本文探討虛擬貨幣選擇權隱含波動率曲面之資訊含量，並以 Bitcoin 與 Ethereum為研究對象。本文不同於僅檢驗單一隱含波動率或未來報酬之研究，而是將未來現貨市場狀態區分為三個層次：未來累積報酬、極端正負報酬事件，以及由高頻現貨資料建構之正向與負向跳躍變異數，用以辨識選擇權市場資訊究竟主要反映於一般報酬方向，或更集中於尾部事件與不連續價格變動。本文使用 2021 年 1 月 1 日至 2025 年 12 月 31 日之 BTC 與 ETH 選擇權成交資料與高頻現貨價格資料，建構每日隱含波動率曲面，並萃取價平隱含波動率、25-delta 風險反轉、25-delta 蝶式價差與隱含波動率期限結構等曲面摘要指標。實證上，本文以時間序列預測迴歸與線性機率模型檢驗上述指標對不同未來市場狀態之資訊含量，並控制過去累積報酬、過去已實現波動率與過去選擇權交易量。
實證結果顯示，隱含波動率曲面摘要指標對未來一般累積報酬之預測關係相對有限，表示選擇權市場資訊未必能直接轉化為平均報酬方向的穩定訊號。相較之下，當被解釋變數改為極端報酬事件時，部分曲面摘要指標在特定資產與期間下呈現顯著性，顯示選擇權市場資訊與尾部事件風險具有一定關聯，但其跨資產與跨期間一致性仍有限。進一步觀察正負跳躍後，曲面摘要指標呈現更明確的資訊含量，其中 25-delta 風險反轉對未來正向跳躍變異數具有最一致的正向關係；負向跳躍方面，價平隱含波動率在 ETH 樣本中呈現較穩定的正向關係。整體而言，本文結果顯示，虛擬貨幣選擇權隱含波動率曲面具有一定前瞻性資訊，但其資訊內容主要反映於尾部事件與不連續價格變動，而非未來一般累積報酬。本文之貢獻在於將虛擬貨幣選擇權資訊含量之分析由單一隱含波動率延伸至隱含波動率曲面摘要指標，並透過 BTC 與 ETH 之並列分析，提供主要虛擬資產選擇權市場資訊含量差異之實證證據。

This study examines the information content embedded in the implied volatility surfaces of cryptocurrency options, using Bitcoin and Ethereum as the main research assets. Rather than focusing only on a single implied volatility measure or future average returns, this study classifies future spot market outcomes into three layers: cumulative returns, extreme positive and negative return events, and positive and negative jump variation constructed from high-frequency spot prices. This design allows the analysis to identify whether options market information is mainly reflected in the direction of ordinary returns or is instead concentrated in tail events and discontinuous price movements. Using BTC and ETH option transaction data and high-frequency spot price data from January 1, 2021 to December 31, 2025, this study constructs daily implied volatility surfaces and extracts four representative surface measures: atthe-money implied volatility, 25-delta risk reversal, 25-delta butterfly, and the implied volatility term structure. The empirical analysis applies predictive time-series regressions and linear probability models, while controlling for past cumulative returns, past realized volatility, and past option trading volume.
The results show that implied volatility surface measures have relatively limited predictive relations with future ordinary cumulative returns, suggesting that information from the options market does not necessarily translate into stable directional signals for average returns. In contrast, when the dependent variables are replaced by extreme return events, some surface measures become significant for specific assets and horizons, indicating that options market information is related to tail-event risk, although the evidence is not fully consistent across assets and forecast horizons. After further distinguishing between positive and negative jumps, the implied volatility surface exhibits clearer information content. In particular, 25-delta risk reversal shows the most consistent positive relation with future positive jump variation, while at-the-money implied volatility is more closely related to negative jump variation in the ETH sample. Overall, the findings suggest that cryptocurrency option implied volatility surfaces contain forward-looking information, but this information is mainly associated with tail events and discontinuous price movements rather than ordinary cumulative returns. This study contributes to the literature by extending the analysis of cryptocurrency option information content from a single implied volatility measure to surface-level summary measures and by providing comparative evidence on BTC and ETH option markets.

