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on Financial Markets |
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Issue of 2023‒11‒20
six papers chosen by |
| By: | Sebastian Doerr; Jon Frost; Leonardo Gambacorta; Vatsala Shreeti |
| Abstract: | The entry of big tech companies into the financial services sector can bring significant benefits in terms of efficiency and financial inclusion. Yet big techs can also quickly dominate markets, engage in discriminatory behaviour, and harm data privacy. This leads to the emergence of new trade-offs between policy goals such as financial stability, competition and privacy. Regulators, both domestically and internationally, are actively working to address these trade-offs. This paper provides an overview over the state of the literature and the policy debate. |
| Keywords: | big techs, financial inclusion, competition, financial stability, data privacy |
| JEL: | E51 G23 O31 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1129&r=fmk |
| By: | Donggyu Kim; Minseog Oh |
| Abstract: | This paper introduces a dynamic minimum variance portfolio (MVP) model using nonlinear volatility dynamic models, based on high-frequency financial data. Specifically, we impose an autoregressive dynamic structure on MVP processes, which helps capture the MVP dynamics directly. To evaluate the dynamic MVP model, we estimate the inverse volatility matrix using the constrained $\ell_1$-minimization for inverse matrix estimation (CLIME) and calculate daily realized non-normalized MVP weights. Based on the realized non-normalized MVP weight estimator, we propose the dynamic MVP model, which we call the dynamic realized minimum variance portfolio (DR-MVP) model. To estimate a large number of parameters, we employ the least absolute shrinkage and selection operator (LASSO) and predict the future MVP and establish its asymptotic properties. Using high-frequency trading data, we apply the proposed method to MVP prediction. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.13511&r=fmk |
| By: | Albert S. (Pete); Karamfil Todorov |
| Abstract: | We develop a novel methodology to measure the risk premium of higher-order cumulants (closely related to the moments of a distribution) based on leveraged ETFs. We show that the risk premium on these ETFs reflects the difference between physical and risk-neutral cumulants, which we call the cumulant risk premium (CRP). We show that the CRP is different from zero across asset classes (equities, bonds, commodities, currencies, and volatility) and is large in times of stress. We illustrate that highly leveraged strategies are extremely exposed to higher-order cumulants. Our results have implications for hedge funds, factor models, momentum strategies, and options. |
| Keywords: | cumulants, leverage, ETF, factor models, VIX, momentum, options |
| JEL: | G1 G12 G13 G23 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1128&r=fmk |
| By: | António Afonso; José Alves; Sofia Monteiro |
| Abstract: | We analyze domestic, foreign, and central banks holdings of public debt for 31 countries for the period of 1989-2022, applying panel regressions and quantile analysis. We conclude that an increase in sovereign risk raises the share of domestic banks’ portfolio of public debt and reduces the percentage holdings in the case of central banks. Better sovereign ratings also increase (decrease) the share of commercial (central) banks’ holdings. Furthermore, the effects of an increment in the risk for domestic investors have increased since the 2010 financial crisis. |
| Keywords: | banking, sovereign debt, sovereign risk, financial crisis, ratings |
| JEL: | C21 E58 G24 G32 H63 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10692&r=fmk |
| By: | Jungjun Park; Andrew L. Nguyen |
| Abstract: | In this paper, we study the Black-Litterman (BL) asset allocation model (Black and Litterman, 1990) under the hidden truncation skew-normal distribution (Arnold and Beaver, 2000). In particular, when returns are assumed to follow this skew normal distribution, we show that the posterior returns, after incorporating views, are also skew normal. By using Simaan three moments risk model (Simaan, 1993), we could then obtain the optimal portfolio. Empirical data show that the optimal portfolio obtained this way has less risk compared to an optimal portfolio of the classical BL model and that they become more negatively skewed as the expected returns of portfolios increase, which suggests that the investors trade a negative skewness for a higher expected return. We also observe a negative relation between portfolio volatility and portfolio skewness. This observation suggests that investors may be making a trade-off, opting for lower volatility in exchange for higher skewness, or vice versa. This trade-off indicates that stocks with significant price declines tend to exhibit increased volatility. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.12333&r=fmk |
| By: | Jaydip Sen; Arup Dasgupta; Subhasis Dasgupta; Sayantani Roychoudhury |
| Abstract: | This chapter presents a calendar rebalancing approach to portfolios of stocks in the Indian stock market. Ten important sectors of the Indian economy are first selected. For each of these sectors, the top ten stocks are identified based on their free-float market capitalization values. Using the ten stocks in each sector, a sector-specific portfolio is designed. In this study, the historical stock prices are used from January 4, 2021, to September 20, 2023 (NSE Website). The portfolios are designed based on the training data from January 4, 2021 to June 30, 2022. The performances of the portfolios are tested over the period from July 1, 2022, to September 20, 2023. The calendar rebalancing approach presented in the chapter is based on a yearly rebalancing method. However, the method presented is perfectly flexible and can be adapted for weekly or monthly rebalancing. The rebalanced portfolios for the ten sectors are analyzed in detail for their performances. The performance results are not only indicative of the relative performances of the sectors over the training (i.e., in-sample) data and test (out-of-sample) data, but they also reflect the overall effectiveness of the proposed portfolio rebalancing approach. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.09770&r=fmk |