The bias between the expected realised variance under the historical measure and the risk neutral probability introduces the concept of the risk premium. How does the market variance risk premium vary over time or look like in the future? Our work introduced a probabilistic modeling of the...

This paper proposes a new method for predicting jump arrivals in stock markets with high-frequency limit order book data. We introduce a new model architecture, based on Convolutional Long Short-Term Memory with attention, to apply time series representation learning with memory and to focus the...

The geometric Brownian motion is routinely used as a dynamic model of underlying project value in real option analysis, perhaps for reasons of analytic tractability. By characterizing a stochastic state variable of future cash flows, this paper considers how transformations between a state...

Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series.This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series. To this end, we use the cross-recurrence plot analysis as a...

The existing delta hedging strategies are model-based: either the strategies are explicitly based on a given option pricing model or reinforcement learning is used to train an agent based on synthetic data generated by a given volatility model. In this paper, we empirically train an agent for...