Vassilis Polimenis

The global financial crisis and its transmission to Asia Pacific

By virtue of the US markets closing later than Asia-Pacific (AP) markets, returns observed in the US include information not reflected in AP until the next day. Provided there is enough integration among markets, this asymmetry should then generate Granger causality from the US returns towards returns in the AP region. It is thus obvious that when testing for Granger causality among the Western and AP markets, interdependence due to non-synchronicity should be clearly identified and factored out. Our novel method is to measure contagion, usually defined as excessive market integration during crises, as excess interdependence beyond the non-synchronicity induced. We test for contagion of the 2008 Financial Crisis from the US to AP. The unmistakable emergence of a new cointegration relation that intertwines the US and AP markets only during the crisis, but not before, presents evidence of long-run contagion, that is a new channel that transmits crisis-related info. We find strong contagion from US to Korea,...

Information Arrival as Price Jumps

We propose two new risk measures (i-beta and i-gamma) for a stock, which aim to distinguish between noise and information. Noise allows the stock price evolution to happen along a continuous path. Market wide economic information is transmitted via price jumps. Noise is idiosyncratic and does not propagate across securities. The main contribution is the development of an exact closed-form non-parametric jump risk estimator that boosts the “signal-to-noise�? ratio by utilizing co-skew moments. Empirically, the procedure is used to extract the i-beta and i-gamma for Google and Yahoo on NASDAQ, and provide an explanation of their seemingly low Sharpe ratio, due to their positively skewed returns.

Sensitivity analysis of market and stock returns by considering positive and negative jumps

Purpose This paper aims to present the results of further investigating the Polimenis (2012) stochastic model, which aims to decompose the stock return evolution into positive and negative jumps, and a Brownian noise (white noise), by taking into account different noise levels. This paper provides a sensitivity analysis of the model (through the analysis of its parameters) and applies this analysis to Google and Yahoo returns during the periods 2006-2008 and 2008-2010, by means of the third central moment of Nasdaq index. Moreover, the paper studies the behavior of the calibrated jump sensitivities of a single stock as market skew changes. Finally, simulations are provided for the estimation of the jump betas coefficients, assuming that the jumps follow Gamma distributions. Design/methodology/approach In the present paper, the model proposed in Polimenis (2012) is considered and further investigated. The sensitivity of the parameters for the Google and Yahoo stock during 2006-2008 estimated by means of the third (central) moment of Nasdaq index is examined, and consequently, the calibration of the model to the returns is studied. The associated robustness is examined also for the period 2008-2010. A similar sensitivity analysis has been studied in Polimenis and Papantonis (2014), but unlike the latter reference, where the analysis is done while market skew is kept constant with an emphasis in jointly estimating jump sensitivities for many stocks, here, the authors study the behavior of the calibrated jump sensitivities of a single stock as market skew changes. Finally, simulations are taken place for the estimation of the jump betas coefficients, assuming that the jumps follow Gamma distributions. Findings A sensitivity analysis of the model proposed in Polimenis (2012) is illustrated above. In Section 2, the paper ascertains the sensitivity of the calibrated parameters related to Google and Yahoo returns, as it varies the third (central) market moment. The authors demonstrate the limits of the third moment of the stock and its mixed third moment with the market so as to get real solutions from (S1). In addition, the authors conclude that (S1) cannot have real solutions in the case where the stock return time series appears to have highly positive third moment, while the third moment of the market is significantly negative. Generally, the positive value of the third moment of the stock combined with the negative value of the third moment of the market can only be explained by assuming an adequate degree of asymmetry of the values of the beta coefficients. In such situations, the model may be expanded to include a correction for idiosyncratic third moment in the fourth equation of (S1). Finally, in Section 4, it is noticed that the distribution of the error estimation of the coefficients cannot be considered to be normal, and the variance of these errors increases as the variance of the noise increases. Originality/value As mentioned in the Findings, the paper demonstrates the limits of the third moment of the stock and its mixed third moment with the market so as to get real solutions from the main system of equations (S1). It is concluded that (S1) cannot have real solutions when the stock return time series appears to have highly positive third moment, while the third moment of the market is significantly negative. Generally, the positive value of the third moment of the stock combined with the negative value of the third moment of the market can only be explained by assuming an adequate degree of asymmetry of the values of the beta coefficients. In such situations, the model proposed should be expanded to include a correction for idiosyncratic third moment in the fourth equation of (S1). Finally, it is noticed that the distribution of the error estimation of the coefficients cannot be considered to be normal, and the variance of these errors increases as the variance of the noise increases.

