theoretical models of the intertemporal volatility-return relationship, and call for further studies of the determinants of the conditional variance of stock returns.     

Long-run risk-return trade-offs

Excess market returns are correlated with past market variance. This dependence is statistically mild at short horizons (thereby leading to a hard-to-detect risk-return trade-off, as in the existing literature) but increases with the horizon and is strong in the long run (i.e., between 6 and 10 years). From an econometric standpoint, we find that the long-run predictive power of past market variance is robust to the statistical properties of long-horizon stock-return predictive regressions. From an economic standpoint, we show that, when conditioning on past market variance, conditional versions of the traditional CAPM and consumption-CAPM yield considerably smaller cross-sectional pricing errors than their unconditional counterparts.     

Time series properties of ARCH processes with persistent covariates

We investigate the time series properties of a volatility model, whose conditional variance is specified as in ARCH with an additional persistent covariate. The included covariate is assumed to be an integrated or nearly integrated process, with its effect on volatility given by a wide class of nonlinear volatility functions. In the paper, such a model is shown to generate many important characteristics that are commonly observed in financial time series. In particular, the model yields persistence in volatility, and also well predicts leptokurtosis. This is true for any type of volatility functions considered in the paper, as long as the covariate is integrated or nearly integrated. Stationary covariates cannot produce important characteristics observed in many financial time series. We present two empirical applications of the model, which show that the default premium (the yield spread between Baa and Aaa corporate bonds) affects stock return volatility and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that the model generally outperforms GARCH and FIGARCH at relatively lower frequencies.     

Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.     

中国股票市场国际联动性研究

本文选取2000~2015年全球40支股票指数日收盘价,通过建立收益率网络和DCC MVGARCH模型波动率网络对中国股票市场国际联动性进行实证分析。研究表明,随着经济全球化的加深,全球股市收益率和波动率联动逐渐增强;全球金融危机和欧债危机期间,收益率联动网络具有小世界性;中国与全球股市长期处于割裂状态,但在全球金融危机期间与其他市场联系加强。在全球经济形势复杂多变的情况下,中国应针对性采取措施促进股市发展,以分享全球金融一体化利益。     

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.     

Stock market and macroeconomic volatility comparison: an US approach

In 2007, as the US subprime mortgage market began to fall down, which reached its peak with the catastrophic collapse of the Lehman Brothers, no one was aware of that this was going to be the worst financial crisis since the Great Depression. Evaluating the advantages and disadvantages connected with financial globalization demands a pure understanding of the influence of financial volatility. Up to the present few researches focused on analyzing macroeconomic volatility of national economies. Therefore, the aim of the paper is to compare the forecast performance of stock market and macroeconomic volatility of US economy between 2007 and 2010. Accordingly, two different types of financial time series were generated, namely weekly stock returns and quarterly return on investment. Firstly, the appropriate model was determined via time series analysis. Secondly, the relevant ARCH-type model was implemented. Finally, conditional variance forecast performance of models was presented with respect to confidence interval. Furthermore, coefficient of correlation between squared residuals and coefficient of conditional variance was given.     

Investor sentiment and country exchange traded funds: Does economic freedom matter?

Using unbalanced panel data of 27 iShares MSCI country-specific exchange traded funds (ETFs) over the period 1996–2014, this paper applies quantile regression to examine the impacts of global, foreign, and U.S. investor sentiments on the returns of the ETFs traded in the U.S. markets. We further investigate whether a country’s economic freedom affects the relationship between investor sentiments and ETF returns. We find that ETF returns are strongly determined by investor sentiments and the ETF expense ratio. The quantile regression approach reveals that high-return ETFs are positively sensitive to changes in global sentiment (measured by market turnover, VIX, U.S. federal funds rate), foreign sentiment (measured by current account balance, inflation, market turnover, public debt), U.S. sentiment, currency exchange ratio, and expense ratio, while negatively influenced by economic freedom and Asian proxy. The effects of VIX and foreign inflation are a reversal; that is, returns from lower (higher) quantiles have a negative (positive) relation with VIX and foreign inflation. Not all components of economic freedom affect returns equally.     

Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities

This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment confirms that the procedure works well in practice. Implementing the procedure with actual S&P500 option-implied volatilities and high-frequency five-minute-based realized volatilities indicates significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of macro-finance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns.     

Aggregate stock market behavior and investors’ low risk aversion

This paper studies whether investors’ high risk aversion can be avoided in a representative-agent model that is able to explain aggregate stock market behavior in the US financial market. We present a consumption-based asset pricing model with a representative agent who has a ‘catching up with the Joneses’ preference to show that high risk aversion can be avoided in a representative-agent model that can help explain many of the empirically observed properties of the aggregate stock market return, including the equity premium and risk-free rate puzzles, the predictability of long-horizon stock returns, and the ‘leverage effect’ in return volatility.     

