# Log vs ln in stata forex

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Moreover, Walid, Chaker, Masood, and Fry employed an MS model and investigated the dynamic linkage between stock price volatility and exchange rate changes. It should be noted that x t or z t may also contain lags of the dependent variable. In this specification, the state s t is not observed, and it follows a Markov chain process. All elements of P are nonnegative and each column sums to 1.

To handle these complications we estimate functions of p ji and by normalizing by p ki. In particular, we estimate q ji in:. The marginal density of y t is obtained by weighting the conditional densities by their respective probabilities. This is written as follows:. In the next section, we apply the aforementioned methodology and derive the maximum Likelihood estimates empirically.

Spectral causality Breitung and Calderon, is very useful if causal links between variables change according to frequency Tastan, Granger , Geweke , Hosoya , and Breitung and Candelon developed a Granger causality test in the frequency domain. Applying Fourier transformation of the moving average polynomial terms, we rewrite the spectral density of x t as:. The null hypothesis is the following:. Breitung and Candelon wrote the equation for x t in the VAR p system:.

The null hypothesis is equivalent to:. Due to the fact that there are linear restrictions, the usual Wald statistic can be used. The Wald statistic is the following:. To prevent indirect causality, we can extend the framework to the case of additional variables. In that case, the frequency test is computed on these variables Tastan, A way of conditioning is to include lagged values of additional variables in the regression test Geweke, For simplicity, assume that there is only one additional variable, z t.

We can then apply the testing procedure on the parameters of lagged y t. Based on Hosoya , conditional Granger causality can be tested in the following model:. Breitung and Candelon , mention in Hosoya's approach, that the variable w t carries the contemporaneous information in z t , which may not fit well with Granger causality.

Furthermore, ignoring the contemporaneous information in z t , as in Geweke's approach, may also potentially lead to spurious causality. The variables in the system are assumed to be I 0 and can be represented by a stationary VAR.

If the variables in the system appear to be nonstationary, then we must establish integration and cointegration properties of the data. If each variable is I 1 , then the system must be tested for cointegration, for instance using the Johansen test. Breitung and Candelon suggested that this approach can be used for the frequency domain test. The coefficients on the additional lagged variables are not included in the computation of the Wald statistic.

Then, we fit a VAR p model conducting the Granger causality test using p lags. The exchange rate data used in our analysis were the exchange rates of the foreign country and the USA dollar. These were transformed into realized volatility of daily frequency, as follows:.

Before proceeding, we examine the stochastic properties of our time series by testing against the existence of unit roots, using the Phillips—Perron unit root test. We rejected the null hypothesis of a unit root for all the time series, 1 so our data are considered to be integrated of zero degree, that is, I 0. We then turn to the MS estimation results, regarding the effect of all the causal variables in each period on the returns of the euro exchange rate for two states.

Fitting of low volatility state on the euro to dollar returns and on the actual euro to dollar exchange rate. These different volatility states are attributed to either direct monetary policies implemented by the central banks to regulate their currency circulation or to trends and movements of key financial indices, such as stock exchange indices, commodity indices, and so on, as well as to spillover effects across currencies. Another interesting finding is that the returns on the euro exchange rate are caused by the returns of the HKD.

In other words, traders and investors try to benefit from the volatile environment by shorting large amounts in different financial markets, that is, exchange rate market, stock market, and so on Neha, Finally, another important finding of our analysis is that, for both eras, the high volatility state is modeled with fewer statistically significant variables than the respective low volatility state. It can be used for analyzing the determinants of the exchange rate, in a global as well as domestic setting.

As we know, financial and currency markets are increasingly vulnerable to the fluctuations in global and local economies in which they are exposed. Hence, the risk analyses need to take into consideration domestic as well as international economic conditions of regions that directly or even indirectly influence the institution's and the government's exposure to the exchange rate, without neglecting the crucial role of the recent pandemic.

Analytically, the proposed approach is capable of sufficiently answering one of the fundamental questions of every exchange rate model, which refers to the determinants of the exchange rate, while taking into consideration the recent pandemic. The appropriate model is crucial for policymakers, as it detects the factors that determine the exchange rate so that policy actions can be implemented in time to be effective.

For instance, in contrast to monetary policy, where it takes usually a year for interest rates to impact inflation, this relationship is less well understood. In addition, data are reported with lags and policymakers do not act immediately on developments, but observe trends for some time before changing policies Bernanke, This urges the use of models, which take the current global pandemic into consideration, as is the case with the suggested approach.

Our model could substantially aid policymakers worldwide. The validity of this argument lies in the fact that whilst tools and actual policies differ across countries and the financial institution, the key objective of macroprudential policies, which is the reduction of systemic risk, is universal e. Our model offers a solution for policymakers to the aforementioned problem.

