Forex indicators 2012 dodge

forex indicators 2012 dodge

best indicators for forex trading. Hello, just fell in love with this indicator, is it possible to convert it to mt5? Thank you. Attached File. File Type: mq4 forex sweepmod. tocic.xyz › document › forex-indicator. INVESTING TREASURY BONDS You can also Slacker music downloader task manager, file transfer, power control. Ihre Hauptaufgabe ist 28 SW: k. Network Diagrams are download-sw section of in memory blocks want to control.

As you can imagine, this leads to forming bad habits such as letting fear and greed infect your trading. It causes you to let losers run, hoping the trade will turn around; it causes you to cut your profitable trades early, out of fear that you will lose a small amount of hard-earned profit. Enter the Matador. The bullfighter.

He has a short window of opportunity during which he can avoid the charging animal. This is your trade. The markets are the bull, and you are the bullfighter. Exit your good trades too early, and your losing trades will catch up with you. Exit too late, and you miss your opportunity to profit as the market starts to move against you.

Chaotic, indifferent, relentless: a raging bull. Accept that your analysis might be wrong, that technical levels will be broken, that your standard indicators used in standard ways will lie to you. Accept chaos, and instead only try to get out of the way of the bull. In other words, work on your exit. But how? Open up a demo account, pick your favorite instrument, and flip a coin : heads means go long, tails means go short.

Exit whenever you want. By losing control over which side of the trade you take , you force your brain to look at the markets as they look at you: objectively and without bias. Without fear or greed. Your brain essentially becomes a highly advanced pattern recognizer, and the longer you do this exercise, the better you get at seeing the way prices move. You will learn how much effective leverage is too much, and what lot sizes are acceptable for the account size.

You will learn which time frames respond in which ways, and which trading styles work best with your personality. When you get good — in other words, when you come emerge with a profit after flipping the coin 30 times — you can take this to the next level. Try it on a live account with the smallest lot size.

You will have the confidence not just to play around, but to risk a few dollars on every bet you make. And then? Use your closed profits to continue increasing your lot size. Think about it — in just a few weeks you could become a profitable live trader , and all you need is a nickel.

DailyFX provides forex news and technical analysis on the trends that influence the global currency markets. Leveraged trading in foreign currency or off-exchange products on margin carries significant risk and may not be suitable for all investors. We advise you to carefully consider whether trading is appropriate for you based on your personal circumstances. Forex trading involves risk. Losses can exceed deposits.

We recommend that you seek independent advice and ensure you fully understand the risks involved before trading. In , the Medici family brought the first foreign-based banking institution to the world but the first record of foreign exchange is dated back to In , the Kingdom of England introduced the gold standard, which reigned over the currency world until the Great Depression.

This was the first step towards a path of convoluted international monetary relations. The subsequent currency troubles even led Keynes to emphasize the importance of rule- based regimes to stabilize business expectations. Ensued the post-World War II Bretton Woods Accord using a soft pegged rate currency system from its establishment in to its bitter end in The latest development is the introduction of so-called cryptocurrencies such as Bitcoin and Ethereum.

Such instruments are however still at their very earliest stage of potential development, leaving the world to be heavily dominated by classic free-floating currencies. Despite being unavoidable, free-floating currencies have been the gold standard for no more than 47 years. It would be presumptuous to assume having discovered all that may be discovered about such an intricate and ever-changing world-scale system in such a short period of time despite the thoroughness of the research performed in the past.

While the existence of pre-existing knowledge cannot be denied, free-floating currencies and considerable contemporary changes, such as the internationalization of financial markets, the end of the cold war or the creation of the Eurozone, opened the door for a brave new world of finance and economics.

This paper explores the viability of the Markov Switching MS model as alternative currency trading strategy to the carry trade. This paper is therefore organized as follows. Section 3 considers the input Section 4 formalizes the model specifications. Section 5 presents the model evaluation and results while Section 6 concludes.

The current state of the literature will now be evaluated to show the scientific motivation and the relevance of this work. The Mundell-Fleming model Fleming, ; Mundell, quickly fell out of favors at the dawn of our contemporary free-floating exchange rate world. The literature split between monetary and portfolio balance models. Both models focus on stocks rather than order flow. The latter differs by assuming perfect capital mobility without perfect capital substitutability, which leads to the existence of risk premium Frankel, As the latter is found to provide better exchange rate predictions than economic models do, it became the prevailing benchmark.

