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on Financial Markets |
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Issue of 2022‒02‒07
four papers chosen by |
| By: | Habis, Helga; Perge, Laura |
| Abstract: | In this paper, we show that the capital asset pricing model can be derived from a three-period general equilibrium model. We show that our extended model yields a Pareto efficient outcome. This result indicates that the beta pricing formula could be applied in a long term model settings as well. |
| Keywords: | general equilibrium, CAPM, intertemporal choice, Pareto efficiency |
| JEL: | D15 D53 G12 |
| Date: | 2022–01–18 |
| URL: | http://d.repec.org/n?u=RePEc:cvh:coecwp:2022/01&r= |
| By: | Sarantis Tsiaplias (Melbourne Institute: Applied Economic & Social Research, the University of Melbourne); Qi Zeng (Department of Finance, the University of Melbourne); Guay Lim (Melbourne Institute: Applied Economic & Social Research, the University of Melbourne) |
| Abstract: | Using a novel quarterly survey of 23 thousand Australian retail equity investors spanning eight years, we study the relationship between investor beliefs and their trading preferences. We provide evidence that, consistent with Mean-Variance preferences, both lower returns and higher volatility increase the marginal propensity to sell. Furthermore, we find that while demographic characteristics and investment experiences are predictive of the holding preferences of retail investors, they are uninformative about their trading directional preferences (i.e. whether to buy or sell). Our findings suggest that a representative-agent portfolio model with Mean-Variance preferences is sufficient to explain the trading directional preferences of retail investors, but not their holding patterns. |
| Keywords: | investor expectiations, shareholder surveys, trading preferences, Mean-Variance utility. |
| JEL: | G11 G40 C35 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:iae:iaewps:wp2021n27&r= |
| By: | Yuanrong Wang; Tomaso Aste |
| Abstract: | Market conditions change continuously. However, in portfolio's investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to identify inherent market states and then integrate such states into a dynamic portfolio optimization process. Extensive experiments across three different markets, NASDAQ, FTSE and HS300, over a period of ten years, demonstrate the advantages of our proposed algorithm, termed Inverse Covariance Clustering-Portfolio Optimization (ICC-PO). The core of the ICC-PO methodology concerns the identification and clustering of market states from the analytics of past data and the forecasting of the future market state. It is therefore agnostic to the specific portfolio optimization method of choice. By applying the same portfolio optimization technique on a ICC temporal cluster, instead of the whole train period, we show that one can generate portfolios with substantially higher Sharpe Ratios, which are statistically more robust and resilient with great reductions in maximum loss in extreme situations. This is shown to be consistent across markets, periods, optimization methods and selection of portfolio assets. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.15499&r= |
| By: | Mawuli Segnon (Department of Economics, Institute for Econometric and Economic Statistics and Chair of Empirical Economics, University of Munster, Germany); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | We investigate the role of geopolitical risks (GPR) in forecasting stock market volatility in a robust autoregressive Markov-switching GARCH mixed data sampling (ARMSGARCH-MIDAS) framework that accounts for structural breaks through regime switching and allows us to disentangle short- and long-run volatility components driven by geopolitical risks. An empirical out-of-sample forecasting exercise is conducted using unique data sets on Dow Jones Industrial Average (DJIA) index and geopolitical risks that cover the time period from January 3, 1899 to December 31, 2020. We find that geopolitical risks as explanatory variables can help to improve the accuracy of stock market volatility forecasts. Furthermore, our empirical results show that the macroeconomic variables such as output measured by recessions, inflation and interest rates contain information that is complementary to the one included in the geopolitical risks. |
| Keywords: | Geopolitical risks, Volatility forecasts, Markov-switching GARCH-MIDAS |
| JEL: | C52 C53 C58 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202203&r= |