Analyzing, Modeling, and Utilizing Observation Series Correlation in Capital Markets

In this paper, we consider the task of the analysis, modeling, and application of dependencies between asset quotes at various capital markets. As an example, we study the dependency between financial instrument observation series in the currency and stock markets. Our work intends to give a theoretical basis to asset management strategies that estimate an asset’s price via regression, taking into account its correlated assets in various markets. Furthermore, we provide a way to increase the estimate quality using an evolutionary algorithm.

Do People Understand the Benefit of Diversification?

Diversification—investing in imperfectly correlated assets—reduces volatility without sacrificing expected returns. Although the expected return of a diversified portfolio is the weighted average return of its constituent parts, the variance of the portfolio is less than the weighted average variance of its constituent parts. Our results suggest that very few people have correct statistical intuitions about the effects of diversification. The average person in our data sees no benefit of diversification in terms of reducing portfolio volatility. Many people, especially those low in financial literacy, believe diversification actually increases the volatility of a portfolio. These people seem to believe that the unpredictability of individual assets compounds when aggregated together. Additionally, most people believe diversification increases the expected return of a portfolio. Many of these people correctly link diversification with the concept of risk reduction but seem to understand risk reduction to mean greater returns on average. We show that these beliefs can lead people to construct investment portfolios that mismatch investors’ risk preferences. Furthermore, these beliefs may help explain why many investors are underdiversified. This paper was accepted by Yuval Rottenstreich, decision analysis.

Managing Market Risk and Controlling VAR

The market risk management in a portfolio selection of correlated assets is considered in this chapter. The chapter elaborates how to construct and select an optimal portfolio of correlated assets in order to control VAR considering the risk associated limits. Stochastic optimisation is used to construct the efficient frontier of minimal mean variance investment portfolios with maximal return and a minimal acceptable risk. Monte Carlo simulation is utilised to stochastically calculate and measure the portfolio return, Variance, Standard Deviation, VAR and Sharpe Ratio of the efficient frontier portfolios. Six Sigma process capability metrics are also stochastically calculated against desired specified target limits for VAR and Sharpe Ratio of the Efficient Frontier portfolios. Simulation results are analysed and the optimal portfolio is selected from the Efficient Frontier based on the criteria of maximum Sharpe Ratio.

Covariance estimation using random permutations

This paper explores a novel technique to compute the level of covariance between any two genuinely correlated assets by adopting the idea of random permutations by proposing an unbiased covariance estimator “[Formula: see text]” based on daily high-low prices. The main goal is to boost the relative efficiency of the estimator by increasing the number of random permutations. We validate this claim with the help of simulations later. Further, we prove theoretically and through simulations that the proposed estimator is unbiased for a pair of random walks. Upon empirically implementing the estimator in a dataset of three sets of stock indices: Nifty, FTSE100 and S&P500 after accounting for exchange effects (USDINR and GBPINR) over a sample period of 252 months (Jan 1996–Dec 2016), we do not find evidence of any bias in the estimator. Also, there is a visible asymmetry in the correlation between US-Indian markets from the two investor’s point of view.

Information Spillovers in Asset Markets with Correlated Values

We study information spillovers in a dynamic setting with correlated assets owned by privately informed sellers. In the model, a trade of one asset can provide information about the value of other assets. Importantly, the information content of trading behavior is endogenously determined. We show that this endogeneity leads to multiple equilibria when assets are sufficiently correlated. The equilibria are ranked in terms of both trade volume and efficiency. The model has implications for policies targeting post-trade transparency. We show that introducing post-trade transparency can increase or decrease welfare and trading volume depending on the asset correlation, equilibrium being played, and the composition of market participants. (JEL D82, D83, G14, G18)