Explaining Aggregated Recovery Rates

This study on explaining aggregated recovery rates (ARR) is based on the largest existing loss and recovery database for commercial loans provided by Global Credit Data, which includes defaults from 5 continents and over 120 countries. The dependence of monthly ARR from bank loans on various macroeconomic factors is examined and sources of their variability are stated. For the first time, an influence of stochastically estimated monthly growth of GDP USA and Europe is quantified. To extract monthly signals of GDP USA and Europe, dynamic factor models for panel data of different frequency information are employed. Then, the behavior of the ARR is investigated using several regression models with unshifted and shifted explanatory variables in time to improve their forecasting power by taking into account the economic situation after the default. An application of a Markov switching model shows that the distribution of the ARR differs between crisis and prosperity times. The best fit among the compared models is reached by the Markov switching model. Moreover, a significant influence of the estimated monthly growth of GDP in Europe is observed for both crises and prosperity times.

Nilai Transaksi E-Money di Indonesia dengan Menggunakan Metode Markov Switching Model

The purpose of the research is to examine and analyze the interest rates, inflation rates, stock returns, and holiday conditions on the values of e-money transactions based on two conditions, before and after the issuance of Bank Indonesia regulations. The research data used in this study is the monthly statistical data of the Bank Indonesia payment system for the period 2008-2018. While, the research was the quantitative using the Markov Regime Switching Model and hypothesis testing using time series regression. The research results showed that there was a significant effect between the inflation rate and the value of e-money transactions. In addition, there was an insignificant effect on interest rates, stock returns, and holiday conditions on the value of e-money transactions. This research contributes to the development of economic research by predicting the value of e-money transactions and predicting the value e-money transactions and predicting the turning point of e-money transactions value based on two conditions, before and after the issuance of Bank Indonesia regulations and can be used as input to the government regarding the regulations that issued by Bank of Indonesia, regulations regarding increased the value of e-money transactions.

Estimating the FOMC’s interest rate rule with variable selection and partial regime switching

When studying the Federal Open Market Committee’s (FOMC’s) interest rate rule, some authors, such as Gonzalez-Astudillo [(2018) Journal of Monetary, Credit, and Banking 50(1), 115–154.], find evidence for changes in inflation and output gap responses. Others, such as Sims and Zha [(2006) American Economic Review 96(1), 54–81.], only find evidence for a change in the variance of the interest rate rule. In this paper, I develop a new two-regime Markov-switching model that probabilistically performs variable selection and identification of parameter change for each variable in the model. I find substantial evidence that there have been changes in the FOMC’s response to the unemployment gap and in the volatility of the rule. When the FOMC responds strongly to the unemployment gap, I find a bimodal density for the inflation response coefficient. Despite the bimodal density, there is a low probability that there have been changes in the FOMC’s response to inflation.

Mean reversion in a price-earnings ratio and under / overvaluation in the Brazilian stock market

ABSTRACT The market price-earnings ratios differ from those of each share. Despite allowing for several pertinent analyses, authors have rarely addressed these valuation ratios in the Brazilian context. We can use it to evaluate whether the stock market is overvalued (undervalued). In this article, we analyze the mean reversion in a price-earnings ratio based on Ibovespa and identify periods of overvaluation (undervaluation) in the Brazilian stock market. We considered the period from December 2004 to June 2018. Until then, there are no studies that sought to identify periods of overvaluation (undervaluation) in this market. In the analyses, we used non-linear econometric methods. We analyzed the mean reversion in the price-earnings ratio using a unit root test that incorporates a Fourier function in the deterministic term. We identified the periods of market overvaluation (undervaluation) through the regime probabilities obtained from a Markov Switching model, estimated with the price-earnings ratio. The results evidenced that the price-earnings ratio based on the Ibovespa has a non-linear trend and exhibits mean reversion. Thus, this valuation ratio should provide information on the future stock market returns, mostly when it is very dispersed in relation to historical standards. We identified four periods of market overvaluation interposed with five periods of market undervaluation. Mean reversion in the price-earnings ratio contraposes the Efficient Markets Hypothesis. There are no other applications of unit root tests with a Fourier function in the Brazilian context. Furthermore, adopting a Markov Switching model to identify periods of market overvaluation (undervaluation) consists of a methodological contribution. Investors can take advantage of the identification of these periods to establish investment strategies.

