本研究探討加密貨幣市場中，流動性條件如何影響以期貨進⾏的避險策略績效。以比特幣現貨與永續期貨的日頻與 15 分鐘資料為基礎，研究進⼀步檢視流動性不⾜ 。包括買賣價差、委託簿深度，以及 Amihud 式流動性指標是否會削弱避險的「避險成效」與「成本調整後的避險效率」。

透過固定效果迴歸、重⼤事件⼦樣本（FTX 崩盤與 2023 年美國銀⾏危機）分析，以及滾動視窗估計，本研究發現：流動性惡化將明顯削弱期貨減緩現貨波動的能⼒，尤其在市場壓⼒升⾼的時刻，其影響最為顯著。相較之下，⾼頻（15 分鐘）層次的避險效率則呈現⾼度噪音，與流動性之間缺乏穩定的系統性關聯，反映出短週期結果更容易受到市場微結構雜訊影響，⽽非結構性流動性摩擦。進⼀步地，研究也發現波動度會放⼤流動性不⾜的負面作用，顯示風險傳遞機制在⾼波動時刻最為脆弱。

本研究將流動性置於傳統避險理論的核⼼位置，擴展了過往僅以波動度為主要驅動因⼦的理論框架。同時，本研究的結論亦對交易所、機構投資⼈與⾦融監理機關具實務意涵，有助於強化市場韌性與提升加密貨幣衍⽣品的風險移轉功能。

This thesis investigates how liquidity conditions influence the performance of futures-based hedging strategies in cryptocurrency markets. Using daily and intraday (15-minute) data on Bitcoin spot and perpetual futures, the study examines whether illiquidity, reflected in bid–ask spreads, order-book depth, and Amihud-type measures, reduces hedging effectiveness and cost-adjusted hedging efficiency. Fixed-effects panel regressions, crisis-window analysis, and rolling-window estimations consistently show that liquidity deterioration weakens the ability of futures to mitigate spot volatility, especially during episodes of market stress such as the FTX collapse and the 2023 U.S. banking turmoil.

Intraday hedging efficiency is noisy and shows little systematic sensitivity to liquidity, suggesting that high-frequency outcomes are driven more by microstructure noise than by structural frictions. Volatility further amplifies the adverse effects of illiquidity, highlighting that risk-transfer mechanisms become most fragile precisely when volatility is elevated. These findings broaden classical hedging theory by positioning liquidity as a core determinant of hedging outcomes in digital-asset markets. They also offer practical guidance for exchanges, institutional investors, and regulators seeking to improve market resilience and strengthen the risk-transfer function of crypto-derivatives.