Alexander, C., Deng, J., Feng, J., and Wan, H. (2023). Net buying pressure and the information in bitcoin option trades. Journal of Financial Markets, 63, 100764.
Bakshi, G., Kapadia, N., and Madan, D. (2003). Stock return characteristics, skew laws, and the differential pricing of individual equity options. The Review of Financial Studies, 16(1),101–143.
Barndorff-Nielsen, O. E., and Shephard, N. (2004). Power and bipower variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2(1), 1–37.
Binance. (n.d.). Kline/candlestick data. Retrieved May 13, 2026, from https://developers.binance.com/docs/binance-spot-api-docs/rest-api/market-data-endpoints
Black, F. (1976). The pricing of commodity contracts. Journal of Financial Economics, 3(1–2), 167–179.
Bollen, N. P. B., and Whaley, R. E. (2004). Does net buying pressure affect the shape of implied volatility functions? The Journal of Finance, 59(2), 711–753.
Bollerslev, T., and Todorov, V. (2011). Tails, fears, and risk premia. The Journal of Finance,66(6), 2165–2211.
Broadie, M., Chernov, M., and Johannes, M. (2007). Model specification and risk premia:Evidence from futures options. The Journal of Finance, 62(3), 1453–1490.
Caporale, G. M., and Plastun, A. (2020). Momentum effects in the cryptocurrency market after one-day abnormal returns. Financial Markets and Portfolio Management, 34(3), 251–266.
Chen, T., Deng, J., and Nie, J. (2024). Implied volatility slopes and jumps in bitcoin options market. Operations Research Letters, 55, 107135.
Chi, Y., and Hao, W. (2021). Volatility models for cryptocurrencies and applications in the options market. Journal of International Financial Markets, Institutions and Money, 75,101421.
Christensen, B. J., and Prabhala, N. R. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50(2), 125–150.
CoinGecko. (n.d.). Global cryptocurrency charts. Retrieved May 13, 2026, from https://www.coingecko.com/en/global-charts
CoinMarketCap. (n.d.). Cryptocurrency prices, charts, and market capitalizations. Retrieved May 13, 2026, from https://coinmarketcap.com/charts/
Conrad, J., Dittmar, R. F., and Ghysels, E. (2013). Ex ante skewness and expected stock returns.The Journal of Finance, 68(1), 85–124.
Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174–196.
Deribit. (n.d.). Deribit API documentation. Retrieved May 13, 2026, from https://docs.deribit.com/
Gatheral, J., and Jacquier, A. (2014). Arbitrage-free SVI volatility surfaces. Quantitative Finance, 14(1), 59–71.
Hoang, L. T., and Baur, D. G. (2020). Forecasting bitcoin volatility: Evidence from the options market. Journal of Futures Markets, 40(10), 1584–1602.
Liu, Y., Tsyvinski, A., and Wu, X. (2022). Common risk factors in cryptocurrency. The Journal of Finance, 77(2), 1133–1177.
Mixon, S. (2007). The implied volatility term structure of stock index options. Journal of Empirical Finance, 14(3), 333–354.
Mixon, S. (2011). What does implied volatility skew measure? The Journal of Derivatives,18(4), 9–25.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Retrieved May 13, 2026, from https://bitcoin.org/bitcoin.pdf
Newey, W. K., and West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.
Pan, J. (2002). The jump-risk premia implicit in options: Evidence from an integrated timeseries study. Journal of Financial Economics, 63(1), 3–50.
Pan, J., and Poteshman, A. M. (2006). The information in option volume for future stock prices. The Review of Financial Studies, 19(3), 871–908.
Vasquez, A. (2017). Equity volatility term structures and the cross section of option returns. Journal of Financial and Quantitative Analysis, 52(6), 2727–2754.
Xing, Y., Zhang, X., and Zhao, R. (2010). What does the individual option volatility smirk tell us about future equity returns? Journal of Financial and Quantitative Analysis, 45(3), 641–662.