Political instability,political freedom and inflation

Using a dynamic panel data approach, we estimate the impact of the political and institutional factors on inflation. Estimation results show that a lower degree of political instability generates lower inflation only for developed and low-inflation countries. However, when political freedom is taken into account, political instability appears to be influential on inflation also for developing countries and turns out to be significant only for high-inflation countries. Such findings emphasize the inflation-reducing effects of political stability depending on democratic political structure.     

The asymmetric effects of monetary shocks: the case of Turkey

This paper examines whether there is an asymmetry in the effects of positive versus negative and small versus big money supply shocks, and whether the effects of the shocks on output and prices vary over the business cycles in the case of Turkey. Negative shocks to money are found to have greater output and smaller price effects compared to the effects of positive shocks, irrespective of the initial state of the economy. It is also found that monetary shocks of different size affect output growth and inflation rates proportionately. These findings can be interpreted as evidence for the view that the short run aggragate supply curve is convex in such a country like Turkey.     

Partial hedging and cash requirements in discrete time

This paper develops a discrete time version of the continuous time model of Bouchard et al. [J. Control Optim., 2009, 48, 3123–3150], for the problem of finding the minimal initial data for a controlled process to guarantee reaching a controlled target with probability one. An efficient numerical algorithm, based on dynamic programming, is proposed for the quantile hedging of standard call and put options, exotic options and quantile hedging with portfolio constraints. The method is then extended to solve utility indifference pricing, good-deal bounds and expected shortfall problems.     

Forecasting high-frequency excess stock returns via data analytics and machine learning

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.     

Adverse selection in cryptocurrency markets

In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse-selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse-selection costs for major cryptocurrencies. Also, our results suggest that adverse-selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse-selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse-selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.