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on Financial Markets |
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Issue of 2021‒12‒20
nine papers chosen by |
| 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 over 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 – a measure of the risk-bearing capacity of U.S. financial intermediaries. The spillover effects of global financial risk are more pronounced for speculative-grade sovereign bonds. |
| JEL: | E43 E44 F33 G12 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29501&r= |
| By: | Philippe Bacchetta (University of Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Margaret Davenport (University of Lausanne); Eric van Wincoop (University of Virginia - Department of Economics; National Bureau of Economic Research (NBER)) |
| Abstract: | Recently portfolio choice has become an important element of many DSGE open economy models. Yet, a substantial body of evidence is inconsistent with standard frictionless portfolio choice models. In this paper we introduce a quadratic cost of changes in portfolio allocation into a two-country DSGE model. We investigate the level of portfolio frictions most consistent with the data and the impact of portfolio frictions on asset prices and net capital flows. We find the portfolio friction accounts for (i) micro evidence of portfolio inertia by households, (ii) macro evidence of the price impact of financial shocks and related disconnect of asset prices from fundamentals, (iii) a broad set of moments related to the time series behavior of saving, investment and net capital flows, and (iv) other phenomena relating to excess return dynamics. Financial and saving shocks each account for close to half of the variance of net capital flows. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2180&r= |
| By: | Sebastiano Michele Zema; Giorgio Fagiolo; Tiziano Squartini; Diego Garlaschelli |
| Abstract: | The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic 'equally-weighted' portfolio. In this paper, we exploit clustering techniques derived from Random Matrix Theory (RMT) to study a third, intermediate (mesoscopic) market structure that turns out to be the most stable over time and provides important practical insights from a portfolio management perspective. First, we illustrate the benefits, in terms of predicted and realized risk profiles, of constructing portfolios by filtering out both random and systemic comovements from the correlation matrix. Second, we redefine the portfolio optimization problem in terms of stock clusters that emerge after filtering. Finally, we propose a new wealth allocation scheme that attaches equal importance to stocks belonging to the same community and show that it further increases the reliability of the constructed portfolios. Results are robust across different time spans, cross-sectional dimensions and set of constraints defining the optimization problem. |
| Keywords: | Random matrix theory; Community detection; Mesoscopic structures; Portfolio optimization. |
| Date: | 2021–12–07 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2021/45&r= |
| By: | Bin Xie; Weiping Li; Nan Liang |
| Abstract: | We analyze various jumps for Heston model, non-IID model and three L\'evy jump models for S&P 500 index options. The L\'evy jump for the S&P 500 index options is inevitable from empirical studies. We estimate parameters from in-sample pricing through SSE for the BS, SV, SVJ, non-IID and L\'evy (GH, NIG, CGMY) models by the method of Bakshi et al. (1997), and utilize them for out-of-sample pricing and compare these models. The sensitivities of the call option pricing for the L\'evy models with respect to parameters are presented. Empirically, we show that the NIG model, SV and SVJ models with estimated volatilities outperform other models for both in-sample and out-of-sample periods. Using the in-sample optimized parameters, we find that the NIG model has the least SSE and outperforms the rest models on one-day prediction. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2111.10033&r= |
| By: | Yajie Yang; Longfeng Zhao; Lin Chen; Chao Wang; Jihui Han |
| Abstract: | In this study, we propose a new multi-objective portfolio optimization with idiosyncratic and systemic risks for financial networks. The two risks are measured by the idiosyncratic variance and the network clustering coefficient derived from the asset correlation networks, respectively. We construct three types of financial networks in which nodes indicate assets and edges are based on three correlation measures. Starting from the multi-objective model, we formulate and solve the asset allocation problem. We find that the optimal portfolios obtained through the multi-objective with networked approach have a significant over-performance in terms of return measures in an out-of-sample framework. This is further supported by the less drawdown during the periods of the stock market fluctuating downward. According to analyzing different datasets, we also show that improvements made to portfolio strategies are robust. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2111.11286&r= |
| By: | Daniele Bianchi; Mykola Babiak |
| Abstract: | We investigate the dynamics of daily realised returns and risk premiums for a large cross-section of cryptocurrency pairs through the lens of an Instrumented Principal Component Analysis (IPCA) (see Kelly et al., 2019). We show that a model with three latent factors and time-varying factor loadings significantly outperforms a benchmark model with observable risk factors: the total (predictive) R2 from the IPCA is 17.2% (2.9%) for individual returns, against a benchmark 9.6% (-0.02%) obtained from a model with six observable risk factors explored in previous literature. By looking at the characteristics that significantly matter for the dynamics of risk premiums, we provide robust evidence that liquidity, size, reversal, and both market and downside risks represent the main driving factors behind expected returns. These results hold for both individual assets and characteristic-based portfolios, pre and post the Covid-19 outbreak, and for weekly individual and portfolio returns. |
