Kyoto University · Economics, Econometrics and Finance
Professor Shinji Kakinaka's research lab specializes in econophysics and financial market analysis, focusing on the complex dynamics of cryptocurrency markets through advanced statistical and fractal methodologies. The lab investigates multifractal structures, asymmetric volatility, and scale-dependent price–volatility relationships using techniques such as detrended fluctuation analysis (DFA) and stable distribution modeling. A central theme is understanding how market efficiency, investor behavior, and risk characteristics vary across time horizons and market regimes, particularly under stress conditions like the COVID-19 pandemic. The lab also explores the implications of investor heterogeneity and scale preferences for portfolio optimization and financial risk management.
Figures are computed from collected data and may differ slightly.
This study investigates asymmetric multifractality and market efficiency of the major cryptocurrencies during the COVID-19 pandemic while accounting for different investment horizons. By applying the asymmetric multifractal detrended fluctuation analysis, we show that the outbreak affected the efficiency property of price behaviors differently between short- and long-term horizons. After the outbreak, the markets exhibited stronger multifractality in the short-term but weaker multifractality in
Asymmetric relationship between price and volatility is a prominent feature of the financial market time series. This paper explores the price–volatility nexus in cryptocurrency markets and investigates the presence of asymmetric volatility effect between uptrend (bull) and downtrend (bear) regimes. The conventional GARCH-class models have shown that in cryptocurrency markets, asymmetric reactions of volatility to returns differ from those of other traditional financial assets. We address this i
This study investigates the scale-dependent structure of asymmetric volatility effect in six representative cryptocurrencies: Bitcoin, Ethereum, Ripple, Litecoin, Monero, and Dash. By developing the dynamical approach of DFA-based fractal regression analysis, we detect whether the volatility of price changes is positively or negatively related to return shocks at different time scales. We find that the asymmetric volatility phenomenon varies by scale and cryptocurrency, and the structure is time
Stable distribution is one of the attractive models that well describes fat-tail behaviors and scaling phenomena in various scientific fields. The approach based upon the method of moments yields a simple procedure for estimating stable law parameters with the requirement of using momental points for the characteristic function, but the selection of points is only poorly explained and has not been elaborated. We propose a new characteristic function-based approach by introducing a technique of s
The mean-DCCA portfolio is known to consider the assets' nonlinearity and scaling properties by embedding the fractal correlation into the mean-variance criterion, with specific strategies under the assumption that the scale preference of investors is constant.We examine whether accounting for changes in investors' scale preference in response to market conditions improves portfolio performance.A portfolio with preference on short-scales is effective under market uncertainty, while long-scale pr
The recent emergence of cryptocurrencies such as Bitcoin and Ethereum has posed possible alternatives to global payments as well as financial assets around the globe, making investors and financial regulators aware of the importance of modeling them correctly. The Levy's stable distribution is one of the attractive distributions that well describes the fat tails and scaling phenomena in economic systems. In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency m
Asymmetric relationship between price and volatility is a prominent feature of the financial market time series. This paper explores the price-volatility nexus in cryptocurrency markets and investigates the presence of asymmetric volatility effect between uptrend (bull) and downtrend (bear) regimes. The conventional GARCH-class models have shown that in cryptocurrency markets, asymmetric reactions of volatility to returns differ from those of other traditional financial assets. We address this i
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