Aggregate Idiosyncratic Volatility Research Articles

Factor momentum in the Chinese stock market

Based on 10 commonly used factors, we construct a novel factor momentum strategy in the Chinese stock market, which earns an annualized return of 9.91 %, with a Sharpe ratio of 1.15. Factor momentum subsumes stock momentum, high-priced momentum, and industry momentum, digests its component factors and a variety of anomalies, and represents the momentum anomaly in China. Furthermore, mispricing correction helps explain factor momentum, which produces stronger returns during higher aggregate idiosyncratic volatility periods as well as among stocks with higher information asymmetry and short-sale constraints. Exposure to factor premiums and the manifestation of predictability determine factor momentum in China.

Has Idiosyncratic Volatility Increased? Not in Recent Times

This study successfully replicates the key findings of Campbell, Lettau, Malkiel, and Xu (2001). We document that aggregate idiosyncratic volatility increases over their sample period from 1962 to 1997. In out-of-sample analysis from 1926 to 1962 and 1998 to 2017, we find that idiosyncratic volatility (IV) decreases, suggesting that their finding is sample-specific. We compare their measure of IV with those obtained from models such as the Fama and French (1993) three-factor model and find that they are very similar. The Campbell et al. (2001) volatility measures can only be estimated at the aggregate level. An advantage of asset pricing model-based IVs is that they can be estimated at the stock level. Employing these stock-level IV measures, we examine trends in a variety of IV series and how IV relates to commonly analyzed firm characteristics. In doing so, we provide further insight into IV and its time-series trends.

Market returns and risk factors for the emerging economies

ABSTRACT This paper creates daily factors (much like Fama and French) and monthly aggregate idiosyncratic volatility measures for 15 emerging economies. Data deficiencies, including firm balance sheet data, are bypassed by constructing daily country factors through principal components. We focus on each country’s aggregate idiosyncratic volatility. We derive empirical measures for these volatilities and test them against prior known results. In addition, we examine the attributes of these measures by employing regime-switching, trend tests, and multiple structural breaks. An expository effort is also made to interpret the derived factors and examine their performance. Our findings indicate that the derived volatility series are robust and empirically valid in correctly detecting most of the exact timing of known volatility periods. The derived factors and volatility series will be posted online for free public access and are expected to be updated on a regular basis.

Does idiosyncratic volatility matter at the global level?

I test the existence of a time-series relationship between the aggregate idiosyncratic volatility and the market index return at the global level by introducing various global measures of aggregate idiosyncratic volatility. I offer four definitions of aggregate global idiosyncratic volatility (GIVOL) based on factor models and two other definitions, which are free from factor models. Regardless of whether I use model-dependent or model-independent measures, I find no evidence of a robust and significant relation between the aggregate GIVOL and the global market return. This result is valid for four different sub-periods and four different subsamples reflecting the different states of the economy and the stock market. It is also robust to the inclusion of several control variables. As global idiosyncratic volatility is not a priced factor in the intertemporal asset pricing framework, the results indicate that international diversification is still effective in eliminating idiosyncratic volatility despite the globalization process.

Does Idiosyncratic Volatility Matter at the Global Level?

We test the existence of a time-series relationship between the aggregate idiosyncratic volatility and the market index return at the global level by introducing various global measures of aggregate idiosyncratic volatility. We offer four definitions of aggregate global idiosyncratic volatility (GIVOL) based on factor models and two other definitions, which are free from factor models. Regardless of whether we use model-dependent or model-independent measures, we find no evidence of a robust and significant relation between the aggregate GIVOL and the global market return. This result is valid for four different sub-periods and four different subsamples reflecting the different states of the economy and the stock market. It is also robust to the inclusion of several control variables. As global idiosyncratic volatility is not a priced factor in the intertemporal asset pricing framework, the results indicate that international diversification is still effective in eliminating idiosyncratic volatility despite the globalization process.

Patterns and Pricing of Idiosyncratic Volatility in the French Stock Market

We investigate the time series behavior of idiosyncratic volatility and its role in asset pricing in France. We find that both aggregate idiosyncratic and market volatility exhibit regime switching behavior similar to that in the U.S. and other developed countries. Furthermore, we find a marginally significant negative IVOL effect in the French stock market. We add new evidence to the mounting results questioning the ubiquity of the IVOL effect which highlights the importance of country verification of so called anomalies in the US, even in developed markets.

Aggregate idiosyncratic volatility and stock return predictability: Evidence from the Korean stock market

ABSTRACTEmploying various proxies of idiosyncratic volatility, we investigate the relationship between aggregate idiosyncratic volatility and future market returns in Korea. We find a positive association between aggregate idiosyncratic volatility and subsequent market returns, and especially, that the idiosyncratic volatility measure proposed by Garcia, Mantilla-García, and Martellini (2014) has a striking forecasting power for future market excess returns. The predictive power of aggregate idiosyncratic volatility is robust to several considerations including weighting schemes, market variance and trading exchanges. Overall, our empirical results indicate that aggregate idiosyncratic volatility plays an important role in predicting future stock returns in the Korean stock market.

