Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach

This paper proposes a new volatility-spillover-asymmetric conditional autoregressive range (VS-ACARR) approach that takes into account the intraday information, the volatility spillover from crude oil as well as the volatility asymmetry (leverage effect) to model/forecast Bitcoin volatility (price range). An empirical application to Bitcoin and crude oil (WTI) price ranges shows the existence of strong volatility spillover from crude oil to the Bitcoin market and a weak leverage effect in the Bitcoin market. The VS-ACARR model yields higher forecasting accuracy than the GARCH, CARR, and VS-CARR models regarding out-of-sample forecast performance, suggesting that accounting for the volatility spillover and asymmetry can significantly improve the forecasting accuracy of Bitcoin volatility. The superior forecast performance of the VS-ACARR model is robust to alternative out-of-sample forecast windows. Our findings highlight the importance of accommodating intraday information, spillover from crude oil, and volatility asymmetry in forecasting Bitcoin volatility.     

一种提高无杂散动态范围的多通道信号接收方法

当大功率信号进入接收机后，将迫使模数转换器工作在非线性区，并导致接收信号中含有大量非线性杂散。为了抑制杂散并提高接收机对小信号的侦测能力,提出了一种具有无杂散高动态范围(Spurious-free Dynamic Range,SFDR)的信号接收方法。首先通过两路不同的通道同时接收信号，然后利用信号之间的频率差异识别杂散，最后在频域上通过频点替换方案抑制杂散。仿真与实验结果表明，当输入为双30 kHz窄带大信号时，该方案能使SFDR提升15 dB。     

阵列处理器中改进几乎空白子帧算法的并行化实现

两层异构网络中出现的小区间干扰协调是当前异构网络干扰问题研究的热点。针对软件实现增强小区调度几乎空白子帧(ABS)的干扰方案存在处理数据量大、速度慢的缺点，基于可重构阵列结构提出了一种动态ABS干扰协调的并行化硬件实现方案。该算法在增强小区内根据用户数动态分配ABS比例，以改善小区边缘用户的信道容量，通过流水线的方式使用多个轻核处理元(PE)实现并行计算以提高运算效率。测试结果表明，使用12个PE同时调度实现算法的映射，运行1 983个周期，其性能比单个PE提升77.03%，该算法的并行计算能力得到显著提升，宏基站用户的吞吐量可以达到4.76 Mbit/s。