Narrow Results

Tracing the origin of information spread in social networks is a crucial yet challenging issue. Different from prior studies largely centered on discrete-time transmission frameworks, this paper fulfills source identification for continuous-time diffusion networks. Specifically, a path-wise continuous-time transmission model is first proposed to unveil the probabilistic dynamic through potential paths, based on which the probability of the incomplete cascade under partial observations can be yielded. Finally, a path-wise continuous-time source identification method (PCSI) is proposed under partial-observation conditions and path-wise modelings. To test it, three synthetic networks and two real networks are selected, with series of experiments conducted to evaluate the influence of observation size and cascade number, as well as its robustness to noises. Experiment results verify that our method is able to achieve obviously better source identification accuracies than the previous studies.

The question addressed in this paper is the performance of the optimal strategy, and the impact of partial information. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein–Uhlenbeck process. We focus on the optimal strategy with a logarithmic utility function under full or partial information. For both cases, we provide the asymptotic expectation and variance of the logarithmic return as functions of the signal-to-noise ratio and of the trend mean reversion speed. Finally, we compare the asymptotic Sharpe ratios of these strategies in order to quantify the loss of performance due to partial information.

Structural damage detection and vibration control have been actively studied in the past decades. However, in most previous investigations, these two aspects have been treated separately according to their individual primary objectives pursued. Although structural identification and model updating have been used in the adaptive control techniques, it is still necessary but challenging to study the on-line integration of structural damage detection and optimal vibration control. In this paper, a technique is proposed for such purpose. First, on-line structural parameter identification and on-line detection of the onset, locations and extents of structural damage of controlled structures with partial measurements of structural acceleration responses is studied. An algorithm is proposed based on the extended Kalman estimator/Kalman estimator and error tracking. Then, the updated structural models are on-line integrated with the instantaneous optimal control scheme to reach the goal of optimal active structural control of the undamaged/damaged structures. Numerical simulation results with different structural damage patterns illustrate the performances of the proposed technique for the on-line integration of structural damage detection and active optimal vibration control.

In most financial markets, prices for long-maturity derivatives are not readily available due to illiquidity. This reality is particularly common in bond markets, as it is very challenging to model prices consistently—for medium-to-long-term bonds under a single specification of the underlying interest rate process. We develop a bond market state-space model that incorporates uncertainty in the underlying interest rate process parameters. Our state-space representation, coupled with the complementary Kalman filtering, provides a modeling configuration that permits for liquidity risk management and pricing that is designed in a consistent fashion for both medium- and long-term bonds. As an example, we constructed a state-space bond market modeling system formulated on the two-factor Vasicek interest rate model. Wherein, the interest rate model is subject to noise for medium-to-long-term bond maturities and follows an unobservable process. We demonstrate our Kalman filter algorithm using the observed United States (US) 10 year bond yield data.

We present some recent developments on optimal quantization methods for numerically feasible solutions to discrete-time optimization problems under partial information. The main problem in effective implementation is in growing dimension of the the approximating filters. We overcome this difficulty by performing a quantization of the pair process filter-observation. Dynamic programming is then applied to solve the approximated optimization problem. Several numerical applications in finance are presented for the pricing of American option or for hedging problems in the context of partially observed stochastic volatility models.