Exact simulation pricing with Gamma processes and their extensions

Exact path simulation of the underlying state variable is of great practical importance in simulating prices of financial derivatives or their sensitivities when there are no analytical solutions for their pricing formulas. However, in general, the complex dependence structure inherent in most nontrivial stochastic volatility (SV) models makes exact simulation difficult. In this paper, we present a nontrivial SV model that parallels the notable Heston SV model in the sense of admitting exact path simulation as studied by Broadie and Kaya. The instantaneous volatility process of the proposed model is driven by a Gamma process. Extensions to the model including superposition of independent instantaneous volatility processes are studied. Numerical results show that the proposed model outperforms the Heston model and two other L\'evy driven SV models in terms of model fit to the real option data. The ability to exactly simulate some of the path-dependent derivative prices is emphasized. Moreover, this is the first instance where an infinite-activity volatility process can be applied exactly in such pricing contexts.Comment: Forthcoming The Journal of Computational Financ

Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on Bitcoin

In the Bitcoin system, participants are rewarded for solving cryptographic puzzles. In order to receive more consistent rewards over time, some participants organize mining pools and split the rewards from the pool in proportion to each participant's contribution. However, several attacks threaten the ability to participate in pools. The block withholding (BWH) attack makes the pool reward system unfair by letting malicious participants receive unearned wages while only pretending to contribute work. When two pools launch BWH attacks against each other, they encounter the miner's dilemma: in a Nash equilibrium, the revenue of both pools is diminished. In another attack called selfish mining, an attacker can unfairly earn extra rewards by deliberately generating forks. In this paper, we propose a novel attack called a fork after withholding (FAW) attack. FAW is not just another attack. The reward for an FAW attacker is always equal to or greater than that for a BWH attacker, and it is usable up to four times more often per pool than in BWH attack. When considering multiple pools - the current state of the Bitcoin network - the extra reward for an FAW attack is about 56% more than that for a BWH attack. Furthermore, when two pools execute FAW attacks on each other, the miner's dilemma may not hold: under certain circumstances, the larger pool can consistently win. More importantly, an FAW attack, while using intentional forks, does not suffer from practicality issues, unlike selfish mining. We also discuss partial countermeasures against the FAW attack, but finding a cheap and efficient countermeasure remains an open problem. As a result, we expect to see FAW attacks among mining pools.Comment: This paper is an extended version of a paper accepted to ACM CCS 201

Pre-Evaluating Efficiency Analysis of Mergers and Acquisitions of Full-Service Carriers in Korea

In November 2020, Korean Air signs an agreement to acquire and merges with 63.88% of Asiana Airlines’ shares, which is conditionally approved by the Korea Fair Trade Commission to address exclusivity concerns. The conditions require both airlines to return certain take-off or landing positions and revise their licenses for 26 international and 8 domestic routes within 10 years. This paper collects passenger traffic data from 2009 to 2019 using Korean data analysis, retrieval, and transfer systems employed by both airlines. Data envelopment analysis is utilized to assess their performance assuming the merger and acquisition. The analysis reveals that Korean Air’s super-efficiency performance in 2011 is the highest among all decision making units (DMUs). The best super-efficiency performance is achieved not only by individual companies but also by the combined enterprise in 2019

Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning

Deploying multiple robots for target search and tracking has many practical applications, yet the challenge of planning over unknown or partially known targets remains difficult to address. With recent advances in deep learning, intelligent control techniques such as reinforcement learning have enabled agents to learn autonomously from environment interactions with little to no prior knowledge. Such methods can address the exploration-exploitation tradeoff of planning over unknown targets in a data-driven manner, eliminating the reliance on heuristics typical of traditional approaches and streamlining the decision-making pipeline with end-to-end training. In this paper, we propose a multi-agent reinforcement learning technique with target map building based on distributed Gaussian process. We leverage the distributed Gaussian process to encode belief over the target locations and efficiently plan over unknown targets. We evaluate the performance and transferability of the trained policy in simulation and demonstrate the method on a swarm of micro unmanned aerial vehicles with hardware experiments.Comment: 10 pages, 6 figures; preprint submitted to IJCAS; first two authors contributed equall

