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European Journal of Finance ; 2023.
Article in English | Web of Science | ID: covidwho-20242863


This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.

Digital Signal Processing ; : 103619, 2022.
Article in English | ScienceDirect | ID: covidwho-1885723


This paper explores a time-varying version of weak-form market efficiency that is a key component of the so-called Adaptive Market Hypothesis (AMH). One of the most common methodologies used for modeling and estimating a degree of market efficiency lies in an analysis of the serial autocorrelation in observed return series. Under the AMH, a time-varying market efficiency level is modeled by time-varying autoregressive (AR) process and traditionally estimated by the Kalman filter (KF). Being a linear estimator, the KF is hardly capable to track the hidden nonlinear dynamics that is an essential feature of the models under investigation. The contribution of this paper is threefold. We first provide a brief overview of time-varying AR models and estimation methods utilized for testing a weak-form market efficiency in econometrics literature. Secondly, we propose novel accurate estimation approach for recovering the hidden process of evolving market efficiency level by the extended Kalman filter (EKF). Thirdly, our empirical study concerns an examination of the Standard and Poor's 500 Composite stock index and the Dow Jones Industrial Average index. Monthly data covers the period from November 1927 to June 2020, which includes the U.S. Great Depression, the 2008-2009 global financial crisis and the first wave of recent COVID-19 recession. The results reveal that the U.S. market was affected during all these periods, but generally remained weak-form efficient since the mid of 1946 as detected by the estimator.

International Journal of Industrial Organization ; : 102841, 2022.
Article in English | ScienceDirect | ID: covidwho-1768177


We propose a model to show that when innovation in a given field becomes more lucrative, its direction can be distorted even though its rate rises. Higher payoffs attract innovators, making the R&D supply side more competitive. This competition endogenously shifts effort toward less promising but quicker-to-invent projects. We empirically quantify the magnitude of this distortion, in the context of pharmaceutical innovation during the Covid-19 pandemic. In the social planner solution, 74 percent more firms would have worked on vaccines and 17 percent more on novel compounds. Policy remedies include advance purchase commitments based on ex-ante value, targeted research subsidies, and antitrust exemptions for joint research ventures.