Various financial researches
Time-Varying Market Integration and Expected Returns in Emerging Markets
We propose a simple model in which the expected returns in emerging markets depend on their systematic risk as measured by their beta relative to the world portfolio as well as on the level of integration in the world market. The level of integration is a time-varying variable that depends on the market value of the assets that can be held by domestic investors only versus the market vale off the assets that can be traded freely. Our empirical analysis for 30 emerging markets shows that there are strong effects of the level of integration on the expected returns in emerging markets. We find that the overall decrease in segmentation has a strong effect on the level of returns in emerging markets, about 4.5 percent per year. The expected returns depend both on the level of segmentation of the emerging market itself and on the regional segmentation level. We find that on average 63 percent of the decrease in expected returns is due to the local level of segmentation and 37 percent of the regional level. Using dividend yields as a proxy for expected returns confirms the effect of the level of segmentation and we find an annual decrease in expected returns of about 11 basis points due to a decrease in segmentation. These conclusions do not change when using additional control variables.
Equilibrium asset pricing with time-varying pessimism
We study the pricing effects of pessimism, as modelled by Knightian model uncertainty aversion for a neighborhood of indistinguishable model specifications that are constrained in their relative entropy from a given reference model. We fully characterise the equilibrium of a pessimistic, representative agent, exchange economy with intertemporal consumption, stochastic opportunity, and a relative entropy constraint that can depend on the state of the economy. We find that Knightian pessimism yields outstanding First Order Risk Aversion (FORA) excess equity returns. On the other hand, equity dynamics are virtually unaffected. Precisely, riskfree rates and equity premia feel a direct impact of pessimism whereas equity returns and worst case equity premia feel an indirect impact only, which completely disappears with log utility. We compute and calibrate explicit equilibrium examples of a pessimistic economy whith an amount of pessimism associated to an 11% upper probability of making confusion between the worst case model and the reference model. Relative entropy is the key in fixing such a realistic amount of pessimism in our calibrations. Even for log utility, we find that such small amount of pessimism generates some 55 basis points more of unconditional equity premium by pushing riskfree rates down. Thus, Knightian pessimism provides an economically and observationally different description of excess equity returns. Its good theoretical and empirical properties could contribute to solve some of the long-standing macro finance puzzles.
Testing multivariate volatility and spillover effects in non-synchronous financial markets
The relationship between volatility and risk has been one of the main factors underlying the interest in volatility modelling. An important question for international diversification is whether shocks in one market influence, or have spillovers into, returns and volatility in other markets. This paper tests for the existence of volatility spillovers among the S&P 500, FTSE 100 and Nikkei 225 stock indexes using intra-daily data from 12/10/1992 to 7/7/2003. Existing work is extended through the application of the vector autoregressive moving average asymmetric generalised autoregressive conditional heteroskedasticity (VARMA-AGARCH) model of Chan, Hoti and McAleer (2002). The results suggest the presence of volatility spillovers from FTSE 100 to both S&P 500 and Nikkei 225, and from S&P 500 to FTSE 100. This paper tests for the sensitivity of the empirical results to: (1) the choice of base currency, and finds the results not to be sensitive to the currency used; (2) the choice of conditional mean specification, and finds that the results are sensitive to the specification used; and (3) the choice of multivariate effect included in the analysis, and finds that the exclusion of an important variable can substantially change the results. The stability of the conditional correlation matrix over time is also examined through the use of rolling windows. The results question the validity of the constant conditional correlation assumption imposed on the VARMA-AGARCH model, as well as several standard nested specifications, and suggest that time-varying or dynamic conditional correlations should be estimated..