The Low Volatility effect in China

The Low Volatility effect in China

19-05-2021 | Insight
In our recent study, we uncover the presence of a strong Low Volatility effect in the Chinese A-share market. This effect is not subsumed by other factors, such as investment and profitability. It also likely stems from the behavioral biases of local individual investors who dominate the market.
  • David Blitz
    Chief Researcher
  • Pim  van Vliet, PhD
    van Vliet, PhD
    Lead Portfolio Manager Conservative Equities
  • Matthias Hanauer

Speed read

  • Low-risk anomaly is pervasive in the Chinese A-share market
  • Low volatility factor is a distinct phenomenon in this market
  • Its likely source is behavioral biases of local individual investors

The Chinese A-share market is the second largest and most liquid in the world after only the US. A major difference, however, is that while the latter is highly institutionalized, the former remains dominated by local individual investors. Perhaps owing to this, the Chinese A-share market is known for its speculative activity and is characterized by higher volatility than the US and international markets.

In our view, A-shares provide a very interesting testing ground for factors, especially since the market is not fully integrated into the global financial system and ownership patterns are clearly different from most large equity markets. While the literature on stock returns in China is still growing and maturing, several studies already indicate that the low-risk anomaly is also present in this market. Our recent research adds to the existing literature by thoroughly examining the volatility effect in China.

Climate Series – don’t miss a thing
Climate Series – don’t miss a thing

Low-risk anomaly is pervasive in Chinese A-share market

Our study builds upon the Fama-French framework, that distinguishes factors in a consistent way and empirically tests them on a like-for-like basis with other factors. First, we show that there is a strong low volatility effect in China. Contrary to the predictions of prevailing theoretical asset pricing models, the least risky stocks in China exhibited the highest returns, while the riskiest ones earned the lowest returns, over the sample period of almost two decades. Consistent with previous research on the low-risk effect, we also conclude that the main driver of the anomaly is volatility, rather than market beta.

Moreover, we find very similar results for shorter (up to 1-month) and longer (up to 5-year) volatility estimation periods. We have seen the volatility effect to be robust across sectors and persistent over time. Indeed, we observe that a Fama-French style volatility factor delivered a higher risk-adjusted premium in China than each of the other factors in the Fama-French-Carhart model, over the December 2000-December 2018 period.

Other factors do not subsume low-risk anomaly

Second, we establish that the low-risk anomaly in China is a distinct phenomenon. While Novy-Marx1 and Fama and French2 have argued that the low-risk anomaly can be subsumed by the investment and profitability factors, in the US stock market, we find that this result does not carry over to the Chinese counterpart.

The low volatility strategy exhibits good practical investability properties in the A-share market

We also show that our results cannot be attributed to investability frictions such as trading costs, since we have noted that the alpha is also strongly present among the largest and most liquid stocks. Moreover, the required turnover is low, which demonstrates that the low volatility strategy exhibits good practical investability properties in the A-share market.

In addition, we note that the volatility effect observed in China is uncorrelated with the same effect in other markets. For global investors, this means that it offers what we believe to be a unique additional source of alpha. The result also argues against a common risk-based explanation for the low volatility anomaly.

Individual investor behavioral biases also help explain low volatility effect

Third, we shed new light on some of the explanations often brought forward for the volatility effect. Unlike the US market, which is highly institutionalized, trading on the Chinese stock exchanges is dominated by individual investors. Our finding that the low volatility effect is also strong in the Chinese market suggests that the phenomenon could have multiple drivers.

A popular explanation for the volatility effect relates to the role of benchmarks, as understood in a classical principal-agent setup3. If delegated portfolio managers with leverage constraints are benchmarked against a market index, they will have a rational preference for risky stocks over safe ones, which can lead to a flattening of the risk-return relation.

However, it cannot be ruled out that, despite not being benchmarked directly, individual investors aim to outperform other investors. This would imply a relative performance objective similar to institutional investors. In addition, individual investors may be more prone to behavioral biases than institutional ones4, or may tend to overpay for risky stocks because of their lottery-ticket features5.

The strong low volatility effect in China is consistent with previous empirical evidence for the US, developed and emerging equity markets

We believe the strong low volatility effect we have witnessed in China is consistent with previous empirical evidence for the US, developed and emerging equity markets. These ‘out-of-sample’ results help us to better understand the low volatility effect. We do, however, acknowledge that the inclusion of A-shares into the MSCI Emerging Markets Index might affect their behavior. This will certainly be an interesting topic for future research.

1 Novy-Marx, R., 2014, “Understanding defensive equity”, NBER working paper no. 20591.
2 Fama, E.F., and French, K.R., 2016, “Dissecting anomalies with a five-factor model”, Review of Financial Studies 29(1): 69–103.
3 Baker, M., Bradley, B., and Wurgler, J., 2011, “Benchmarks as limits to arbitrage: Understanding the low-volatility anomaly”, Financial Analysts Journal 67(1): 40–54.
4 Blitz, D., Falkenstein, E., and van Vliet, P., 2014, “Explanations for the volatility effect: An overview based on the CAPM assumptions”, Journal of Portfolio Management 40(3): 61–76.
5 Barberis, N., and Huang, M., 2008, “Stocks as lotteries: The implications of probability weighting for security prices”, American Economic Review 98(5): 2066–2100.


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