Carl Lönnbark

Simultaneity and asymmetry of returns and volatilities: the emerging Baltic States' stock exchanges

The paper suggests a nonlinear and multivariate time series model framework that enables the study of simultaneity in returns and in volatilities, as well as asymmetric effects arising from shocks and exogenous variables. The model is employed to study the three closely related Baltic States’ stock exchanges and the influence exerted by the Russian stock exchange. Using daily data, we find recursive structures with returns in Riga directly depending on returns in Tallinn and Vilnius, and Tallinn on Vilnius. For volatilities, both Riga and Vilnius depend on Tallinn. In addition, we find evidence of asymmetric effects of shocks arising in Moscow and in Baltic States on both returns and volatilities.

A corrected Value-at-Risk predictor

In this article, it is argued that the estimation error in Value-at-Risk (VaR) predictors gives rise to underestimation of portfolio risk. We propose a simple correction and find in an empirical illustration that it is economically relevant.

Profitability of Technical Trading Rules on the Baltic Stock Markets

In this note we study whether simple technical trading rules are profitable on the three Baltic stock markets. To statistically assess our findings we consider the conventional t-test and a block-bootstrap procedure. The two evaluation methods give conflicting results. The t-test supports some of the rules, while the block-bootstrap does not.

Approximation methods for multiple period Value at Risk and Expected Shortfall prediction

In this paper, we are interested in predicting multiple period Value at Risk and Expected Shortfall based on the so-called iterating approach. In general, the properties of the conditional distribution of multiple period returns do not follow easily from the one-period data generating process, rendering this a non-trivial task. We outline a framework that forms the basis for setting approximations and study four different approaches. Their performance is evaluated by means of extensive Monte Carlo simulations based on an asymmetric GARCH model, implying conditional skewness and excess kurtosis in the multiple period returns. This simulation-based approach was the best one, closely followed by that of assuming a skewed t-distribution for the multiple period returns. The approach based on a Gram–Charlier expansion was not able to cope with the implied non-normality, while the so-called Root-k approach performed poorly. In addition, we outline how the delta-method may be used to quantify the estimation error in the predictors and in the Monte Carlo study we found that it performed well. In an empirical illustration, we computed 10-day Value at Risk’s and Expected Shortfall for Brent Crude Oil, the EUR/USD exchange rate and the S&P 500 index. The Root-k approach clearly performed the worst and the other approaches performed quite similarly, with the simulation based approach and the one based on the skewed t-distribution somewhat better than the one based on the Gram–Charlier expansion.

Identification of Jumps in Financial Time Series

The paper outlines and tests, by means of Monte-Carlo simulations, a simple strategy of using existing non-parametric tests for jumps at the daily frequency to identify jumps at higher sampling frequencies. The suggested strategy allow for identification of the number of jumps and jump times during a day, as well as, the size and direction (negative or positive) of the jumps. The method is of importance in order to facilitate detailed empirical studies concerning, for example, causes for jumps in financial price series at finer levels than the daily. The Monte Carlo study reveals that the strategy works reasonably well, particular for lower jump intensities. An application of the studied strategy on the Handelsbanken stock is provided.

Effects of Explanatory Variables in Count Data Moving Average Models

This note gives dynamic effects of discrete and continuous explanatory variables for count data or integer-valued moving average models. An illustration based on a model for the number of transactions in a stock is included.