Approaches to Understanding the Function of Intrinsic Activity and its Relationship to Task-evoked Activity in the Human Brain

Traditionally neuroscience research has focused on characterizing the topography and patterns of brain activation evoked by specific cognitive or behavioral tasks to understand human brain functions. This activation-based paradigm treated underlying spontaneous brain activity, a.k.a. intrinsic activity, as noise hence irrelevant to cognitive or behavioral functions. This view, however, has been profoundly modified by the discovery that intrinsic activity is not random, but temporally correlated at rest in widely distributed spatiotemporal patterns, so called resting state networks (RSN). Studies of temporal correlation of spontaneous activity among brain regions, or functional connectivity (FC), have yielded important insights into the network organization of the human brain. However, the underlying fundamental relationship between intrinsic and task-evoked brain activity has remained unclear, becoming an increasingly important topic in neuroscience. An emerging view is that neural activity evoked by a task and the associated behavior is influenced and constrained by intrinsic activity. Additionally, intrinsic activity may be shaped in the course of development or adult life by neural activity evoked by a task through a Hebbian learning process. This thesis aims to reveal correspondences between intrinsic activity and task-evoked activity to better understand the nature and function of intrinsic brain activity. We measured in human visual cortex the blood oxygen level dependent (BOLD) signal with fMRI to analyze the multivoxel activity patterns and FC structures of intrinsic activity, and compare them to those evoked by natural and synthetic visual stimuli. In chapter 1, we review previous evidence of an association between intrinsic and task-evoked activity across studies using different experimental methods. Two experimental strategies from the literature were adapted to our own experiments. First, from anesthetized animal studies of intrinsic activity in visual cortex, we set out to measure macro-scale multi-voxel patterns of spontaneous activity fluctuations as they relate to visually driven patterns of activity (Chapters 2 and 4). Second, from inter-subject correlation studies of visual activity driven by natural stimuli, we measure relationships between intrinsic and evoked activity, specifically in relation to their topographic similarity at the network level (Chapter 5). In Chapter 2 to 4, we establish a multivariate-pattern analysis (MVPA) approach to evaluate patterns of intrinsic and task-evoked activity. The main idea is that patterns of activity induced by behaviorally relevant stimuli over long periods of time would be represented in spontaneous activity patterns within the same areas. To test the idea, in Chapter 2, we compare the overall degree of pattern similarities between resting-state activity patterns, frame-by-frame (framewise), and visual-stimulus evoked activity patterns for natural (face, body, scenes, man-made objects) and synthetic (phase and position scrambled) object images during low-level detection task. We found that the variability, not the mean, of pattern similarity was significantly higher for natural than synthetic stimuli in visual occipital regions that preferred particular stimulus categories. Chapter 3 extends the static categorical pattern similarity measure of Chapter 2 into a temporal correlation measure. We built pattern-based FC matrices for different stimulus categories (e.g. a face specific multivoxel pattern) in regions that preferred particular stimulus categories (e.g. FFA, STG), and showed that the occurrence of a specific categorical pattern generalizes across category specific regions. These pattern-based FCs resemble that of resting-state FC of the same regions supporting that resting state patterns are related to category-specific stimulus-evoked multivoxel activity patterns. In Chapter 4, we repeat the analysis used in Chapter 2 with language stimuli. Language stimuli (alphabetic letters and English words) are interesting as they are learned through intensive training as kids learn to read. Therefore, they represent a non-natural category of stimuli that is, however, highly trained in literate individuals. The visual stimuli used in Chapter 2 to 4 are designed specifically for a laboratory environment that does not correspond to realistic ecological environments. In Chapter 5, to overcome this limitation, we use the more naturalistic visual experience of movie-watching and compare the whole-brain FC network structure of movie-watching and of resting-state. We show the whole-brain FC structure evoked by movie-watching is partly constrained by the resting network structure. In conclusion, our experiments show that the link between intrinsic activity and task-evoked activity is not only limited to inter-regional interactions (as in regular resting-state FC), hence potentially reflecting anatomical connectivity or modulations of excitability between cortical regions, but extends to multivoxel patterns that carry information about specific stimulus categories. This result supports the notion that intrinsic activity constrains task-evoked, not only in terms of topography or activation levels, but also in terms of the information states that are represented in cortex