A Copula-GARCH Model for Macro Asset Allocation of a Portfolio with Commodities: an Out-of-Sample Analysis

Many authors have suggested that the mean-variance criterion, conceived by Markowitz (1952), is not optimal for asset allocation, because the investor expected utility function is better proxied by a function that uses higher moments and because returns are distributed in a non-Normal way, being asymmetric and/or leptokurtic, so the mean-variance criterion can not correctly proxy the expected utility with non-Normal returns. In Riccetti (2010) I apply a simple GARCH-copula model and I find that copulas are not useful for choosing among stock indices, but they can be useful in a macro asset allocation model, that is, for choosing the stock and the bond composition of portfolios. In this paper I apply that GARCH-copula model for the macro asset allocation of portfolios containing a commodity component. I find that the copula model appears useful and better than the mean-variance one for the macro asset allocation also in presence of a commodity index, even if it is not better than GARCH models on independent univariate series, probably because of the low correlation of the commodity index returns to the stock, the bond and the exchange rate returns

Asset management with TEV and VaR constraints: the constrained efficient frontiers

It is well known that investors usually assign part of their funds to asset managers who are given the task of beating a benchmark portfolio. On the other hand, the risk management office could impose some restrictions to the asset managers' activity in order to mantain the overall portfolio risk under control. This situation could lead managers to select non efficient portfolios in the total return and absolute risk perspective. In this paper we focus on portfolio efficiency when a tracking error volatility (TEV) constraint holds. First, we define the TEV Constrained-Efficient Frontier (ECTF), a set of TEV constrained portfolios that are mean-variance efficient. Second, we discuss the effects on such boundary when a VaR and/or a variance restriction is also added

Risk aversion, prudence and temperance. It is a matter of gap between moments

Higher order risk preferences are important determinants of choices under uncertainty. We build a questionnaire different from usually adopted ones: our questionnaire is simpler in order to reduce the number of random choices, and it includes questions with largely diversified stake sizes to observe different gaps between moments. Moreover, we collect results from a large and heterogeneous population to provide more general and unbiased results. Our results confirm the preference of the majority of the respondents for higher odd and lower even moments of the expected return distribution. However, we highlight three features: (i) the importance of the gap between the values of the corresponding moments of the two choices, (ii) the behavioral change in presence of a positive/zero/negative expected value, (iii) the huge heterogeneity in behaviors, also due to the complexity of the choice as an important driver of the propensity to switch from choosing on the basis of preferences to choosing randomly. We also find that age and geographical location are important determinants of risk propensity

Financial and non-financial risk attitudes: What does it matter?

In this paper we try to: (i) study the personal features of the subjects that can influence the risk attitude in financial and non-financial contexts, (ii) understand the correspondences among some behaviors in financial and non-financial choices. We start from the questionnaire used by Colasante and Riccetti (2020), that investigates how subjects take into account the first four moments of the return distribution in making risky decisions, and that collects data from a very large and heterogeneous population. We find that age and geographical location are important determinants of risk propensity in all domains. Moreover, we find that risk attitudes in financial and non-financial contexts are correlated, but correlation is far from 1, with a larger risk aversion in non-financial contexts. Therefore, there is a common underlying risk trait, but the context is also relevant. Interestingly, the financial risk propensity is positively correlated to the propensity to perform illegal activities

Firm-bank credit networks, business cycle and macroprudential policy

We present an agent-based model to study firm-bank credit market interactions in different phases of the business cycle. The business cycle is exogenously set and it can give rise to various scenarios. Compared to other models in this literature strand, we improve the mechanism according to which the dividends are distributed, including the possibility of stock repurchase by firms. In addition, we locate firms and banks over a space and firms may ask credit to many banks, resulting in a complex spatial network. The model reproduces a long list of stylized facts and their dynamic evolution as described by the cross-correlations among model variables. The model allows us to test the effectiveness of rules designed by the current financial regulation, such as the Basel 3 countercyclical capital buffer. We find that the effectiveness of this rule changes in different business cycle environments and this should be considered by policy makers

Increasing inequality, consumer credit and financial fragility in an agent based macroeconomic model

We investigate the interplay between increasing inequality and consumer credit in a complex macroeconomic system with financially fragile heterogeneous households, firms and banks. Simulation results show that there are pros and cons of introducing consumer credit: on the one hand, for a certain time, it leads to lower unemployment through boosting aggregate demand; on the other hand, it accelerates the system tendency to the crisis. Since the increase of financial profits goes with a decline of households\u2019 real wealth, a policy trade-off emerges

Leveraged network-based financial accelerator

In this paper we build on the network-based financial accelerator model of Delli Gatti et al.(2010), modelling the firms\u2019 financial structure following the \u2018\u2018dynamic trade-off theory\u2019\u2019, instead of the \u2018\u2018packingordertheory\u2019\u2019. Moreover,we allow for multi periodal debt structure and consider multiple bank-firm links based on a myopic preferred-partner choice. In case of default, we also consider the loss given default rate (LGDR).We find many results:(i) if leverage increases, the economy is riskier; (ii) a higher leverage pro- cyclicality has a destabilizing effect; (iii) a pro-cyclical leverage weakens the monetary policy effect;(iv) a central bank that wants to increase the interest rate should previously check if the banking system is well capitalized;(v) an increase of the reserve coefficient has an impact similar to that produced by raising the policy rate, but for the enlargement of bank reserves that improves the resilience of the banking system to shocks

Financialisation and crisis in an agent based macroeconomic model

We analyse the role of dividends distributed by firms and banks, highlighting the effects of their increase on financial instability and macroeconomic dynamics. During the last decades, the financialisation of nonfinancial corporations has been characterised by a shift from a "retain and reinvest" strategy to a "downsize and distribute" strategy. We will investigate such a phenomenon by varying some of the model parameters, so simulating firms\u2019 and banks\u2019 behaviours under alternative settings. On the one hand, more distributed dividends increases agents\u2019 wealth and thus consumption may rise due to a wealth-effect. On the other hand, increasing dividends reduce firms\u2019 net worth that may result in a strong dependence of firms\u2019 production on bank credit; at the same time, if also banks distribute more dividends, then banks\u2019 capital decreases and this may result in credit rationing. As we will see, financialisation through increasing dividends impacts financial (in)stability and income distribution, with relevant consequences on macroeconomic dynamics

Systemic risk measurement: bucketing global systemically important banks

The general consensus on the need to enhance the resilience of the financial system has led to the imposition of higher capital requirements for certain institutions, supposedly based on their contribution to systemic risk. Global Systemically Important Banks (G-SIBs) are divided into buckets based on their required additional capital buffers ranging from 1% to 3.5%. We measure the marginal contribution to systemic risk of 26 G-SIBs using the Distressed Insurance Premium methodology proposed by Huang et al. (J Bank Financ 33:2036\u20132049, 2009) and examine ranking consistency with that using the SRISK of Acharya et al. (Am Econ Rev 102:59\u201364, 2012). We then compare the bucketing using the two academic approaches and supervisory buckets. Because it leads to capital surcharges, bucketing should be consistent, irrespective of methodology. Instead, discrepancies in the allocation between buckets emerge and this suggests the complementary use of other methodologies