Portfolio underdiversification is one of the most costly losses accumulated over a household’s life cycle. We provide new evidence on the impact of financial inclusion services on households’ portfolio choice and investment efficiency using 2015, 2017, and 2019 survey data for Chinese households. We hypothesize that higher financial inclusion penetration encourages households to participate in the financial market, leading to better portfolio diversification and investment efficiency. The results of the baseline model are consistent with our proposed hypothesis that higher accessibility to financial inclusion encourages households to invest in risky assets and increases investment efficiency. We further estimate a dynamic double machine learning model to quantitatively investigate the non-linear causal effects and track the dynamic change of those effects over time. We observe that the marginal effect increases over time, and those effects are more pronounced among low-asset, less-educated households and those located in non-rural areas, except for investment efficiency for high-asset households.

Individual institutions marginal contributions to the systemic risk contain predictive power for its potential future exposure and provide early warning signals to regulators and the public. We use higher-order co-skewness and co-kurtosis to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.

China commodity futures market network is constructed. Commodity is the node of the network, and the network link is defined by the price correlation matrix. We analyze the relationship between the centrality of each commodity in the commodity futures market network and the optimal weight of the commodity portfolio, empirically examine the market system factors and commodity personalized factors that affect the centrality of commodity, and evaluate the effect of network structure on the optimization of commodity portfolio selection under the mean-variance framework. It is found that the commodities with high network centrality are often related to industrial products and have high volatility. Commodities with higher centrality have lower portfolio weights. We put forward a kind of commodity futures investment strategy based on network, according to the network centricity grouping the commodities, the network centricity lower edge of the commodity structure of the portfolio, cumulative yield is better than that of centricity higher core product portfolio, the whole market portfolio yield, but due to large maximum retracement, lead to the stability and ability to resist risk compared with the other two groups of goods combination. The main contribution of this paper is to optimize portfolio selection by establishing the relationship between portfolio weight and commodity centrality by using commodity futures market network as a tool.

This paper analyzes international portfolio selection with exchange rate risk based on behavioural portfolio theory (BPT). We characterize the conditions under which the BPT problem with a single foreign market has an optimal solution, and show that the optimal portfolio contains the traditional mean–variance efficient portfolio without consideration of exchange rate risk, and an uncorrelated component constructed to hedge against exchange rate risk. We illustrate that the optimal portfolio must be mean–variance efficient with exchange rate risk, while the same is not true from the perspective of local investors unless certain conditions are satisfied. We further establish that international portfolio selection in the BPT with multiple foreign markets consists of two sequential decisions. Investors first select the optimal BPT portfolio in each market, overlooking covariances among markets, and then allocate funds across markets according to a specific rule to achieve mean–variance efficiency or to minimize the loss in efficiency.

This paper investigates the impact of background risk on an investor’s portfolio choice in a mean–variance framework, and analyzes the properties of efficient portfolios as well as the investor’s hedging behavior in the presence of background risk. Our model implies that the efficient portfolio with background risk can be separated into two independent components: the traditional mean–variance efficient portfolio and a self-financing component constructed to hedge against background risk. Our analysis also shows that the presence of background risk shifts the efficient frontier of financial assets to the right with no changes in its shape. Moreover, both the composition of the hedge portfolio and the location of the efficient frontier are greatly affected by a number of background risk factors, including the proportion of background assets in total wealth and the correlation between background risk and financial risk.

Efficient portfolio is a portfolio that yields maximum expected return given a level of risk or has minimum level of risk given a level of expected return.However,the optimal portfolios seem not being as efficient as intended.Especially during financial crisis period.optimal portfolio is not an optimal investment as it does not yield maximum return given a specific level of risk,vice and versa.One possible explanion for an unimpressive performance of the seemingly efficient portfolio is incorrectness in parameter estimates called"estimation risk in parameter estimates".Five different estimating strategies are employed to explore ex post portfolio performance when estimation risk is incorporated.These strategies are traditional mean-variance(EV),Adjusted Beta(AB) approach,Capital Asset Pricing Model(CAPM),Single Index Model(SIM), and Single Index Model incorporating shrikage Bayesian factor namely Bayesian Single Index Model(BSIM).Among the five alternative strategies,shrinkage estimators incorporating the single index model outperforms other traditional portfolio selection strategies.Allowing for asset mispricing and applying Bayesian shrinkage adjusted factor to each asset's alpha,a single factor namely excess market return is adequate in alleviating estimation uncertainty. JEL：G320

This paper investigates the impacts of background risk on investors’ portfolio choice in a mean-variance framework and analyzes the properties of the selected portfolio and investors’ hedging behaviour. Our model implies that the optimal portfolio with background risk can be separated into two independent components: the traditional mean-variance optimal portfolio and the self-financing portfolio constructed to hedge against background risk. Our results show that both the composition and risk of the optimal portfolio are greatly affected by a number of background risk factors, including the quantity and risk of the assets that are exposed to background risk, as well as the correlation between background assets and those in the portfolio.

This paper studies sequential portfolio choices by MPS-risk-averse investors in a continuous time jump-diffusion framework. It is shown that the optimal trading strategies for MPS risk averse investors, if they exist, must be located on a so-called `temporal efficient frontier' (t.e.f.). The t.e.f. is found not to coincide with the local instantaneous frontier --- the continuous time analogue of Markowitz's mean-variance frontier. This observation is potentially useful in understanding the existence of documented financial anormally in empirical finance --- MPS risk averse investors may not wish to invest along the local instantaneous Markowitz's mean-variance frontier, but instead hold portfolios on the t.e.f.. The optimal portfolio on the t.e.f. could well fall strictly within the instantaneous local Markowitz's efficient frontier. Our observations on mutual fund separation are also profound and interesting. In contrast to the classical two-fund separation along the line of Black (1972) and Tobin (1958), our study shows that MPS-risk-averse investors' optimal trading strategy is target rate specific. Precisely, investors with different target rates may end up investing into different managed mutual funds, each involving a specific set of separating portfolios. Our theoretic findings are, nevertheless, much in line with the real world phenomena on the existence of various types of mutual funds offered by different financial institutes, each aiming to attract demand from some specific groups of investors --- a picture that is in sharp contrast to the theoretical prediction made by Black (1972) and Tobin (1958). Finally, our study sheds light on the difference between expected utility and MPS-risk-averse investors concerning their trading behavior in sequential time frame. Even though these two groups of investors may end up holding a common risky portfolio in each spot market, the differences between their trading behaviors are most reflected through the portfolio weights assigned to each of the separating portfolios within the time frame and across states. Precisely, the portfolio weights corresponding to investors respectively from the two groups are associated with recognizable different time patterns. We showed that such difference in trading behavior would be also reflected from the time patterns of the instantaneous returns and the volatilities of the funds respectively managed by investors from these two groups.

Abstract: It is negative risk if there is a good chance of coming out better than our reference level. This paper proposes a general risk measure: bilateral partial moment, where downside risk is supplemented with the "upside potential". Variance, mean absolute deviation, semi-variance and other downside risk definition are all incorporated in this framework. The portfolio selection problems in this general class of risk model are discussed. The portfolio optimization provides the flexibility for the selection of an appropriate target return and the weightiness of upside potentials.