Targeting Relative Inflation Forecast as Monetary Policy Framework for Adopting the Euro

This study proposes relative inflation forecast targeting as an operational framework of monetary policy for adopting the euro by the EU new Member States. This strategy assumes containing differentials between the domestic and the eurozone inflation forecasts as an operational target. A model prescribing the RIFT framework is presented along with a set of appropriate policy indicator variables and instrument rules. The proposed framework advances the strategy based on relatively strict inflation targeting that is currently pursued by some NMS. Several ARCHclass tests in various functional forms are employed for providing preliminary empirical evidence on convergence of inflation differentials relative to the euro area for Poland, Czech Republic and Hungary.http://deepblue.lib.umich.edu/bitstream/2027.42/40140/3/wp754.pd

Volatilidade do mercado de ações do Paquistão: uma comparação de modelos do tipo Garch com cinco

This study conducts empirical analyses modeling the volatility of Pakistani stock market over the period of 1st January 2008 to 30th June 2018 via different GARCH type Model; Symmetric (GARCH & GARCH-M) and Asymmetric (EGARCH & TGARCH) with five different Distribution Techniques such as Normal Distribution (Norm), Student’s t Distribution (Std.), Generalized Error Distribution (GED), Student’s t Distribution with fix the degree of freedom (Std. with fix DOF) and Generalized Error Distribution with fix parameters (GED with fix parameters). The results are shown in GARCH (1, 1) lagged conditional variance and squared disturbance which effects conditional variance is significant in all distribution. GARCH-M (1, 1) depicts a positive significant at 1% results in Std. and GED which indicates the existence of risk premium and insignificant in rest of the distribution on. EGARCH and TGARCH both are found to leverage effect significant at 1% level. In determining the accuracy and adequacy of forecasting density and choice of volatility model the results on simulated data indicates choice of conditional distribution appear as a more dominant factor. EGARCH model with Student’s t the distribution technique is delivered satisfactory results as compare to other models which censored by statistical tools of maximum Log Likelihood, minimum AIC, and SIC. The previous study of Pakistani Stock Market is limited to GARCH family models with one or two distributions. This study covers the limitations and also contributes existing literature in this regard. This research is considered important for investors, policymakers, and researchers.Este estudio realiza análisis empíricos que modelan la volatilidad del mercado de valores pakistaní durante el período del 1 de enero de 2008 al 30 de junio de 2018 a través de diferentes modelos de tipo GARCH; Simétrico (GARCH & GARCH-M) y Asymmetric (EGARCH & TGARCH) con cinco técnicas de distribución diferentes, como la distribución normal (Norm), la distribución t de Student (Std.), La distribución de errores generalizada (GED), la distribución t de Student con la corrección del grado de libertad (Std. con corrección DOF) y Distribución de errores generalizada con parámetros de corrección (GED con parámetros de corrección). Los resultados se muestran en GARCH (1, 1) varianza condicional retrasada y perturbación al cuadrado, lo que afecta a la varianza condicional es significativo en toda la distribución. GARCH-M (1, 1) muestra un resultado positivo significativo al 1% en la norma. y GED, que indica la existencia de prima de riesgo e insignificante en el resto de la distribución en. Tanto EGARCH como TGARCH tienen un efecto de apalancamiento significativo al nivel del 1%. Al determinar la precisión y la adecuación de la densidad de pronóstico y la elección del modelo de volatilidad, los resultados en datos simulados indican que la elección de la distribución condicional aparece como un factor más dominante. El modelo EGARCH con la técnica de distribución de Student se entrega con resultados satisfactorios en comparación con otros modelos que están censurados por las herramientas estadísticas de máxima probabilidad de registro, mínimo AIC y SIC. El estudio anterior de la Bolsa de Valores de Pakistán se limita a los modelos de la familia GARCH con una o dos distribuciones. Este estudio cubre las limitaciones y también aporta la literatura existente en este sentido. Esta investigación se considera importante para los inversores, los responsables políticos y los investigadores.Este estudo realiza análises empíricas modelando a volatilidade do mercado de ações paquistanês no período de 1º de janeiro de 2008 a 30 de junho de 2018 através de diferentes modelos do tipo GARCH; Simétrico (GARCH & GARCH-M) e Assimétrico (EGARCH & TGARCH) com cinco diferentes Técnicas de Distribuição, como Distribuição Normal (Norm), Distribuição t de Student (Padrão), Distribuição de Erro Generalizada (GED), Distribuição t de Student com correção do grau de liberdade (Std. com correção de DOF) e distribuição de erros generalizada com parâmetros de correção (GED com parâmetros de correção). Os resultados são apresentados na variância condicional defasada GARCH (1, 1) e na perturbação quadrada que afeta a variância condicional em todas as distribuições. GARCH-M (1, 1) representa um significante positivo com resultados de 1% em Std. e GED que indica a existência de prêmio de risco e insignificante em resto da distribuição em. EGARCH e TGARCH ambos são encontrados para alavancar o efeito significativo ao nível de 1%. Ao determinar a precisão e a adequação da densidade de previsão e a escolha do modelo de volatilidade, os resultados em dados simulados indicam que a escolha da distribuição condicional aparece como um fator mais dominante. O modelo EGARCH com Student t a técnica de distribuição apresenta resultados satisfatórios quando comparado a outros modelos que foram censurados por ferramentas estatísticas de máxima Likelihood, mínima AIC e SIC. O estudo anterior do mercado de ações paquistanês é limitado a modelos de família GARCH com uma ou duas distribuições. Este estudo cobre as limitações e também contribui com a literatura existente a esse respeito. Esta pesquisa é considerada importante para investidores, formuladores de políticas e pesquisadores

