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Purpose – The purpose of this chapter is to introduce new methods to behavioral research on group negotiation.

Design/methodology/approach – We describe three techniques from the field of Machine Learning and discuss their possible application to modeling dynamic processes in group negotiation: Markov Models, Hidden Markov Models, and Inverse Reinforcement Learning. Although negotiation research has employed Markov modeling in the past, the latter two methods are even more novel and cutting-edge. They provide the opportunity for researchers to build more comprehensive models and to use data more efficiently. To demonstrate their potential, we use scenarios from group negotiation research and discuss their hypothetical application to these methods. We conclude by suggestions for researchers interested in pursuing this line of work.

Originality/value – This chapter introduces methods that have been successfully used in other fields and discusses how these methods can be used in behavioral negotiation research. This chapter can be a valuable guide to researchers that would like to pursue computational modeling of group negotiation.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

This chapter presents a model of distribution dynamics in the presence of measurement error in the underlying data. Studies of international growth convergence generally ignore the fact that per capita income data from the Penn World Table (PWT) are not only continuous variables but also measured with error. Together with short-time scale fluctuations, measurement error makes inferences potentially unreliable. When first-order, time-homogeneous Markov models are fitted to continuous data with measurement error, a bias towards excess mobility is introduced into the estimated transition probability matrix. This chapter evaluates different methods of accounting for this error. An EM algorithm is used for parameter estimation, and the methods are illustrated using data from the PWT Mark 6.1. Measurement error in income data is found to have quantitatively important effects on distribution dynamics. For instance, purging the data of measurement error reduces estimated transition intensities by between one- and four-fifths and more than halves the observed mobility of countries.

Recently, there has been much progress in developing Markov switching stochastic volatility (MSSV) models for financial time series. Several studies consider various MSSV specifications and document superior forecasting power for volatility compared to the popular generalized autoregressive heteroscedasticity (GARCH) models. However, their application to option pricing remains limited, partially due to the lack of convenient closed-form option pricing formulas which integrate MSSV volatility estimates. We develop such a closed-form option pricing formula and the corresponding hedging strategy for a broad class of MSSV models. We then present an example of application to two of the most popular MSSV models: Markov switching multifractal (MSM) and component-driven regime switching (CDRS) models. Our results establish that these models perform well in one-day-ahead forecasts of option prices.

Many Information Theoretic Measures have been proposed for a quantitative assessment of causality relationships. While Gouriéroux, Monfort, and Renault (1987) had introduced the so-called “Kullback Causality Measures,” extending Geweke’s (1982) work in the context of Gaussian VAR processes, Schreiber (2000) has set a special focus on Granger causality and dubbed the same measure “transfer entropy.” Both papers measure causality in the context of Markov processes. One contribution of this paper is to set the focus on the interplay between measurement of (non)-markovianity and measurement of Granger causality. Both of them can be framed in terms of prediction: how much is the forecast accuracy deteriorated when forgetting some relevant conditioning information? In this paper we argue that this common feature between (non)-markovianity and Granger causality has led people to overestimate the amount of causality because what they consider as a causality measure may also convey a measure of the amount of (non)-markovianity. We set a special focus on the design of measures that properly disentangle these two components. Furthermore, this disentangling leads us to revisit the equivalence between the Sims and Granger concepts of noncausality and the log-likelihood ratio tests for each of them. We argue that Granger causality implies testing for non-nested hypotheses.

This paper proposes an efficient test designed to have power against alternatives where the error correction term follows a Markov switching dynamics. The adjustment to long run equilibrium is different in different regimes characterised by the hidden state Markov chain process. Using a general nonlinear MS-ECM framework, we propose an optimal test for the null of no cointegration against an alternative of a globally stationary MS cointegration. The Monte Carlo studies demonstrate that our proposed tests display superior powers compared to the linear tests. In an application to price-dividend relationships, our test is able to find cointegration while linear based tests fail to do so.

The purpose of this paper is to provide an extensive review of the monetary model of exchange rate determination which is the main theoretical framework on analyzing exchange rate behavior over the last 40 years. Furthermore, we test the flexible price monetarist variant and the sticky price Keynesian variant of the monetary model. We conduct our analysis employing a sample of 14 advanced economies using annual data spanning the period 1880–2012.

The theoretical background of the paper relies on the monetary model to the exchange rate determination. We provide a thorough econometric analysis using a battery of unit root and cointegration testing techniques. We test the price-flexible monetarist version and the sticky-price version of the model using annual data from 1880 to 2012 for a group of industrialized countries.

We provide strong evidence of the existence of a nonlinear relationship between exchange rates and fundamentals. Therefore, we model the time-varying nature of this relationship by allowing for Markov regime switches for the exchange rate regimes. Modeling exchange rates within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. These results show that linearity is rejected in favor of an MS-VECM specification which forms statistically an adequate representation of the data. Two regimes are implied by the model; the one of the estimated regimes describes the monetary model whereas the other matches in most cases the constant coefficient model with wrong signs. Furthermore it is shown that depending on the nominal exchange rate regime in operation, the adjustment to the long run implied by the monetary model of the exchange rate determination came either from the exchange rate or from the monetary fundamentals. Moreover, based on a Regime Classification Measure, we showed that our chosen Markov-switching specification performed well in distinguishing between the two regimes for all cases. Finally, it is shown that fundamentals are not only significant within each regime but are also significant for the switches between the two regimes.

The results are of interest to practitioners and policy makers since understanding the evolution and determination of exchange rates is of crucial importance. Furthermore, our results are linked to forecasting performance of exchange rate models.

The present analysis extends previous analyses on exchange rate determination and it provides further support in favor of the monetary model as a long-run framework to understand the evolution of exchange rates.

Introduction: Studies on the insurance sector/companies have, in recent years, taken their place in literature at an increasing rate. Especially after the 2008 global financial crisis, the need for people to ensure their assets has structurally changed both the transaction volume and the yield structure of insurance sector. The increase in demand for insurance has also increased the appetite of investors to make an investment on this sector. The transaction volume of the insurance sector has increased year by year coupled with the number of insurance companies traded on the stock exchanges has started to increase in the same direction.

Aim: This chapter aims to determine the return structure of the Borsa Istanbul Insurance Index (XSGRT) based on daily closing values.

Method: Markedly with similar studies in the literature review, the authors determined that the Markov Regime Switching (MRS) model is the best-suited model for the current research. It was applied for the data set of XSGRT Index from 1997 to 2020.

Results: The result shows that XSGRT has three regimes named as expansion regime, normal regime and recession regime. Subsequently, it has been determined that the index generally attends to transition from the recession regime to the expansion regime and normal regime. This outcome is statistically significant at a 5% significance level and confirmed by backtesting results. Likewise, the duration of the recession regime is longer than the normal and expansion regime.

Conclusion: Despite the fact that the XSGRT has not yet completed its development compared to other main and sectoral indices, it is one of the indices that offer attractive earnings for investors. To put it differently, the desire of insurance companies to stay longer totally in the normal and expansion period and their immediate exit from the recession period provides them with a significant competitive advantage in contrast to other indices.

Originality/Value: This research contributes to the literature by providing additional evidence for existing studies using the longer duration of data set and applying the MRS model for Insurance Index. Best of our knowledge, it is the first study that examines the return structure of XSGRT based on its daily closing values from 1997 to 2020. In essence, investors can use the result of this study and compare it with other stock indices to make the accurate investment decision to maximise their welfare and return on their equity investments. The authors suggest that not only the return but also the regime structures of the invested shares (indices) should be taken into account for investment decisions.