Rapid detection of the switching point in a financial market structure using the particle filter

Abstract

We apply the particle filter for the quick and accurate estimation of a switching point in a financial market based on a recently developed theoretical model, the potentials of unbalanced complex kinetics (PUCK) model, which fulfils all empirically stylized facts such as fat-tailed distribution of price changes and the anomalous diffusion in a short-time scale. We show the efficiency of an optimized driving force in particle filtering for the estimation of the parameters of the PUCK model, using a simulation study. As an example, we apply the method to the dollar-yen exchange market before and after the biggest earthquake in Japan in March 2011. With this fast and efficient estimation method, we can clearly confirm that the statistics of the time series of exchange rate changed drastically at the time of the arrival of the quake in Tokyo area, implying that the earthquake worked as a trigger for the market's switching point.

Financial Brownian Particle in the Layered Order Book Fluid and Fluctuation-Dissipation Relations

“…Extending the initial intuition of Bachelier, the random nature of financial price fluctuations is presently mostly understood as resulting from the the imbalance of buy and sell orders at each time step [12]. In order to explain non-Gaussian properties of market price fluctuations, extensions in the form of Langevin-type equations with an inertia term have been proposed [13][14][15][16][17][18][19][20].…”

Intra-day response of foreign exchange markets after the Tohoku-Oki earthquake

“…During the reviewing process, we learned that Yura et al [26] also investigated a change point of the USD/JPY market on March 11, 2011. Although their method may be able to be applied online, our results have at least three advantages: We explicitly analyzed the reaction of the USD/JPY market by using the dataset of earthquakes, while the work of Yura et al [26] only showed that there was a change point of the USD/JPY market soon after the start of Tohoku-Oki earthquake; we did not use any mathematical models for dynamics of the USD/JPY market and earthquakes, and thus our results are model-free; the change point we detected was at least 12 s earlier than the change point found by Yura et al [26].…”

Financial Knudsen number: Breakdown of continuous price dynamics and asymmetric buy-and-sell structures confirmed by high-precision order-book information

“…Recently, several novel properties of financial prices series have been documented, such as the negative auto-correlation of price changes at short times, the abnormal diffusion of prices [17][18][19], the long-auto correlation of volatility [20], the long-memory process of sign of orders [21,22], the property that implicitly underlies the present considerations as financial multifractality [23,24], large price changes characterized by the gap of a limit order book (containing no quotes between prices) [25], the existence of endogenous feedback mechanisms that are well characterised by the self-excited Hawkes model [26], rich market impact dynamics revealed in nonlinear price changes caused by submitted orders [27][28][29], and the Fokker-Plank description for the queue dynamics of the order book [30].…”