scholarly journals Risk Analysis Of Hedge Funds: A Markov Switching Model Analysis

2013 ◽  
Vol 30 (1) ◽  
pp. 243 ◽  
Author(s):  
Frederic Teulon ◽  
Khaled Guesmi ◽  
Saoussen Jebri
Keyword(s):  
Risk Analysis ◽  
Hedge Funds ◽  
Regime Switching ◽  
Emerging Market ◽  
Markov Switching ◽  
Hedge Fund ◽  
Markov Switching Model ◽  
Market Strategy ◽  
Switching Model ◽  
High Volatility

The paper applies Markov Regime Switching GARCH Model (SW-GARCH) to investigate the volatility behavior of strategies hedge fund monthly returns for the period 1997-2011. The results highlight two different regimes: The first regime is characterized by a high volatility for all strategies hedge fund monthly returns. The second is characterised by lower volatility and positive average returns (except Emerging Market strategy). Our results helped to capture even the short-lived crises along with the material crises of 2001 and 2008.

scholarly journals Markov Switching Model Analysis of Implied Volatility for Market Indexes with Applications to S&P 500 and DAX

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Luca Di Persio ◽  
Samuele Vettori
Keyword(s):  
Regime Switching ◽  
Implied Volatility ◽  
Financial Time Series ◽  
Markov Switching ◽  
Sampling Procedure ◽  
Model Parameters ◽  
Classical Approach ◽  
Markov Switching Model ◽  
Discrete State ◽  
Switching Model

We adopt aregime switchingapproach to study concrete financial time series with particular emphasis on their volatility characteristics considered in a space-time setting. In particular the volatility parameter is treated as an unobserved state variable whose value in time is given as the outcome of an unobserved, discrete-time and discrete-state, stochastic process represented by a suitable Markov chain. We will take into account two different approaches for inference on Markov switching models, namely, the classical approach based on the maximum likelihood techniques and the Bayesian inference method realized through a Gibbs sampling procedure. Then the classical approach shall be tested on data taken from theStandard & Poor’s 500and theDeutsche Aktien Indexseries of returns in different time periods. Computations are given for a four-state switching model and obtained numerical results are put beside by explanatory graphs which report the outcomes obtained exploiting both smoothing and filtering algorithms used in the estimation/calibration procedures we proposed to infer on the switching model parameters.

scholarly journals TESTING FOR REGIME SWITCHING IN SINGAPOREAN BUSINESS CYCLES

2005 ◽  
Vol 50 (01) ◽  
pp. 25-34 ◽  
Author(s):  
ROBERT BREUNIG ◽  
ALISON STEGMAN
Keyword(s):  
Model Selection ◽  
Business Cycles ◽  
Nonlinear Model ◽  
Regime Switching ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Intended Use ◽  
Better Than ◽  
Model Fits

We examine a Markov-Switching model of Singaporean GDP using a combination of formal moment-based tests and informal graphical tests. The tests confirm that the Markov-Switching model fits the data better than a linear, autoregressive alternative. The methods are extended to allow us to identify precisely which features of the data are better captured by the nonlinear model. The methods described here allow model selection to be related to the intended use of the model.

AN N-STATE ENDOGENOUS MARKOV-SWITCHING MODEL WITH APPLICATIONS IN MACROECONOMICS AND FINANCE

2019 ◽  
pp. 1-29
Author(s):  
Shih-Tang Hwu ◽  
Chang-Jin Kim ◽  
Jeremy Piger
Keyword(s):  
Regime Switching ◽  
Likelihood Function ◽  
Markov Switching ◽  
Ratio Test ◽  
Markov Switching Model ◽  
Switching Model ◽  
Finite Samples ◽  
Endogenous Switching ◽  
Volatility Model ◽  
Three State Model

We develop an N-regime Markov-switching model in which the latent state variable driving the regime switching is endogenously determined with the model disturbance term. The model’s structure captures a wide variety of patterns of endogeneity and yields a simple test of the null hypothesis of exogenous switching. We derive an iterative filter that generates objects of interest, including the model likelihood function and estimated regime probabilities. Using simulation experiments, we demonstrate that the maximum likelihood estimator performs well in finite samples and that a likelihood ratio test of exogenous switching has good size and power properties. We provide results from two applications of the endogenous switching model: a three-state model of US business cycle dynamics and a three-state volatility model of US equity returns. In both cases, we find statistically significant evidence in favor of endogenous switching.

scholarly journals Hedge Funds Development and their Role in Economic Crises

2013 ◽  
Vol 60 (1) ◽  
pp. 168-181
Author(s):  
Theodosios Palaskas ◽  
Chrysostomos Stoforos ◽  
Costantinos Drakatos
Keyword(s):  
Hedge Funds ◽  
Emerging Market ◽  
Rapid Development ◽  
Critical Role ◽  
Hedge Fund ◽  
Subprime Crisis ◽  
Economic Crises ◽  
Market Strategy ◽  
Geographic Expansion ◽  
The Impact

Abstract The rapid development of hedge funds and their emanating critical role in the financial markets and the financial system globally, combined with the increased frequency of economic crises during the last 25 years, brought them to the centre of discussions concerning the following issue: «To what extent the operation of hedge funds can affect the birth, peak and even geographic expansion of economic crises?». In this context, the present paper aims to contribute to the limited and sporadic discussion of whether the hedge funds could be held responsible for economic crises. To this extend the growth and the impact of hedge funds on financial crises is analysed and evaluated using the HFR database -in their birth, aggravation or even geographic expansion- both from a historical perspective and in relation to the 2007-today crisis. Based on the evidence presented in this paper, hedge funds cannot be blamed for the birth of the crises of the last 25 years. Comparing the data across the different crises, it becomes obvious that, with the exception of the 2007 subprime crisis, where almost all hedge fund strategies suffered considerable losses, in all other crises studied in the present paper, the hedge fund strategies with a negative return were the ones that had an exposure to the specific sector and/or region that was in the centre of the crisis i.e. Emerging market strategy presented substantial negative monthly performance over the Asian crisis, Convertible arbitrage strategy was affected by the dot-com crisis, etc.