For further information on traditional macroeconomic models, Chinn provides an in-depth overview. In order to overcome this puzzle, several directions have been taken by the literature, of which seven are discussed: Taylor rule, financial globalization, risk premium, market microstructure, panel data, cointegration, and last but not least Markov regime switching.

A related development that deserves to be mentioned lies in stochastic volatility and autoregressive conditional heteroskedasticity ARCH modelling. Both techniques aim at predicting standard deviation rather than the mean, at which they boast very significant success.

Volatility digression apart, traditional macroeconomic models are built upon current economic fundamentals. Engel, Mark, and West emphasize that those fundamentals carry less weight on exchange rate determination than the expectations of their future value. Traditional models neglected the endogeneity of monetary policy, yet modelling monetary policy is critical to integrate those expectations.

Both Taylor rule and financial globalization provide us with tools to better comprehend FX price formation. The existence of an exchange rate-specific risk premia and market microstructure might achieve this purpose. Currency managers conspicuously acknowledge the existence of risk premia in their strategies.

While risk premia do not contradict the efficient market hypothesis Fama, , , market microstructure does. In a related fashion, panel data enables a model to incorporate information linked to the functioning of financial markets. The pace of information diffusion in a particular market depends on its microstructure, which is specific to each and every market.

For instance, the premise that option and spot markets incorporate information to a different extend and speed is widespread among practitioners. Peterson and Tucker confirm this assumption in currency markets by showing a statistically significant relationship between the proportional deviation of option-implied and observable spot rates, and subsequent currency returns after controlling for interest rate differentials.

One may postulate that information typically spreads more quickly in liquid than in illiquid markets. Information may indeed be inferred from the co-movements of each individual market with each other. Despite providing promising results, none of those models considers the structure of the time series data itself. In , Granger and Newbold warn about spurious findings of standard regression models on nonstationary data.

Nelson and Plosser highlight the existence of unit root processes in many macroeconomic time series. Motivated by the goal to dodge spurious regression results Zietz, , Engle and Granger instigated the use of error correction models ECM based on cointegration as defined by Granger Although cointegration takes a deeper look at the data structure, an assumption of linearity remains present.

The introduction by Hamilton of his MS model in constituted an absolute breakthrough for non-linear models in finance. Engel and Hamilton show the presence of long swings, i. Several authors in the literature confirm the appropriateness of regimes to describe exchange rates such as Bergman and Hansson or Engel and Kim The regime dependence finds several explanations in the literature.

Notwithstanding these advancements in the literature, the Meese-Rogoff puzzle remains the standard in the macroeconomics literature, especially in the short run. Goodhart explains this apparent puzzle by the substantial extraneousness of macroeconomics-based exchange rate prediction modelling to the actual currency trading process. The well-established success of both short-term and long-term currency trading strategies such as technical analysis and carry trade seems to support this view.

Technical analysis attempt to predict short-term asset prices by relies on their past asset values and trading volume data. Such strategies typically rely support and resistance levels, which are price levels believed to bound asset price movements with high probability. While one may consider the success of these strategies as a proof that the Meese-Rogoff puzzle does not hold, Melvin, Prins, and Shand suggest its irrelevance for currency investing.

Beating the random walk indeed does not constitute a useful metric for investors. Burnside, Eichenbaum, Kleshchelski, and Rebelo hypothesize the origin of carry trade returns to stem from a large tail risk premia since their correlation with orthodox risk factors does not explain them. The typical example is the drop of c. While fitting carry trades, the risk premia explanation however might not be applicable to technical trading. The success of technical trading is indeed not universal across asset classes.

This aims the discussion towards the specific market microstructure of currency markets, which heavily differs from other classes. Market microstructure does explain the profitability of both technical analysis and carry-trade by the combination of self-fulfilling phenomena and specific features of the currency markets Osler, Similar fragility can be inferred for technical analysis strategies as well. While nothing prohibits self-fulfilling and self- reinforcing dynamics to constitute equilibria Osler, , their longevity as prevalent equilibria relies on thin ice.

The U. I believe FX trading should therefore not rely on strategies whose success relies on self-fulfilling prophecies. Those approaches do provide significant tail risk management but entail a significant setback. This hindrance lies in the cost of maintaining a downsize hedge. From June to September , the former provided returns of c. This performance is almost three times larger than the one of its tail risk-hedged counterpart.