Targeting Relative Inflation Forecast as Monetary Policy Framework for Adopting the Euro

This study proposes relative inflation forecast targeting as an operational framework of monetary policy for adopting the euro by the EU new Member States. This strategy assumes containing differentials between the domestic and the eurozone inflation forecasts as an operational target. A model prescribing the RIFT framework is presented along with a set of appropriate policy indicator variables and instrument rules. The proposed framework advances the strategy based on relatively strict inflation targeting that is currently pursued by some NMS. Several ARCHclass tests in various functional forms are employed for providing preliminary empirical evidence on convergence of inflation differentials relative to the euro area for Poland, Czech Republic and Hungary.Inflation targeting; Monetary convergence; Euro adoption; EU new Member States.

Monetary Policy Rules for Convergence to the Euro

This paper aims to devise a monetary policy instrument rule that is suitable for open economies undergoing monetary convergence to a common currency area. The open-economy convergence-consistent Taylor rule is forward-looking, consistent with monetary framework based on inflation targeting, containing input variables that are relative to the corresponding variables in the common currency area. The policy rule is tested empirically for three inflation targeting countries converging to the euro, i.e. the Czech Republic, Poland and Hungary. Stability tests of the input variables affirm prudent inclusion of these variables in the suggested policy rule. Empirical tests of the proposed instrument rule point to systemic differences in monetary policies among these euro-candidates. The Czech inflation targeting is forwardlooking relying on a sensible balance between inflation and output growth objectives. Poland's policy focuses on backward-looking inflation, while the Hungarian policy on exchange rate stability. Forecasts of policy instruments based on the prescribed rule are more accurate and reliable for the Czech Republic and Hungary, but less for Poland.monetary convergence, Taylor rules, inflation targeting

Relative Inflation-Forecast as Monetary Policy Target for Convergence to the Euro

A monetary policy framework based on targeting a relative inflation-forecast is proposed for the economies converging to the euro. Such strategy aims at containing the differentials between the domestic and the implicit monetary union inflation-forecasts. Hence, these differentials become a basis for setting an operational policy target. The proposed framework can be viewed as an extension of flexible inflation targeting that prioritizes low and stable inflation over the exchange rate stability. It is believed to be consistent with the Maastricht convergence criteria and can be implemented in concurrence with the exchange rate stability benchmark for the ERM2. Several empirical tests are conducted to determine feasibility of adopting an instrument rule for the proposed policy framework in the three largest inflation-targeting candidates to the euro: the Czech Republic, Hungary and Poland. The stability tests as well as the volatility dynamics tests suggest that adoption of the relative inflation-forecast targeting framework is possible in these countries

Essays On Oil Price Volatility And Irreversible Investment

In chapter 1, we provide an extensive and systematic evaluation of the relative forecasting performance of several models for the volatility of daily spot crude oil prices. Empirical research over the past decades has uncovered significant gains in forecasting performance of Markov Switching GARCH models over GARCH models for the volatility of financial assets and crude oil futures. We find that, for spot oil price returns, non-switching models perform better in the short run, whereas switching models tend to do better at longer horizons. In chapter 2, I investigate the impact of volatility on firms\u27 irreversible investment decisions using real options theory. Cost incurred in oil drilling is considered sunk cost, thus irreversible. I collect detailed data on onshore, development oil well drilling on the North Slope of Alaska from 2003 to 2014. Volatility is modeled by constructing GARCH, EGARCH, and GJR-GARCH forecasts based on monthly real oil prices, and realized volatility from 5-minute intraday returns of oil futures prices. Using a duration model, I show that oil price volatility generally has a negative relationship with the hazard rate of drilling an oil well both when aggregating all the fields, and in individual fields

An empirical assessment of the key drivers of sovereign bond yields in South Africa: it’s not just about fundamentals