Jointly estimating jump betas

Purpose - – This paper aims to enhance a co-skew-based risk measurement methodology initially introduced in Polimenis, by extending it for the joint estimation of the jump betas for two stocks. Design/methodology/approach - – The authors introduce the possibility of idiosyncratic jumps and analyze the robustness of the estimated sensitivities when two stocks are jointly fit to the same set of latent jump factors. When individual stock skews substantially differ from those of the market, the requirement that the individual skew is exactly matched is placing a strain on the single stock estimation system. Findings - – The authors argue that, once the authors relax this restrictive requirement in an enhanced joint framework, the system calibrates to a more robust solution in terms of uncovering the true magnitude of the latent parameters of the model, at the same time revealing information about the level of idiosyncratic skews in individual stock return distributions. Research limitations/implications - – Allowing for idiosyncratic skews relaxes the demands placed on the estimation system and hence improves its explanatory power by focusing on matching systematic skew that is more informational. Furthermore, allowing for stock-specific jumps that are not related to the market is a realistic assumption. There is now evidence that idiosyncratic risks are priced as well, and this has been a major drawback and criticism in using CAPM to assess risk premia. Practical implications - – Since jumps in stock prices incorporate the most valuable information, then quantifying a stocks exposure to jump events can have important practical implications for financial risk management, portfolio construction and option pricing. Originality/value - – This approach boosts the “signal-to-noise” ratio by utilizing co-skew moments, so that the diffusive component is filtered out through higher-order cumulants. Without making any distributional assumptions, the authors are able not only to capture the asymmetric sensitivity of a stock to latent upward and downward systematic jump risks, but also to uncover the magnitude of idiosyncratic stock skewness. Since cumulants in a Levy process evolve linearly in time, this approach is horizon independent and hence can be deployed at all frequencies.

The Informational Loadings of a Stock

In this short paper, I selectively review some recent developments related to the idea that jumps in stock prices incorporate the most valuable information, and thus the quantification of a stock’s exposure to jump events is important for financial risk management and portfolio construction. There are two main methodologies of estimating jump betas: a) the more widely used high or ultra high frequency procedures that rely on the asymptotical behavior of elaborate and sophisticated econometric constructs, such as the bi-power variation or local averaging techniques in order to isolate market microstructure noise at high frequencies, and b) very recently a new non-parametric skew-based methodology that does not rely on the use of high frequency data and is thus immune to market microstructure noise.

Non-Stationary Dividend-Price Ratios

Dividend yields have been widely used in previous research to relate stock market valuations to cash flow fundamentals. However, this approach relies on the assumption that dividend yields are stationary. Due to the failure to reject the hypothesis of a unit root in the classical dividend-price ratio for the US stock market, Polimenis and Neokosmidis (2016) proposed the use of a modified dividend price ratio (mdp) as the deviation between d and p from their long run equilibrium, and showed that mdp provides substantially improved forecasting results over the classical dp ratio. Here, we extend that paper by performing multivariate regressions based on the Campbell-Shiller approximation, by utilizing a dynamic econometric procedure to estimate the modified dp, and by testing the modified ratios against reinvested dividend-yields. By comparing the performance of mdp and dp in the period after 1965, we are not only able to enhance the robustness of the findings, but also to debunk a possible false explanation that the enhanced mdp performance in predicting future returns comes from a capacity to predict the risk-free return component. Depending on whether one uses the recursive or population methodology to measure the performance of mdp, the Out-of-Sample performance gain is between 30% to 50%.

Day-of-the-week effect around the 2008 financial crisis

This paper investigates the existence and possible changes of day-of-the-week effect before and during the 2008 financial crisis. We use six regional equity markets of varying degrees of maturity. While for the sample period Bulgaria, Romania, Cyprus and Ukraine are clearly considered emerging markets, the Greek and the Turkish markets are characterised by large overall capitalisations and exhibit significantly more depth and turnover. Our tests are non-parametric, allowing for relaxation of the normality in the data. Our findings show mixed evidence: in the more developed markets of Greece and Turkey, day-of-the-week effects fade away during the crisis. Contrary to Bulgaria, Cyprus shows no evidence of significant market inefficiency. Finally, Romania and Ukraine report opposite results, by exhibiting day-of-the-week effect during the crisis sub-period. The contradictory evidence is probably due to a different level of maturity, liquidity and interdependence for these markets.