The skew implies the higher cost of out-of-the-money OTM puts over OTM calls; the kurtosis, the higher cost of OTM options over at-the-money ATM options; and the typically upward-sloping term structure, the higher cost of long-dated options over their short-dated alternatives. Rolling long OTM puts therefore typically incurs a significant roll cost. The negative correlation between cost of hedging and market sentiment bolsters this reality. It is especially true for short-dated options, which will tend to whip around much more than their long-dated counterparts in reaction to market news or changes in market sentiment.

The hedging cost hence might be reduced through the use of short-dated options. This evaluation of the current literature shows a potential lack of sufficient viable alternatives. The purpose of this paper is to investigate this dearth. The first hurdle in doing so lies in identifying the most relevant starting point among recent developments in the literature.

The most thorough approach consists in analyzing the empirical statistical properties of currency markets. A sensible currency trading strategy must hence handle such characteristics. For this purpose, the above-mentioned directions taken by the literature are examined. Despite their success and widespread adoption by academics and practitioners alike, the first two methods unfortunately not only focus on volatility but also are quite sensitive to structural breaks.

Markov switching on the other hand is designed with the very purpose to handle them. By enabling the existence of multiple Markov states exhibiting distinct distributional properties, MS accommodates time-dependent variance and skewness as well. Being non-linear, MS boasts promising characteristics for short-term exchange rate forecasting.

Kritzman, Page, and Turkington explain that the MS literature currently neglects out-of-sample prediction in favor of in-sample fitting. They also show the performance improvement provided by MS modelling in asset allocation. This corroborates the conjecture made by Clarke and de Silva that investors relying on regime-specific expected return and risk enjoy more opportunities that those who do not.

This paper hence investigated the predictive ability of a MS model in the context of currency trading. Being a strong believer of ensemble learning, it is attempted to integrate other paths taken by the recent literature in exchange rate determination to augment the proposed Markov switching model.

Another key characteristic of the model however needs to be discussed before this incorporation is. The choice of data frequency is indeed absolutely central to the results of predictive models. The literature heavily investigates such models with a monthly or quarterly frequency to fit the frequency at which various macroeconomic variables get released. Rossi on the other hand explains that the focus of economists on monthly and quarterly frequencies diverge from the one of financiers who favor higher frequencies.

Cheung and Erlandsson support this view by conjecturing that higher sampling frequency provides better information on dynamic properties of exchange rate time series data. Higher frequencies are therefore preferred for the purpose of this paper. They may be classified as weekly, daily, or even higher frequency due to their respective display of distinctive characteristics of the market.

Daily data is prey to weekly patterns such as the Monday effect Cross, , which highlights that stock returns consistently perform better on Fridays than on Mondays due to different price patterns during both days. While both effects relate to stock trading, the occurrence of such periodic effects in currency markets can be presumed. The choice between these three frequencies calls upon analyzing the level of automation involved in the employed trading strategy.

The strategy can rely on discretion, algorithmic trading, or algorithmic decision-making support. Discretionary trading was considered a safe and sound practice a few decades ago. Fully ignoring human trading abilities by relying on algotrading however also constitutes a mistake to avoid. They explain that since human beings are built for swift pattern recognition, traders exhibit high interoception levels detect valuable physiological trading signals.

While they argue that such trait would enable traders to beat algorithmic trading, some doubts still arise on this matter. This paper is indeed based on the premise that because algorithms cannot possibly grasp such physiological signals, human traders who rely on algorithmic support as well as their own predictive abilities constitute a better combination.

This paper therefore does not enter the so-called high frequency trading world. Human traders are put at a strong disadvantage in this algotrading-ruled realm since they cannot possibly compete with the decision-making speed of those algorithms. The remaining choice is limited to daily and weekly frequencies, from which this paper picks the former. Financial crises lead to daily changes in correlations and currency tail events such as the ones leading to carry trade unwinds occur over a few days.

For best adaptive power, the Markov switching model should logically rely on daily rather than weekly data. The latter risks providing lagged information whose predictive power has vanished in the same spirit as monthly and quarterly frequencies may do. This paper however incorporates the underlying ideas of those models, though to a limited extent. The model should therefore gain some of the predictive power associated with Taylor fundamentals. Market microstructure, by definition, does not suffer from this frequency mismatch setback as it may even be collected tick by tick.

Yet, the literature scarcely incorporates it into its models. The accessibility of order flow data represents a challenge for the academic due to its proprietary nature. However it does not for the financier. The use of market microstructure in this paper attempts to further the scant literature while remaining practically implementable for the man on the Street.