Thesis (M.Com. (Business Finance))--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic and Business Sciences, 2017The writer studies the short-run determinants of bond yield volatility in South Africa (SA) by analyzing the impact that global factors –representing global funding conditions – have on the changes to the rand denominated generic 10-year government bond yield (SAGB). This is followed by a one-period forward forecast of this volatility. The explanatory variables tested in this study are as follows: net bond purchases by foreign investors, Chicago Board Options Volatility Index (VIX), JP Morgan Emerging Market Bond Index (JP EMBI) spread, the US dollar to SA rand (USDZAR) exchange rate, the SA 5 year credit default swap (CDS) rate, the 12 month interest rate expectation/9x12 forward rate agreement (FRA), dollar spot price of gold and dollar spot price of oil. The study period ranges from January 2000 to December 2015. The GARCH modelling technique is used due to its ability to capture the volatility clustering effects observed in time series return data. The writer used the Gaussian distribution as the default model, however in order to control for the skewness and fat-tails in financial market return data, the Student-T and Generalised Error distributions are also tested to see if the non-normally distributed bond returns could be better captured by alternative parametric assumptions. The results show that all the explanatory variables, with the exception of the FRA, are statistically significant in explaining volatility in the local generic 10-year government bond.GR201

Empirical Research on Value-at-Risk Methods of Chinese Stock Indexes

The Chinese stock market has been established for more than 20 years. Although it is not as mature as the highly developed western securities markets, it has a huge influence on the global economy. It is significant to study the risks of the Chinese stock market, especially the risk of stock indexes. Affected by the economic globalization today, more and more financial derivatives and financial instruments appear which may lead to the increase of related risk so that the demand of research on the risk of the financial market is also getting higher and higher. Risk measurement is a key in risk management, and its measurement methods are constantly evolving. Value at Risk (VaR) method is one of the effective methods to measure the financial risk, which is widely used in domestic and foreign financial institutions. Compared with traditional models, it has much more accuracy and reasonability and is much easier to implement. As the two main indexes in Chinese stock market, the Shanghai Composite stock index and the Shenzhen Component index are selected as the research objectives. And the loss series of the two indexes are tested through normality test, unit root test, autocorrelation test and ARCH effect test. The outcomes of these tests indicate these loss series are skewed and stationary with the effect of ARCH. Hereby, the GARCH-type models are suitable to be used to estimate VaR. The TGARCH model and the EGARCH model under the hypothesis of Student’s t-distribution and generalized error distribution are employed for the six test periods from 2011 to 2016. And it can be concluded with backtesting that all these four models (the VaR-TGARCH-t model, the VaR-TGARCH-GED model, the VaR-EGARCH-t model and the VaR-EGARCH-GED model) are appropriate for the two indexes despite the fact several models fail the Kupiec test for the period 2015-2016.For the Shenzhen Component index, the VaR-TGARCH-t model may fit it most because all numbers of violations for the six test periods fall in the confidence intervals

Stochastic volatility models in financial econometrics: an application to South Africa

Thesis (Ph.D.)--University of the Witwatersrand, Faculty of Commerce, Law & Management, School of Economic and Business Sciences, 2015.The dissertation carries out a study to understand asset price behaviour in South Africa. This is investigated through the application of stochastic volatility models to trace the characteristics of high frequency financial data; daily temperature, exchange rates, interest rates, stock and house prices. Innovation in the derivatives market has seen the introduction of weather derivatives as a risk mitigation tool against adverse weather movements. Chapter Two applies three different time series models of temperature to estimate payoffs to determine which method offers the best hedging strategy in four South African cities. Results from the study suggest that the seasonality GARCH method of estimating payoffs for temperature based weather derivatives offers superior performance compared to the Cumulative Cooling Degree Days (CDD) and the historical method. This suggests that the seasonality GARCH method can be applied in these cities to hedge against adverse temperature movements. In Chapter three we consider the estimation methodology for jump diffusion models and GARCH models. Chapter four investigates volatility on exchange rate data. Use is made of the british pound/south african rand, euro/south african rand and u.s dollar/ south african rand exchange rates. The research introduces a jump diffusion model to trace the behaviour of exchange rate data. Estimation results are able to match the summary statistics in mean, variance, skewness and kurtosis. Results from the model can also explain the volatility smile for short and medium term maturities. A fat tailed GARCH model is introduced to capture the persistence in volatility on exchange rate data. Results from this chapter have an implication for pricing currency options to offer leverage to organisations affected by exchange rate risk. Chapter five extends the analysis to study the behaviour of short term interest rates, making use of the 90 Day Treasury bill (T-Bill) rate. The chapter considers a variant application of the Chan et al. (1992) model for short term interest rates wherein a jump diffusion model is introduced. The results match the summary statistics equivalent suggesting the capability of the model specification. Splitting the estimation period suggests that the jump size is highest post inflation target though with a smaller intensity. However, the 90 Day T-Bill shows higher volatility after inflation targeting though with a lesser intensity. These findings have a bearing on valuation of short term interest derivatives and also investigating multi factor models of interest rates. In chapter six four vi sectors (banking, mining, media and leisure) are considered to explore movements in stock prices. A jump diffusion model is applied to get estimation results. The results confirm related studies that stock prices have incidents of volatility which can be captured by a jump diffusion model. The results also shed light on the importance of portfolio diversification considering the different results across the sectors investigated. The implication also lies in understanding market efficiency. Chapter seven applies the jump diffusion model on house prices to understand more on the drivers of volatility on house prices. The interesting results on this chapter can be summarised as follows; the four different house segments have almost similar jump sizes though the small house price segment has highest intensity. This can point to expectations and volatility from participants in this segment at a higher level than for other segments over different regimes over the study period. The estimated higher moments were not normalised as had happened for the three previous chapters after introducing the jump diffusion model. Results from this chapter have an application to valuing mortgage premium across different house price segments. It is recommended that rigorous research on asset prices using various approaches be considered as it goes a long way in informing policy makers and investors to mitigate risk in an environment of volatile asset prices. With the growing interest in weather derivatives world-wide, there is a need to educate farmers, government entities, potential counter-parties and other organisations affected by weather related risk on the importance of weather derivatives so that a foundation is laid for trading in this special type of insurance