Patterns and Pricing of Idiosyncratic Volatility in French Stock Market

Purpose: The current research is to investigate the time series behavior of idiosyncratic volatility (IVOL) and its role in asset pricing in France in a twenty-year testing period. Design/methodology/approach: We test for the presence of trends in aggregate idiosyncratic and market volatility using Bunzel and Vogelsang’s (2005) t-dan test. We follow Bekaert et al. (2012) to test for regime shifts of both aggregate idiosyncratic and market volatilities. And then, we employ portfolio level analysis and cross-sectional univariate Fama-MacBeth regressions to examine the relationship between IVOL and cross-sectional stock returns in French stock market. Findings: First, we find that both idiosyncratic and market volatility do not exhibit long-term trends. Instead, their patterns are consistent with regime switching behavior. Second, though we initially find a strong significant negative IVOL effect in the French stock market which is robust in bi-variate Fama-MacBeth regressions, the negative IVOL effect is becoming marginal significant when we control for SIZE, BM, momentum, and short-term reversal simultaneously. Our new evidence suggests that there is a marginal IVOL effect in the French stock market adding to the increasing number of studies questioning the ubiquity of the negative IVOL puzzle. Originality/value: First, we present the first empirical evidence on examining the trends of both aggregate idiosyncratic and market volatilities, and the pricing role of IVOL in French stock market. We draw an attention for both academia and practitioners on an individual developed stock market. Second, we add new evidence to the mounting results questioning the ubiquity of the IVOL effect. This highlights the importance of country verification of so called anomalies in the US, even in developed markets. Finally, we confirm earlier evidence both aggregate idiosyncratic and market volatilities in the French stock market exhibits regime switching behavior rather than showing a long-term time trends.

Trend in aggregate idiosyncratic volatility

We suggest that price interaction among stocks is an important determinant of idiosyncratic volatility. We demonstrate that as more (less) stocks are listed in the markets, price interaction among stocks increases (decreases), and hence stocks, on average, become more (less) volatile. Our results show that price interaction has a significant positive effect of idiosyncratic volatility. The results of various robustness checks indicate that the effect of price interaction is still significant to the presence of liquidity, newly listed firms, cash flow variables, business cycle variables, and market volatility. Once the price interaction effect is taken into account, no trend remains in idiosyncratic volatility. We conclude that there is no trend, but a reflection of the positive effect of price interaction on idiosyncratic volatility.

Aggregate Idiosyncratic Volatility, Dynamic Aspects of Loss Aversion, and Narrow Framing

We test the dynamic aspects of the loss aversion feature of Kahneman and Tversky (Prospect theory: an analysis of decision under risk. Econometrica 47:263–291, 1979) and find that idiosyncratic volatility is negatively associated with unrealized gains of stock returns. Moreover, we show that this negative relationship is stronger for stocks with high individual investors’ holdings. Finally, we show that controlling for firm age as defined by Fink et al. (What drove the increase in idiosyncratic volatility during the internet boom? J Financ Quant Anal 45:1253–1278, 2010) eliminates the significance of retail trading proportions as a driver of idiosyncratic volatility. These findings are robust to price, sentiment, and IPO dates. Bivariate vector auto-regression confirms the causality of unrealized gains of stock returns on idiosyncratic volatility.

Aggregate idiosyncratic volatility, dynamic aspects of loss aversion, and narrow framing

Aggregate idiosyncratic volatility, dynamic aspects of loss aversion, and narrow framing

Can Exposure to Aggregate Tail Risk Explain Size, Book-to-Market, and Idiosyncratic Volatility Anomalies?

We examine the impact of aggregate tail risk on return dynamics of size, book-to-market ratio, and idiosyncratic volatility sorted portfolios. Using changes in VIX Tail Hedge Index (ΔVXTH) as a proxy for aggregate tail risk, and controlling for market, size, book-to-market, and aggregate volatility risk, we document significant portfolio return exposures to tail risk. In particular, portfolios that contain small, value and volatile stocks exhibit consistently positive and statistically significant tail risk betas, whereas portfolios of big, growth and non-volatile stocks exhibit negative tail risk betas. We posit that due to their positive tail risk exposures, tail risk-averse investors demand extra compensation to hold small, value, and high idiosyncratic volatility stocks. Our results offer a tail risk-based explanation to size, value, and idiosyncratic volatility anomalies.

