We consider a limit order book, where buyers and sellers register to trade a security at specific prices. The largest price the buyers on the book are willing to pay to buy the security is called the market bid price, and the smallest price the sellers on the book are willing to receive to sell the security is called the market ask price. Market ask price is always greater than the market bid price, and these prices move upwards and downwards due to new arrivals, market trades, and cancellations. When the two prices become equal, a trade occurs, and immediately after the trade, these prices bounce back, that is, the market bid price decreases and the market ask price increases. We model these two price processes as `` bouncing geometric Brownian motions ( GBM)'': that is, the price processes evolve according to two independent GBMs between trading times . We show that, under this model,  the inter-trading times follow an inverse Gaussian distribution , and the logarithmic returns between consecutive trading times follow a normal inverse Gaussian distribution. We show that the logarithmic trading price process is a renewal reward process, and that, under a suitable scaling, this renewal reward process converges to a standard Brownian motion \to \to0 . Finally, we develop a GBM asymptotic model for trading prices, and derive a simple and effective prediction formula . We illustrate the effectiveness of the prediction methods with an example using real stock price data.
We consider a limit order book, where buyers and sellers register to trade a security at specific prices. The largest price buyers on the book are willing to offer is called the market bid price, and the smallest price sellers on the book are willing to accept is called the market ask price. Market ask price is always greater than market bid price, and these prices move upwards and downwards due to new arrivals, market trades, and cancellations. We model these two price processes as " bouncing geometric Brownian motions ( GBMs)", which are defined as exponentials of two mutually reflected Brownian motions. We then modify these bouncing GBMs to construct a discrete time stochastic process of trading times and trading prices, which is parameterized by a positive parameter \delta. Under this model, it is shown that the inter-trading times are inverse Gaussian distributed , and the logarithmic returns between consecutive trading times follow a normal inverse Gaussian distribution. Our main results show that the logarithmic trading price process is a renewal reward process, and under a suitable scaling, this process converges to a standard Brownian motion as \delta\to 0. We also prove that the modified ask and bid processes approach the original bouncing GBMs as \delta\to0 . Finally, we derive a simple and effective prediction formula for trading prices, and illustrate the effectiveness of the prediction formula with an example using real stock price data.
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