Acharya, V. V., & Pedersen, L. H. Asset pricing with liquidity risk. Journal of Financial Economics, 77(2), 375-410, 2005.
Alexander, C. Market risk analysis, Volume II: Practical financial econometrics. John Wiley & Sons, Chichester, UK, pp. 426, 2008.
Amihud, Y. Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56, 2002.
Angrist, J. D., & Pischke, J.-S. Mostly harmless econometrics: An empiricist’s companion. Princeton University Press, Princeton, NJ, pp. 392, 2009.
Baltagi, B. H. Econometric Analysis of Panel Data (4th ed.). John Wiley & Sons, Chichester, UK, pp. 366, 2008.
Baur, D. G., & Lucey, B. M. Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2), 217-229, 2010.
Baur, D. G., & Dimpfl, T. Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151, 2018.
Bernanke, B. S., & Gertler, M. Inside the black box: The credit channel of monetary policy transmission. Journal of Economic Perspectives, 9(4), 27-48, 1995.
Bessembinder, H., Carrion, A., Tuttle, L., & Venkataraman, K. Liquidity management and market structure in corporate bond markets. Journal of Financial Economics, 130(3), 491-510, 2019.
Bessembinder, H., Jacobsen, S., Maxwell, W. F., & Venkataraman, K. Capital commitment and illiquidity in corporate bonds. Journal of Finance, 74(4), 1615-1661, 2019.
Blau, B. M., & Griffith, T. G. Option-implied volatility and future returns in cryptocurrency markets. Journal of Futures Markets, 42(4), 676-693, 2022.
Bollerslev, T. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327, 1986.
Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192-198, 2017.
Bouri, E., Shahzad, S. J. H., & Roubaud, D. Cryptocurrencies as hedges and safe-havens for US equity sectors. The Quarterly Review of Economics and Finance, 75, 294-307, 2020.
Brownlees, C. T., & Gallo, G. M. Financial econometric analysis at ultra-high frequency: Data handling concerns. Computational Statistics & Data Analysis, 51(4), 2232-2245, 2006.
Brunnermeier, M. K., & Pedersen, L. H. Market liquidity and funding liquidity. Review of Financial Studies, 22(6), 2201-2238, 2009.
Chang, C.-L., McAleer, M., & Tansuchat, R. Conditional correlations and volatility spillovers between crude oil and stock index returns. The North American Journal of Economics and Finance, 25, 116-138, 2013.
Chen, C. Y.-H., Härdle, W. K., & Trück, S. Hedging cryptocurrencies with futures: Evidence from a time-varying framework. Journal of Empirical Finance, 63, 55-73, 2021.
Chordia, T., Roll, R., & Subrahmanyam, A. Market liquidity and trading activity. Journal of Finance, 56(2), 501-530, 2001.
Chordia, T., Roll, R., & Subrahmanyam, A. Evidence on the speed of convergence to market efficiency. Journal of Financial Economics, 76(2), 271-292, 2005.
Christie-David, R., & Chaudhry, M. Global risk premia in the forward foreign exchange market. Journal of Multinational Financial Management, 11(4), 385-405, 2001.
Conlon, T., & McGee, R. Safe haven or risky hazard? Bitcoin during the COVID-19 bear market. Finance Research Letters, 35, 101607, 2020.
Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28-34, 2018.
Corbet, S., Larkin, C., & Lucey, B. The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies. Finance Research Letters, 35, 101554, 2020.
Corbet, S., Larkin, C., Lucey, B., Meegan, A., & Yarovaya, L. The impact of macroeconomic uncertainty on Bitcoin returns. The European Journal of Finance, 26(14), 1261-1279, 2020.
Díaz, A., Fuertes, A. M., & Miffre, J. Bitcoin volatility: Evidence from the options market. Journal of Banking & Finance, 138, 106421, 2022.
Driscoll, J. C., & Kraay, A. C. Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), 549-560, 1998.
Durbin, J., & Watson, G. S. Testing for serial correlation in least squares regression. Biometrika, 37(3/4), 409-428, 1950.
Ederington, L. H. The hedging performance of the new futures markets. Journal of Finance, 34(1), 157-170, 1979.
Fama, E. F. Stock returns, real activity, inflation, and money. The American Economic Review, 71(4), 545-565, 1981.
Floros, C., & Vougas, D. V. Hedge ratio estimation and hedging effectiveness: The case of the Greek stock index futures market. Managerial Finance, 32(10), 812-828, 2006.
Foley, S., Karlsen, J. R., & Putniņš, T. J. Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies? Review of Financial Studies, 32(5), 1798-1853, 2019.