| Keywords: | cryptocurrency markets; instrumented PCA; asset pricing; factor models; risk premiums; |
| JEL: | G11 G12 G17 C23 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:cer:papers:wp710&r= |
| By: | Guoxi Duan (Graduate School of Economics, Osaka University); Hisashi Tanizaki (Graduate School of Economics, Osaka University) |
| Abstract: | This paper examine the weak form market efficiency in five stock markets, China (CSI 300 index), Hong Kong (HSI), Japan (Nikkei 225), the US (NASDAQCOM) and Germany (DAX) from the perspective of random walk hypothesis. The methods for testing random walk are autocorrelation, runs test, strategy. This paper also examine three calendar effects in all stock market: January effect, the-turn-of-the-month effect (the TOM effect), the day-of-the-week-effect (the DOW effect). The results are: (1)From the viewpoint of autocorrelation, the most efficient market among five stock markets is the market in Hong Kong while strong evidence of autocorrelation is found in China, CSI 300 and The US, NASDAQCOM. (2) The results of runs tests do not found evidence against randomness in daily returns for CSI 300, HSI from 2006 to 2020 but find a little evidence for Nikkei 225, NASDAQCOM, DAX. The higher level of efficient markets among five stock markets are the markets in China and Hong Kong. (3) The strategy analyzed in this paper does not find evidence indicating inefficient market for five indexes. (4) January effect did not exist in five indexes. (5) All five indexes are characterized with a TOM effect in different level and therefore the hypothesis of an efficient market is rejected for five markets. (6) All five indexed are found the-day-of-week effect which also indicates inefficient stock markets. we conclude that all five market are not efficient from 2006 to 2020. |
| Keywords: | market efficiency hypothesis, random walk, January effect, the-turn-of-the-month effect, the day-of-the-week-effect |
| JEL: | G10 G14 C22 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:osk:wpaper:2124&r= |
| By: | Guoxi Duan (Graduate School of Economics, Osaka University); Hisashi Tanizaki (Graduate School of Economics, Osaka University) |
| Abstract: | This paper studies market efficiency from weak form aspect using data of Shanghai Stock Exchange composite index (SSEC) and Shenzhen Stock Exchange composite index (SZSEC) under expected return theory. Some classical methods are used to examine the features of stock returns and a little evidence against mutually independency, random walk of returns, and sub-martingale of stock prices is found. A notion of a new simple statistical test based on information set for judgement of market efficiency is proposed. Through hypothesis tests, evidence indicating inefficient markets around 2008, 2011 and 2018 under expected return theory is found. It is a new finding that SZSEC is more sensitive to information and therefore may be more appealing to investors than SSEC. Moreover, there is an another new finding that when market extends in size the degree of whole market efficiency declines. From the relationship between market efficiency and volatility, volatility is not a very good criterion for market efficiency but some rough rules can be concluded to help investors make their decisions on what time to conduct their own strategies. Finally, the results suggest that it is the time to think about strategies. |
| Keywords: | stock market, market efficiency hypothesis, random walk, investment strategy, hypothesis test |
| JEL: | C12 G12 G14 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:osk:wpaper:2122&r= |
| By: | Guoxi Duan (Graduate School of Economics, Osaka University); Hisashi Tanizaki (Graduate School of Economics, Osaka University) |
| Abstract: | This paper analyzes CSI 300 index and its 300 constituent stocks with seven market efficiency measures: autocorrelation of daily returns, autocorrelation of absolute daily returns, runs test, forecast ability of other historical data on daily return (the predictive ability of yesterday fs change of trading volume on today fs return in this paper), the return of specific trading strategy, variance ratio and pricing errors contained in daily return. We do a Principal component analysis to convert these indicators to a single indicator representing the market efficiency. Then we try to find the co-movement among different measures through correlation coefficient and among different stocks through OLS regression: market efficiency values of individual stocks are regressed on market efficiency values of CSI 300 for seven measures respectively. We found that different market efficiency measures are indeed consistent to each other to some extent and the individual stocks are somewhat consistent with the whole market indicating there is a systematic market efficiency in stock market in China. Our finding also support the idea that the market efficiency in Chinese stock market is changing all time without showing a clear upward trend from 2005 to 2020. In the end, we set three hypotheses to explain relatively high level of market efficiency in 2005, 2012, 2017 and 2019: the ability of market detecting and reacting to pricing errors, public information or private information is becoming quickly and accurately. We found that when the market is in a bad condition, the market contains more pricing-errors in daily returns and the ability of market detecting and reacting to private information is also bad. |
| Keywords: | stock market, market efficiency hypothesis, random walk, investment strategy |
| JEL: | G10 G14 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:osk:wpaper:2123&r= |