What do movements in financial traders’ net long positions reveal about aggregate stock returns?

Previous financial economics studies have successfully identified the existence of informed trading in futures markets; however, there is no study on the specific type of strategy chosen by informed agents to maximize profits. To fill this gap in the literature, we investigate the importance of movements in futures traders’ net long positions in predicting aggregate equity market returns. This study finds that movements in the net long positions of bond, commodity, and stock futures traders are strong predictors of aggregate stock returns as they outperform a large number of popular return predictors both in and out of sample. In addition, a one-standard-deviation change in futures traders’ net long positions can lead to an increase (decrease) of up to 3.4% (4.12%) in annualized market excess equity returns. The study’s first-order autocorrelation results reveal an absence of persistence in the net long predictors. A vector autoregression decomposition shows that the economic source of financial traders’ net long position predictive power stems predominantly from the discount rate and cash flow channels. Overall, the study finds that financial traders are informed traders who are able to anticipate future aggregate cash flows and associated discount rate news.     

Modelling volatility by variance decomposition

In this paper, we propose two parametric alternatives to the standard GJR-GARCH model of Glosten et al. (1993), based on additive and multiplicative decompositions of the variance. They allow the variance of the model to have a smooth time-varying structure. The suggested parameterizations describe structural change in the conditional and unconditional variances where the transition between regimes over time is smooth. The main focus is on the multiplicative decomposition of the variance into an unconditional and conditional components. Estimation of the multiplicative model is discussed in detail. An empirical application to daily stock returns illustrates the functioning of the model. The results show that the ‘long memory type behaviour’ of the sample autocorrelation functions of the absolute returns can also be explained by deterministic changes in the unconditional variance.     

A new approach to risk-return trade-off dynamics via decomposition

This paper revisits the puzzling time series relation between risk premium and conditional volatility by proposing a flexible risk-return trade-off that allows for a variety of possible shapes and incorporates potential nonlinearities inherent in excess return dynamics. We derive this flexible risk-return relation using the decomposition approach of Anatolyev and Gospodinov (2010), which splits excess returns into the product of absolute returns and signs. Using this decomposition strategy, we study four major international financial markets. The empirical results support a significant and positive risk-return trade-off that is driven by conditional volatility, market timing and the interdependence between the two components, which is generically related to return skewness.     

Economic policy uncertainty and stock market returns: New evidence

This paper investigates the predictive performance of the Chinese economic policy uncertainty (EPU) index constructed by Davis, Liu, and Sheng (2019) in forecasting the returns of China’s stock market. Using the univariate and bivariate predictive regression model, we confirm that the monthly EPU index can significantly and negatively impact the next month’s stock returns, and has better out-of-sample predictability than the existing EPU index and several macroeconomic variables. By comparing the forecasting effect of the EPU index before and during special events with sharply increased uncertainty, we find that the EPU’s forecasting power decline rapidly when an event of sharply increased uncertainty occurs. Finally, our conclusions are consistent through a batch of robustness tests.     

对上证指数波动性的实证分析

股票价格频繁波动是股票市场中最明显的特征之一。ARCH类模型可以成功的预测金融资产收益的方差。通过对我国股价指数的统计描述,表明我国金融资产收益率存在自回归条件异方差,并表现出非正态性。并且应用GARCH、TARCH、EGARCH模型理论,进一步分析了日收益率波动的条件异方差性、非对称性。     

Great Places to Work®: Resilience in Times of Crisis

We study the resilience of the “100 Best Companies to Work for in America” in times of financial crisis by analyzing their long‐term financial performance. Apart from implementing methods that tackle the statistical problems of stock returns, we use a conditional model to measure financial performance in periods of market growth (bull markets) and market downturn (bear markets). We find that best places to work are indeed resilient in times of crisis since neither their financial performance nor their systematic risk are affected during bear markets: top companies continue to outperform the market during periods of crisis, and the performance of lower‐ranked great workplaces does not deteriorate. Moreover, we find that previous studies were overestimating performance, and only great workplaces on the top half of the rankings exhibit positive excessive returns. © 2015 Wiley Periodicals, Inc.     

The impact of sentiment and attention measures on stock market volatility

We analyze the impact of sentiment and attention variables on the stock market volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. We apply a state-of-the-art sentiment classification technique in order to investigate the question of whether sentiment and attention measures contain additional predictive power for realized volatility when controlling for a wide range of economic and financial predictors. Using a penalized regression framework, we identify the most relevant variables to be investors’ attention, as measured by the number of Google searches on financial keywords (e.g. “financial market” and “stock market”), and the daily volume of company-specific short messages posted on StockTwits. In addition, our study shows that attention and sentiment variables are able to improve volatility forecasts significantly, although the magnitudes of the improvements are relatively small from an economic point of view.