Market Efficiency and Idiosyncratic Volatility in Vietnam

We study idiosyncratic volatility in the Vietnamese stock market between July 2007 and February 2015. We show that there is no relationship between the idiosyncratic volatility and average returns in the Vietnamese market. Our results also indicate that neither the aggregate market volatility, the aggregate idiosyncratic volatility nor can predict market returns. Finally, we find no trend in idiosyncratic volatility and a decreasing trend of market volatility over the sample period. In addition, we find strong evidence of both short as well as long term reversal in Vietnam stock market during the sample period.

A new approach to measuring riskiness in the equity market: Implications for the risk premium

Abstract We introduce a new approach to measuring riskiness in the equity market. We propose option implied and physical measures of riskiness and investigate their performance in predicting future market returns. The predictive regressions indicate a positive and significant relation between time-varying riskiness and expected market returns. The significantly positive link between aggregate riskiness and market risk premium remains intact after controlling for the S&P 500 index option implied volatility (VIX), aggregate idiosyncratic volatility, and a large set of macroeconomic variables. We also provide alternative explanations for the positive relation by showing that aggregate riskiness is higher during economic downturns characterized by high aggregate risk aversion and high expected returns.

Dynamics of Idiosyncratic Volatility and Market Volatility: An Emerging Market Perspective

Estimating idiosyncratic volatility (IVOL) using various model-dependent and model-independent measures, we investigate the characteristics of aggregate IVOL in Malaysia over the period 1990–2008. The IVOL estimated in all models have similar patterns and has no trend over the sample period. There is evidence of episodic phenomenon. During financial crisis periods, market volatility is relatively higher than IVOL – a plausible reason is high correlation between firms' returns. Small firms and low-priced stocks appear to influence IVOL more than large firms and high-priced stocks. In Malaysia, market volatility and IVOL may predict GDP growth.

A Model-Free Measure of Aggregate Idiosyncratic Volatility and the Prediction of Market Returns

AbstractIn this paper, we formally show that the cross-sectional variance of stock returns is a consistent and asymptotically efficient estimator for aggregate idiosyncratic volatility. This measure has two key advantages: It is model free and observable at any frequency. Previous approaches have used monthly model-based measures constructed from time series of daily returns. The newly proposed cross-sectional volatility measure is a strong predictor for future returns on the aggregate stock market at the daily frequency. Using the cross section of size and book-to-market portfolios, we show that the portfolios’ exposures to the aggregate idiosyncratic volatility risk predict the cross section of expected returns.

Idiosyncratic Volatility Covariance and Expected Stock Returns

Given that the idiosyncratic volatility (IDVOL) of individual stocks co-varies, we develop a model to determine how aggregate idiosyncratic volatility (AIV) may affect the volatility of a portfolio with a finite number of stocks. In portfolio and cross-sectional tests, we find that stocks whose returns are more correlated with AIV innovations have lower returns than those that are less correlated with AIV innovations. These results are robust to controlling for the stock's own IDVOL and market volatility. We conclude that risk-averse investors pay a premium for stocks that pay well when AIV is high, consistent with our model.

A Model-Free Measure of Aggregate Idiosyncratic Volatility and the Prediction of Market Returns

In this paper, we formally show that the cross-sectional variance of stock returns is a consistent and asymptotically efficient estimator for aggregate idiosyncratic volatility. This measure has two key advantages: it is model-free and observable at any frequency. Previous approaches have used monthly model based measures constructed from time series of daily returns. The newly proposed cross-sectional volatility measure is a strong predictor for future returns on the aggregate stock market at the daily frequency. Using the cross-section of size and book-to-market portfolios, we show that the portfolios’ exposures to the aggregate idiosyncratic volatility risk predict the cross-section of expected returns.

Aggregate Idiosyncratic Volatility

AbstractWe examine aggregate idiosyncratic volatility in 23 developed equity markets, measured using various methodologies. We find no evidence of upward trends after extending the sample to 2008. Instead, idiosyncratic volatility is well described by a stationary autoregressive process that occasionally switches into a higher-variance regime that has relatively short duration. We also document that idiosyncratic volatility is highly correlated across countries. Most of the time variation in idiosyncratic volatility can be attributed to variation in a growth opportunity proxy, total (U.S.) market volatility, and in most specifications, the variance premium, a business cycle sensitive risk indicator.

Does Aggregate Riskiness Predict Future Economic Downturns?

We propose options' implied and physical measures of riskiness and investigate their performance in predicting future returns on the U.S. equity market. The predictive regressions indicate a positive and significant relation between time-varying riskiness and expected market returns. The significantly positive link between aggregate riskiness and market risk premium remains intact after controlling for the S&P500 index option's implied volatility (VIX), aggregate idiosyncratic volatility, and a large set of macroeconomic and financial variables. We present a theoretical framework that justifies the positive link between aggregate riskiness and equity premium. We also provide alternative explanations for the positive relation by showing that aggregate riskiness is higher during economic downturns characterized by high aggregate risk aversion and high expected returns.