scholarly journals Modelling foreign exchange rates: a comparison between markov-switching and markov-switching GARCH

2020 ◽  
Vol 20 (2) ◽  
pp. 917
Author(s):  
Mohd Azizi Amin Nunian ◽  
Siti Meriam Zahari ◽  
S.Sarifah Radiah Shariff
Keyword(s):  
Exchange Rates ◽  
Foreign Exchange ◽  
Markov Switching ◽  
Foreign Exchange Rates ◽  
Markov Switching Model ◽  
Switching Model ◽  
The Us ◽  
Log Likelihood ◽  
High Volatility ◽  
Japanese Yen

Foreign exchange rate is important as it determines a country's economic condition. It is used to carry out transfers of purchasing power between two or more countries. Volatility in exchange rates may result in difficulty in decision making especially, in financial sectors as high volatility could increase the risk in exchange rates. Thus, Markov switching model is employed in this study as it is believed to be efficient in handling not only volatilility but also nonlinearity characteristics in exchange rates. The aims of this study are to model the foreign exchange rates using two models; Markov Switching (M-S) models and Markov Switching Generalized Autoregressive Conditional Heteroscedasticity (M-S GARCH) and to compare these two models based on log-likelihood, AIC and BIC criteria. This study used the quarterly data of foreign exchange rates for Singapore Dollar (SGD), Korean Won (KRW), China Yuan Renminbi (CNY), Japanese Yen (JPY) and the US Dollar (USD) against Malaysia Ringgit (MYR) which were collected from Quarter 4, 2006 to Quarter 1, 2018. The findings indicate that Markov Switching is the best model since it has the highest log-likelihood value, and the lowest AIC and BIC values. The results show that JPY and SGD have highly persistent trends on regime 1 with probability values 0.96 and 0.84, respectively as compared to CNY, KRW and USD, while the latter have high persistent trends on regime 2 with probability values, 0.99, 0.95, 0.82, respectively.

Portfolio optimization in hedge funds by OGARCH and Markov Switching Model

Omega ◽  
2015 ◽  
Vol 57 ◽  
pp. 34-39 ◽  
Author(s):  
Cuicui Luo ◽  
Luis Seco ◽  
Lin-Liang Bill Wu
Keyword(s):  
Portfolio Optimization ◽  
Hedge Funds ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model

scholarly journals Unemployment rate and wage growth in Brazil: evidence from a Markov-switching model

2020 ◽  
Vol 24 (2) ◽  
pp. 171-194
Author(s):  
Wellington Charles Lacerda Nobrega ◽  
Cássio da Nóbrega Besarria ◽  
Felipe Araújo De Oliveira
Keyword(s):  
Unemployment Rate ◽  
Regime Switching ◽  
Phillips Curve ◽  
Markov Switching ◽  
Wage Growth ◽  
New Keynesian ◽  
Switching Regression ◽  
Markov Switching Model ◽  
Switching Model ◽  
Key Factor

This paper has the purpose to investigate the relationship between unemployment rate and wage growth for the Brazilian economy from 2000to 2016, by means of a Markov-switching regression model. The empirical approach is based on the New-Keynesian Phillips Curve developed by Galí (2011). The estimation results suggest the existence of two well definedregimes, one characterized by the non-validation of the Phillips Curve, while in the other the trade-off between unemployment and wageinflation is validated, with the economic cycle being a key factor in regime switching.

scholarly journals The Regime-Switching Behaviour of Exchange Rates and Frontier Stock Market Prices in Sub-Saharan Africa

2021 ◽  
Vol 14 (3) ◽  
pp. 122
Author(s):  
Maud Korley ◽  
Evangelos Giouvris
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Regime Switching ◽  
Markov Switching ◽  
Sub Saharan Africa ◽  
Var Model ◽  
High Volatility ◽  
Sub Saharan ◽  
The Relationship ◽  
Fx Market

Frontier markets have become increasingly investible, providing diversification opportunities; however, there is very little research (with conflicting results) on the relationship between Foreign Exchange (FX) and frontier stock markets. Understanding this relationship is important for both international investor and policymakers. The Markov-switching Vector Auto Regressive (VAR) model is used to examine the relationship between FX and frontier stock markets. There are two distinct regimes in both the frontier stock market and the FX market: a low-volatility and a high-volatility regime. In contrast with emerging markets characterised by “high volatility/low return”, frontier stock markets provide high (positive) returns in the high-volatility regime. The high-volatility regime is less persistent than the low-volatility regime, contrary to conventional wisdom. The Markov Switching VAR model indicates that the relationship between the FX market and the stock market is regime-dependent. Changes in the stock market have a significant impact on the FX market during both normal (calm) and crisis (turbulent) periods. However, the reverse effect is weak or nonexistent. The stock-oriented model is the prevalent model for Sub-Saharan African (SSA) countries. Irrespective of the regime, there is no relationship between the stock market and the FX market in Cote d’Ivoire. Our results are robust in model selection and degree of comovement.

A Markov-Switching model for building occupant activity estimation

2019 ◽  
Vol 183 ◽  
pp. 672-683 ◽  
Author(s):  
Sebastian Wolf ◽  
Jan Kloppenborg Møller ◽  
Magnus Alexander Bitsch ◽  
John Krogstie ◽  
Henrik Madsen
Keyword(s):  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Activity Estimation
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