The effect of financial, macroeconomic and sentimental factors on stock market volatility

The aim of this paper is to find economic factors that could be helpful in explaining the market’s shifts between periods of prosperity and crisis. The study took into account the main stock indices from developed markets of the USA, Germany and Great Britain, and from two emerging markets, i.e. Poland and Turkey. The analysis confirms the existence of two different states of volatility in these markets, namely the state with a positive returns’ mean and low volatility, and the state with a negative or insignificant mean and high volatility. The Markov-switching model with a dynamic probability matrix was applied in the study. The subject of the analysis was the impact of domestic and global factors, such as VIX and TED spread, oil prices, sentiment indices (ZEW), and macroeconomic indices (unemployment, longterm interest rate, CPI), on the probability of switching between the states. The authors concluded that in all the examined countries, changes in long-term interest rates have an influence on market returns. However, the direction of this impact is different for developed and emerging markets. As regards developed markets, high prices of oil, 10-year bonds, and the ZEW index can suggest a high probability of the countries remaining in the first state, whereas an increase in the VIX index and the TED spread significantly reduces the probability of staying in this state. The other studied factors proved to be rather local in nature.

Can Adaptive Market Hypothesis Explain the Existence of Seasonal Anomalies? Evidence from Dhaka Stock Exchange, Bangladesh

This paper examines the behavior of seasonal anomalies in Dhaka Stock Exchange (DSE) of Bangladesh and whether the time varying nature of the anomalies is in line with Adaptive Market Hypothesis (AMH). With this aim the research investigated whether the changes in market conditions, for example: up and down market states, stock market bubbles and crashes, initiation of automated trading system and circuit breaker system can affect the behavior of calendar anomalies and therefore, can provide justification for the seasonal patterns in DSE. To achieve the stated objectives, this study utilizes daily general index values of DSE from 1993 to 2018, with GARCH (1,1) model, Markov switching model, subsample analysis and rolling window analysis. The findings support the existence of AMH at DSE in the form of time-varying nature of seasonal anomalies. However, not all seasonal anomalies examined in the study were found to grow weaker over time. The most important finding of this study is that the investors in emerging stock markets, for example DSE, may not learn from the past investment experiences and show the adapting ability towards changed market conditions in the same manner like the investors in a developed market.

Transition Assessment of the Bangladeshi Financial Market Stress Regimes: A Markov Switching Modeling Approach

Purpose of this study: In view of the global financial crises and the ensuing consequences, this research presents the utility of demonstrating an assessment that can forecast the Bangladeshi financial market’s well-being by analysing episodes of economic crises which may prevent market distress. By graphically demonstrating eventual economic episodes in the financial sector, this study sets out to illustrate the chronological scenario of economic turning points. The scope of this research is to study the vulnerable aspects of financial instability in Bangladesh and seek possible remedies. Methodology: The Bangladeshi financial market regimes will be constructed based on Hamilton's Markov Switching Model (1989). This paper is the first attempt in utilising a standardised methodology found in business cycle literatures so as to determine the turning points of economic episodes in the Bangladeshi financial dynamic cycle. Main Findings: This study examines the financial crises and economic distress experienced by banks as forms of economic vulnerabilities. Thus, it describes the financial regimes of transition period movements in the context of the vulnerability of the Bangladeshi financial market sector using the Markov Switching Modeling (MSM) Approach and shows ways to possibly achieve recovery. Research Limitations/Implications: This research focuses on the current financial episodes of the economic sector’s dynamic movements in a condensed area, while the selection of a broad financial arena of parameters results in more significant and robust outcomes. Novelty/Originality: Further studies are needed to define and measure the financial cycle concept and its relationship with business cycles, as well as to delineate dynamic models that can offer substantial probabilistic assessments regarding changes in financial cycle regimes. This can significantly develop the capability of the financial market supervisory authorities to forecast macro-prudential systemic risks and to avoid or reduce the consequences of economic crises. This current study provides a platform for future studies in similar fields.

Markov Switching

Markov switching models are a family of models that introduces time variation in the parameters in the form of their state, or regime-specific values. This time variation is governed by a latent discrete-valued stochastic process with limited memory. More specifically, the current value of the state indicator is determined by the value of the state indicator from the previous period only implying the Markov property. A transition matrix characterizes the properties of the Markov process by determining with what probability each of the states can be visited next period conditionally on the state in the current period. This setup decides on the two main advantages of the Markov switching models: the estimation of the probability of state occurrences in each of the sample periods by using filtering and smoothing methods and the estimation of the state-specific parameters. These two features open the possibility for interpretations of the parameters associated with specific regimes combined with the corresponding regime probabilities. The most commonly applied models from this family are those that presume a finite number of regimes and the exogeneity of the Markov process, which is defined as its independence from the model’s unpredictable innovations. In many such applications, the desired properties of the Markov switching model have been obtained either by imposing appropriate restrictions on transition probabilities or by introducing the time dependence of these probabilities determined by explanatory variables or functions of the state indicator. One of the extensions of this basic specification includes infinite hidden Markov models that provide great flexibility and excellent forecasting performance by allowing the number of states to go to infinity. Another extension, the endogenous Markov switching model, explicitly relates the state indicator to the model’s innovations, making it more interpretable and offering promising avenues for development.