Froot, K. A., Scharfstein, D. S., & Stein, J. C. Risk management: Coordinating corporate investment and financing policies. The Journal of Finance, 47(5), 1629-1658, 1992.
Gandal, N., & Halaburda, H. Competition in the cryptocurrency market. Bank of Canada Working Paper No. 2014-33, 2014.
Gkillas, K., & Katsiampa, P. An application of extreme value theory to cryptocurrencies. Economics Letters, 164, 109-111, 2018.
Goyenko, R. Y., Holden, C. W., & Trzcinka, C. A. Do liquidity measures measure liquidity? Journal of Financial Economics, 92(2), 153-181, 2009.
Harvey, A. C. Forecasting, structural time series models and the Kalman filter. Cambridge University Press, Cambridge, UK, pp. 572, 1990.
Hasbrouck, J. Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press, Oxford; New York, pp. 198, 2007.
Hasbrouck, J. Trading costs and returns for US equities: Estimating effective costs from daily data. Journal of Finance, 64(3), 1445-1477, 2009.
Hausman, J. A. Specification tests in econometrics. Econometrica, 46(6), 1251-1271, 1978.
Hoechle, D. Robust standard errors for panel regressions with cross-sectional dependence. Stata Journal, 7(3), 281-312, 2007.
Karpoff, J. M. The relation between price changes and trading volume: A survey. Journal of Financial and Quantitative Analysis, 22(1), 109-126, 1987.
Keating, C., & Shadwick, W. F. A universal performance measure. Journal of Performance Measurement, 6(3), 59-84, 2002.
Klein, T., Pham Thu, H., & Walther, T. Bitcoin is not the New Gold-A comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59, 105-116, 2018.
Kristoufek, L. What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PLoS ONE, 10(4), e0123923, 2015.
Kroner, K. F., & Sultan, J. Time-varying distributions and dynamic hedging with foreign currency futures. Journal of Financial and Quantitative Analysis, 28(4), 535-551, 1993.
Lien, D., & Tse, Y. Some recent developments in futures hedging. Journal of Economic Surveys, 16(3), 357-396, 2002.
Liu, X., Chen, W., & Zhu, K. Market fragmentation in cryptocurrency exchange. SSRN Working Paper No. 4545349, 2023.
Liu, Y., & Tsyvinski, A. Risks and returns of cryptocurrency. The Review of Financial Studies, 34(6), 2689-2727, 2021.
Lo, A. W. The statistics of Sharpe ratios. Financial Analysts Journal, 58(4), 36-52, 2002.
Ljung, G. M., & Box, G. E. P. On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303, 1978.
Makarov, I., & Schoar, A. Trading and arbitrage in cryptocurrency markets. Journal of Financial Economics, 135(2), 293-319, 2020.
Mencia, J., & Sentana, E. Multivariate location-scale mixtures of normals and mean-variance-skewness portfolio allocation. Journal of Econometrics, 153(2), 105-121, 2009.
Menkveld, A. J. High frequency trading and the new market makers. Journal of Financial Markets, 16(4), 712-740, 2013.
Menkveld, A. J. The economics of high-frequency trading: Taking stock. Annual Review of Financial Economics, 8, 1-24, 2016.
Menkveld, A. J., & Wang, T. How do designated market makers create value for small-cap stocks? Journal of Financial Markets, 16(3), 571-603, 2013.
Newey, W. K., & West, K. D. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703-708, 1987.
Pagnottoni, P., & Dimpfl, T. Price discovery on Bitcoin markets. Digital Finance, 1(1), 139-161, 2019.
Park, T. H., & Switzer, L. N. Time-varying distributions and the optimal hedge ratio. Applied Financial Economics, 5(3), 131-137, 1995.
Petersen, M. A. Estimating standard errors in finance panel data sets: Comparing approaches. The Review of Financial Studies, 22(1), 435-480, 2009.
Schär, F. Decentralized finance: On blockchain- and smart contract-based financial markets. Federal Reserve Bank of St. Louis Review, 103(2), 153-174, 2021.
Shaikh, I., & Padhi, P. Performance of volatility models during periods of market stress. Studies in Economics and Finance, 31(4), 428-442, 2014.
Sharpe, W. F. The Sharpe ratio. The Journal of Portfolio Management, 21(1), 49-58, 1994.
Sortino, F. A., & Price, L. N. Performance measurement in a downside risk framework. The Journal of Investing, 3(3), 59-64, 1994.
Urquhart, A., & Zhang, H. Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. International Review of Financial Analysis, 63, 49-57, 2019.
White, H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817-838, 1980.
Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press, Cambridge, MA; London, pp. 1096, 2010.