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on Financial Markets |
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Issue of 2022‒01‒03
five papers chosen by |
| By: | Peter Reinhard Hansen; Zhuo Huang; Chen Tong; Tianyi Wang |
| Abstract: | We show that the Realized GARCH model yields close-form expression for both the Volatility Index (VIX) and the volatility risk premium (VRP). The Realized GARCH model is driven by two shocks, a return shock and a volatility shock, and these are natural state variables in the stochastic discount factor (SDF). The volatility shock endows the exponentially affine SDF with a compensation for volatility risk. This leads to dissimilar dynamic properties under the physical and risk-neutral measures that can explain time-variation in the VRP. In an empirical application with the S&P 500 returns, the VIX, and the VRP, we find that the Realized GARCH model significantly outperforms conventional GARCH models. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.05302&r= |
| By: | HORIKAWA Takumi (Bank of Japan); MATSUI Yujiro (Bank of Japan); GEMMA Yasufumi (Bank of Japan) |
| Abstract: | In this paper, we attempt to understand the characteristics of the Japanese government bond (JGB) repo market by applying network analysis methods to highly granular data on JGB repo transactions. We especially use a measure of "network centrality" which quantitatively identifies financial institutions that play an important role in the transaction network and a "community detection" method which identifies groups of financial institutions that have close transactional relationships with each other. From the results, it was observed that some highly important financial institutions functioned as intermediaries for transactions and that continuous transaction relationships within groups were built around them. These characteristics may contribute to the efficient matching of cash borrowing and lending needs, and to the smooth execution of large-lot transactions. We also conducted some analysis of the behavior of the network structure of the JGB repo market under market stress using the data from March 2020, when the repo rate fluctuated significantly due to the spread of the COVID-19 pandemic. The results of the analysis in this paper indicate the importance of continuously monitoring the functioning of the JGB repo market, and also provide clues for maintaining and improving the functioning and robustness of the market. |
| Keywords: | Network analysis; Financial markets; Repo transactions; PageRank; Bow-tie decomposition; Community detection |
| JEL: | D85 G14 G20 L14 |
| Date: | 2021–12–21 |
| URL: | http://d.repec.org/n?u=RePEc:boj:bojwps:wp21e14&r= |
| By: | Simon Gilchrist; Bin Wei; Vivian Z. Yue; Egon Zakrajšek |
| Abstract: | In this paper, we study the interplay between sovereign risk and global financial risk. We show that a substantial portion of the comovement among sovereign spreads is accounted for by changes in global financial risk. We construct bond-level sovereign spreads for dollar-denominated bonds issued by more than 50 countries from 1995 to 2020 and use various indicators to measure global financial risk. Through panel regressions and local projection analysis, we find that an increase in global financial risk causes a large and persistent widening of sovereign bond spreads. These effects are strongest when measuring global risk using the excess bond premium, which is a measure of the risk-bearing capacity of US financial intermediaries. The spillover effects of global financial risk are more pronounced for speculative-grade sovereign bonds. |
| Keywords: | sovereign bonds; CDS; global financial risk; excess bond premium; global financial cycle |
| JEL: | E43 E44 F33 G12 |
| Date: | 2021–11–24 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedawp:93483&r= |
| By: | Yizhuo Li; Peng Zhou; Fangyi Li; Xiao Yang |
| Abstract: | With the development of artificial intelligence technology, quantitative trading systems represented by reinforcement learning have emerged in the stock trading market. The authors combined the deep Q network in reinforcement learning with the sentiment quantitative indicator ARBR to build a high-frequency stock trading model for the share market. To improve the performance of the model, the PCA algorithm is used to reduce the dimensionality feature vector while incorporating the influence of market sentiment on the long-short power into the spatial state of the trading model and uses the LSTM layer to replace the fully connected layer to solve the traditional DQN model due to limited empirical data storage. Through the use of cumulative income, Sharpe ratio to evaluate the performance of the model and the use of double moving averages and other strategies for comparison. The results show that the improved model proposed by authors is far superior to the comparison model in terms of income, achieving a maximum annualized rate of return of 54.5%, which is proven to be able to increase reinforcement learning performance significantly in stock trading. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2111.15354&r= |
| By: | Martin Hillebrand (ESM); Marko Mravlak (ESM); Peter Schwendner (Zurich University of Applied Sciences) |
| Abstract: | This study analyses investor demand in syndicated EFSF and ESM bond issuances from 2014 to 2020 on an unprecedented granularity level of individual orders. In particular, we investigate three main aspects of order book dynamics: first, we determine the main factors segmenting investor demand. Second, we analyse price dynamics in the transactions and its relation to investor demand. Third, we examine whether there are any indications of order book inflation that might explain the increased volatility in order book volume. We identify issuance tranche and tenor as the main determinants of investor demand, which are to a large extent anticipated by the envisaged notional amount of the issuance. Further, we note that the pricing of ESM bond issuances is carried out in an economical manner, i.e. the new issue premium tends to be lower in a market context with large demand. Lastly, we look at the drivers of large order books and find a mixture of above average number and volume of orders. This confirms that there are no indications of order book inflation tendencies in the analysed time period. |
| Keywords: | Investor demand, bond issuance, bond syndication, bond primary market, investor behaviour, order books, order book inflation, new issue premium |
| JEL: | G12 G15 G23 G40 |
| Date: | 2021–12–22 |
| URL: | http://d.repec.org/n?u=RePEc:stm:wpaper:50&r= |