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on Financial Markets |
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Issue of 2021‒05‒03
five papers chosen by |
| By: | Maya Jalloul; Mirela Miescu |
| Abstract: | We use a threshold VAR model to capture connectedness of the equity returns of the G7 in a regime-contingent manner as dened by low- and high-geopolitical risks (GPR).We nd that connectedness is statistically stronger when GPR is at its higher rather than lower regime, but more importantly, this observation can be associated with threats of geopolitical adverse events, rather than with their actual realization. To explain our empirical observations we employ a model of international trade in assets and international relative asset prices. We introduce uncertainty in the future dividend payments combined with ambiguity aversion of agents to changes in the expected dividends. This allows us to model a geopolitical threat as a shock that affects the level of ambiguity about future dividends. At the same time, a geopolitical act is defined as a shock to the current period endowment of a given country, with limited effects on asset prices and returns. Our obtained results have important portfolio allocation implications for investors. |
| Keywords: | Geopolitical Risk, Equity Market Connectedness, Threshold VAR, Asset Trade, Multi-Country Macroeconomic Model |
| JEL: | C32 F12 F40 G12 G15 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:lan:wpaper:324219805&r= |
| By: | Ren, Rui; Lu, Meng-Jou; Li, Yingxing; Härdle, Wolfgang |
| Abstract: | The Financial Risk Meter (FRM) is an established mechanism that, based on conditional Value at Risk (VaR) ideas, yields insight into the dynamics of network risk. Originally, the FRM has been composed via Lasso based quantile regression, but we here extend it by incorporating the idea of expectiles, thus indicating not only the tail probability but rather the actual tail loss given a stress situation in the network. The expectile variant of the FRM enjoys several advantages: Firstly, the coherent and multivariate tail risk indicator conditional expectile-based VaR (CoEVaR) can be derived, which is sensitive to the magnitude of extreme losses. Next, FRM index is not restricted to an index compared to the quantile based FRM mechanisms, but can be expanded to a set of systemic tail risk indicators, which provide investors with numerous tools in terms of diverse risk preferences. The power of FRM also lies in displaying FRM distribution across various entities every day. Two distinct patterns can be discovered under high stress and during stable periods from the empirical results in the United States stock market. Furthermore, the framework is able to identify individual risk characteristics and capture spillover effects in a network. |
| Keywords: | expectiles,EVaR,CoEVaR,expectile lasso regression,network analysis,systemicrisk,Financial Risk Meter |
| JEL: | C00 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021008&r= |
| By: | Jonathan Huntley (Penn Wharton Budget Model, The Wharton School, University of Pennsylvania); Valentina Michelangeli (Bank of Italy); Felix Reichling (Penn Wharton Budget Model, The Wharton School, University of Pennsylvania) |
| Abstract: | We use data on one-participant retirement savings plans to identify a behavioral bias in savings decisions. Investors who earn top-decile returns increase contributions to their accounts more than other investors. Using characteristics of the investors, characteristics of their retirement savings accounts, and multivariate regression analysis, we first show that such ``return chasing'' behavior is robust to controls for financial illiteracy, macroeconomic conditions, learning, transaction costs, housing prices, and informational frictions. We then use a structural two-asset model with tax-deferred and taxable assets to show that a permanent increase in expected returns produces investment responses for younger or liquidity-constrained investors that are consistent with our data. Our results provide evidence that younger investors' recent portfolio experiences have highly persistent effects on their expectations. |
| Keywords: | household finance, retirement saving, life-cycle |
| JEL: | D14 G4 |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_1334_21&r= |
| By: | D’Hondt, Catherine (Université catholique de Louvain, LIDAM/LFIN, Belgium); De Winne, Rudy (Université catholique de Louvain, LIDAM/LFIN, Belgium); Merli, Maxime |
| Abstract: | Using information self-reported by retail investors in a risk-return profiling survey, we investigate the determinants of individual return objectives as well as the capacity of investors to reach them. Controlling for a large set of covariates, we provide empirical evidence that return objectives are related to subjective individual characteristics (such as financial literacy and risk tolerance), some sociodemographics (age, education), as well as recent past trading intensity. Retail investors with higher return objectives perform better, compared to their counterparts who want to avoid any risk of capital loss. The capacity to reach the return objective however decreases as the level of return objectives increases. |
| Keywords: | Return objectives, Risk tolerance, Financial literacy, Retail investors, MiFID |
| JEL: | G11 G40 |
| Date: | 2021–03–08 |
| URL: | http://d.repec.org/n?u=RePEc:ajf:louvlf:2021003&r= |
| By: | Christopher G. Lamoureux; Huacheng Zhang |
| Abstract: | We analyze characteristics' joint predictive information through the lens of out-of-sample power utility functions. Linking weights to characteristics to form optimal portfolios suffers from estimation error which we mitigate by maximizing an in-sample loss function that is more concave than the utility function. While no single characteristic can be used to enhance utility by all investors, conditioning on momentum, size, and residual volatility produces portfolios with significantly higher certainty equivalents than benchmarks for all investors. Characteristic complementarities produce the benefits, for example momentum mitigates overfitting inherent in other characteristics. Optimal portfolios' returns lie largely outside the span of traditional factors. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.12975&r= |