高階系統性風險之研究

Essays on High Order Systematic Risk

陳德峰(Te-Feng Chen)

指導教授 ： 張森林

國立臺灣大學/管理學院/財務金融學研究所/博士(2014年)

https://doi.org/10.6342/NTU.2014.00139

摘要

本論文主題在於了解高階系統性風險對於橫斷面資產定價的影響。本論文包含兩篇文章。
第一章探討股票市場波動性的波動性是否在資產定價上扮演重要的狀態變數。投資者對於市場波動度具有風險趨避的行為。除此之外，投資者也擔心市場波動度本身的波動度可能使得市場波動度變化地更劇烈。本文發展一個以股票市場為基礎的三因子模型；其中，橫斷面股票報酬率決定於市場風險、波動性風險、以及波動性的波動性風險。利用高頻率的標準普爾500 指數選擇權資料，本文估計出股票市場波動性的波動性。實證結果支持理論，本文發現高波動性的波動性風險的公司，將有10.5％年股票報酬率風險溢酬。本文指出，股票市場波動性的波動性是資產定價上是重要的風險因子。
第二章探討研究非線性的風險報酬抵換理論。如果市場報酬中存在高階風險溢酬，則高階風險溢酬則應該被定價於橫斷面的資產報酬補償其承擔的高階系統性風險。考慮了市場報酬的非常態性，本文提出一個無模型假設的資本資產定價模型，其模型中使用高階系統性風險來定價橫斷面的資產報酬。研究結果指出，第二階系統性風險是顯著地並且負向地被定價；因此，結果隱含證券市場線為倒Ｕ曲線。有較高第二階系統性風險的股票，將有較高的波動性與較高的機會獲取市場波動性風險溢酬所隱含的上方波動性收益，因此將有較高的市場價格以及較低的風險溢酬。本文建構交易策略於捕抓第二階風險溢酬，實證結果指出於第一階系統性風險建構的投資組合估計出第二階風險溢酬為−12.00%、於第二階系統性風險建構的投資組合估計出第二階風險溢酬為−15.60%、以及於風險中立波動性敏感度建構的投資組合估計出第二階風險溢酬為−16.08%。本文發現實證結果與模型一致，第二階風險溢酬與市場波動性風險溢酬相關、第二階風險溢酬可以解釋橫斷面資產波動性與報酬的難題、以及解釋反向操作系統性風險的難題。本文提供對於高階系統性風險的認知，也說明了非線性的風險報酬抵換的重要性。

關鍵字

波動性的波動性 ； 預期股票報酬 ； 波動性風險溢酬 ； 無模型架設之資本資產定價模型 ； 高階系統性風險 ； 究非線性的風險報酬抵換理論

並列摘要

My dissertation aims at understanding the high order systematic risks in the cross-section of equity returns. It contains two chapters.
Chapter One extends Bollerslev, Tauchen, and Zhou (2009) to derive an market-based equilibrium asset pricing model in which, along with market return volatility, the volatility of market-return volatility (volatility-of-volatility) is a state variable and important for pricing individual stocks. While investors are averse to high market volatility, there is possibility that high market volatility could fluctuate even further, which could drive investors to hedge the increasing uncertainty by buying defensive stocks and dumping crash-prone stocks. To test the model, we use the high-frequency S&P 500 index option data to estimate a time series of the variance of market variance. Consistent with the model, we find that defensive stocks (i.e., returns co-move more positively with volatility-of-volatility) have lower expected returns. A hedge portfolio long in defensive stocks and short in crash-prone stocks yields a significant 10.5 percent average annual return. Furthermore, the volatility-of-volatility risk largely subsumes the valuation effect of volatility risk documented in previous studies. In sum, our model and test results provide a unified framework to better understand the importance of volatility-of-volatility risk in asset pricing.
Chapter Two studies the feature of nonlinear risk-return trade-off. If market returns have high order risk premiums, expected stock returns should comprise compensation for bearing the corresponding high order systematic risks. Allowing for non-normality in market moments, this paper presents an approximate capital asset pricing model in which high order risks are important for pricing individual stocks. Our results show that the second-order risk is significantly and negatively priced and contributes to an inverse-U shaped relation between cross-sectional expected returns and systematic risks. Stocks with high exposure to the second-order risk are volatile and are capable of earning the upside variance potential implied by the negative market variance risk premium. We develop trading strategies to mimic the second-order risk premium and we show that the resulting mimicking factor, on average, per year is −12.00% estimated from the first-order co-moment risks, −15.60% from the second-order co-moment risks, and −16.08% from the risk-neutral variance beta. Based on the mimicking factors, we find evidence consistent with our model that the second-order risk premium (1) is related to market variance risk premium, (2) accounts for the total volatility puzzle, the idiosyncratic volatility puzzle, and the MAX puzzle, and (3) helps explain the betting-against-beta premium. Our study provides a unified framework for better understanding of high order risk-return tradeoff and sheds light on the role of the second-order risk premium.

並列關鍵字

Volatility-of-volatility ； Expected stock returns ； Variance risk premium ； Model-free CAPM ； Cumulants ； High order risks ； Nonlinear risk-return trade-off

參考文獻

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