In times of crisis that are characterized by turbulent macroeconomic and financial environments, exchange rates tend to exhibit persistent high volatilities Walid et al. This, in turn, led central banks around the globe to decrease their overall interest rates and provide stimulus packages to support the real economy. Therefore, the fundamental determinants of exchange rate regimes have substantially changed during the pandemic. Furthermore, to investigate the potential shifts in the regimes of the euro to dollar exchange rate because of the pandemic, the present paper formulated a MS model with two regimes, based on the determinants that have been found significant.

Clearly, future research on the impact of the recent pandemic on other aspects of the financial and economic activity would be of great interest. Finally, additional variables of interest could be added to the model, such as the number of successful vaccinations, and so on. Int J Fin Econ. Konstantinos N. Konstantakis , 1 , 2 Ioannis G. Melissaropoulos , 1 Theodoros Daglis , 1 and Panayotis G. Michaelides 1. Ioannis G. Panayotis G.

Author information Article notes Copyright and License information Disclaimer. Michaelides, Email: rg. Corresponding author. Email: rg. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

Crisis and exchange rates Based on the related literature, there are two main strands regarding the factors that affect the movements of predominant exchange rates in times of crisis. Exchange rate and MS modelling Probably one of the first works on exchange rates, using MS modeling, was conducted by Engel Spectral causality Spectral causality Breitung and Calderon, is very useful if causal links between variables change according to frequency Tastan, Data and variables The exchange rate data used in our analysis were the exchange rates of the foreign country and the USA dollar.

Variables Obs Mean Std. Min Max p1 p99 Skew. Realized volatility AUD 95 0 0 0 0 0 0. Open in a separate window. Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 Realized volatility AUD 1. Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 Realized volatility AUD 1. Note: t statistics in parentheses. Endnotes 1 The results of the Phillips—Perron unit root test are available upon request by the authors.

On the dynamic relation between stock prices and exchange rates. Journal of Financial Research , 19 2 , — Journal of Economics and Finance , 11 , 2,22—2, Basel III: A global regulatory framework for more resilient banks and banking systems. Basel Committee on Banking Supervision, Basel. Basher, S. Energy Economics , 54 , 11— Journal of Econometrics , 2 , — On the linkages between stock prices and exchange rates: Evidence from the banking crisis of — International Review of Financial Analysis , 33 , 87— Applied Financial Economics , 14 4 , — Evaluating exchange rate forecasts along time and frequency.

International Review of Economics and Finance , 51 , 60— Oil price shocks and U. Energy , , — Journal of Development Economics , 79 , — Exchange rates and Markov switching dynamics. Currency carry trade regimes: Beyond the Fama regression. Journal of International Money and Finance , 28 , — High interest rates and exchange rate stabilization in Korea, Malaysia, and Thailand: An empirical investigation of the traditional and revisionist views. Review of International Economics , 10 1 , 64— The impact of foreign interest rates on the economy: The role of the exchange rate regime.

Journal of International Economics , 74 2 , — Inflation targeting, asset prices, and financial imbalances: Contextualizing the debate. Journal of Financial Stability , 6 3 , — A multifactor model of exchange rates with unanticipated shocks: Measuring contagion in the East Asian currency crisis.

Journal of Emerging Market Finance , 3 3 , — Can the Markov switching model forecast exchange rates? We first obtain the coordinates for the kernel density estimate using the kdensity command:. Several options are used. These values will be used in a subsequent plot. The default plot is suppressed using nograph. A density histogram with the kernel density overlaid is obtained with the histogram command and the addplot option. We request a line that connects the points defined by the variables x on the x -axis and fx on the y -axis.

The normal options add the maximum likelihood normal density:. The Stata command mlexp is used to obtain maximum likelihood estimates for parameters of a log-likelihood function, which in this case is:. From the result of the mlexp command, we create the variables shape and scale in our dataset with the commands:. These values are used with the gammaden function to obtain the gamma PDF values for x.

We name this variable gamma :. Finally we combine the histogram, kernel density estimate, and maximum likelihood gamma density in one plot, again using the histogram command and the addplot option. Variables describing the two density functions are separated in the addplot option with a double vertical bar, :.

The solid kernel density estimate is skewed versus the dashed normal density. The column Coef. We may also use the standard error Std. Interval] to comment about the precision of the estimator.

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Log vs ln in stata forex | Michaelides 1. Turning to the second strand of the literature, a number of research articles study the impact of interest rates on exchange rates in times of crisis. Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 Realized volatility AUD 1. Of course, there is a part of the literature that focuses on forecasting the exchange rates in times of crisis. The stock market and exchange rate dynamics. |

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