Stochastic volatility models in financial econometrics: an application to South Africa

Thesis (Ph.D.)--University of the Witwatersrand, Faculty of Commerce, Law & Management, School of Economic and Business Sciences, 2015.The dissertation carries out a study to understand asset price behaviour in South Africa. This is investigated through the application of stochastic volatility models to trace the characteristics of high frequency financial data; daily temperature, exchange rates, interest rates, stock and house prices. Innovation in the derivatives market has seen the introduction of weather derivatives as a risk mitigation tool against adverse weather movements. Chapter Two applies three different time series models of temperature to estimate payoffs to determine which method offers the best hedging strategy in four South African cities. Results from the study suggest that the seasonality GARCH method of estimating payoffs for temperature based weather derivatives offers superior performance compared to the Cumulative Cooling Degree Days (CDD) and the historical method. This suggests that the seasonality GARCH method can be applied in these cities to hedge against adverse temperature movements. In Chapter three we consider the estimation methodology for jump diffusion models and GARCH models. Chapter four investigates volatility on exchange rate data. Use is made of the british pound/south african rand, euro/south african rand and u.s dollar/ south african rand exchange rates. The research introduces a jump diffusion model to trace the behaviour of exchange rate data. Estimation results are able to match the summary statistics in mean, variance, skewness and kurtosis. Results from the model can also explain the volatility smile for short and medium term maturities. A fat tailed GARCH model is introduced to capture the persistence in volatility on exchange rate data. Results from this chapter have an implication for pricing currency options to offer leverage to organisations affected by exchange rate risk. Chapter five extends the analysis to study the behaviour of short term interest rates, making use of the 90 Day Treasury bill (T-Bill) rate. The chapter considers a variant application of the Chan et al. (1992) model for short term interest rates wherein a jump diffusion model is introduced. The results match the summary statistics equivalent suggesting the capability of the model specification. Splitting the estimation period suggests that the jump size is highest post inflation target though with a smaller intensity. However, the 90 Day T-Bill shows higher volatility after inflation targeting though with a lesser intensity. These findings have a bearing on valuation of short term interest derivatives and also investigating multi factor models of interest rates. In chapter six four vi sectors (banking, mining, media and leisure) are considered to explore movements in stock prices. A jump diffusion model is applied to get estimation results. The results confirm related studies that stock prices have incidents of volatility which can be captured by a jump diffusion model. The results also shed light on the importance of portfolio diversification considering the different results across the sectors investigated. The implication also lies in understanding market efficiency. Chapter seven applies the jump diffusion model on house prices to understand more on the drivers of volatility on house prices. The interesting results on this chapter can be summarised as follows; the four different house segments have almost similar jump sizes though the small house price segment has highest intensity. This can point to expectations and volatility from participants in this segment at a higher level than for other segments over different regimes over the study period. The estimated higher moments were not normalised as had happened for the three previous chapters after introducing the jump diffusion model. Results from this chapter have an application to valuing mortgage premium across different house price segments. It is recommended that rigorous research on asset prices using various approaches be considered as it goes a long way in informing policy makers and investors to mitigate risk in an environment of volatile asset prices. With the growing interest in weather derivatives world-wide, there is a need to educate farmers, government entities, potential counter-parties and other organisations affected by weather related risk on the importance of weather derivatives so that a foundation is laid for trading in this special type of insurance