Risk premia is available at a daily frequency as well through its implementation made by various providers. The benefits of both market microstructure and risk premia are fed to the algorithm. A major drawback of panel data-based models lies in the associated computational cost and complexity.

The order of each element within this classification correlates with the associated model complexity. Relying on multiple equations, nonlinearity, cointegration and time-variation in the parameters, the model developed in this paper unfortunately exhibits high complexity. Model complexity is unfortunately negatively correlated to explainability. Finance professionals typically tend towards avoiding black-boxes for risk management purposes.

One may indeed try to avoid flash crashes or loss of control over algorithms, which can result in tremendous losses. Knight Capital lost USD mn within 30 minutes when one of its trading algorithms went rogue Philips, To avoid such superfluous complexity, the use of panel data is foregone. Yet the related notion of breath comes into play. Grinold and Kahn perfectly illustrate this concept.

The higher the number of currency pairs forecasted, the higher the chances of success of a currency portfolio. As mentioned, the model hereby developed relies on cointegration analysis. It does so at the regime level due to the existence of unit roots in economic and financial time series. The results from regression made on non-cointegrated are unreliable. The last element included in the model is the presence of ARCH effects. Those effects are highly representative of the behavior of financial data.

The potential for MS models to provide a viable alternative to the carry trade and other standard currency investing strategies is investigated empirically through the development of a statistical trading model. This model is scripted in the Python programming language using the Spyder development environment available in the Anaconda distribution.

The script utilizes the following so-called packages: os, sys, datetime, numpy, scipy, pandas, matplotlib, statsmodels, and arch. Several limits hinder this investigation. The data sample can be considered relatively short and do not include full-blown crisis events. The sample might not be fully representative of the true population.

The MS model might therefore not thrive as much as it could. The paper introduces MS to the currency trading realm and therefore needs to remain relatively broad. This characteristic might hamper the performance of the model due to a complete lack of optimization. The exchange rates used in this paper are fixing prices, which might not realistically represent the prices at which this strategy might get traded.

This paper however does not aim to investigate linear regression in details. Further information on linear regression and the OLS method can be found in any standard statistics or econometrics textbook. Modelling will be performed on time series data, which represents the core of empirical economics and financial data.

Two types of issues could hinder this process. Missing values and measurement errors will not be discussed here. Market data available on Bloomberg and provided by Citi is indeed assumed to be free from such predicaments. The likelihood of multicollinearity in market time series is far from negligible though. Empirical data could on the other hand violate common assumptions of time series models. Those violations are the absence of stationarity, the existence of structural breaks as well as ARCH.

Either one of those violations can lead a regression to be spurious. Multicollinearity may be either perfect of imperfect. In its perfect form, at least one linear relationship exists among the regressors. The full rank assumption is therefore violated. The existence of perfect multicollinearity remains singular yet the imperfect form of multicollinearity is frequent. In this case, at least two regressors are highly but not perfectly correlated, which leads the full rank assumption to be theoretically met but to also at the verge of failure in severe cases.

While multicollinearity is always present to some extent in empirical data, statistical issues arise from severe multicollinearity. The benefits of shrinkage methods like Ridge regression are based on the bias-variance trade-off: regularization decreases variance at the cost of increasing bias.

While decreasing variance reduces multicollinearity, one may not wish information regarding variance to be lost in the process nor to use biased estimators. Expanding the sample size could be performed through an increase in frequency or in timeframe. The frequency of market data matters tremendously since market microstructure is linked to this frequency. Changing the frequency means analyzing a distinct underlying microstructure of the observed data.

Such change is therefore not an option. Increasing the length of the timeframe leads to slower adaptation to changes in the data structure. This is considered undesirable in our model as discussed later. The presence of multicollinearity will therefore be flagged through the use of variance inflation factors VIF. Before the calculation process of VIFs is described, the following definitions shall be given. Multicollinearity exists by definition only in a multiple regression setting.

Once calculated, if the VIF of at least one independent variable is superior to a critical value, the variable with the highest VIF will be dropped from the investigated model. The only outstanding question is: how high is too high? This remains an on-going debate. Keeping this in mind, a choice is made in this paper regarding a fixed threshold level from which multicollinearity will be considered to have reached an excessive level.

As discussed later, the aim is not to develop an algorithmic trading system nevertheless automation remains critical however. The automatic nature required of the model means the practitioner should not need to have an extended look at the data fed into the algorithm. The model could adapt such threshold based on the data fed to it.

This solution is unfortunately not advisable for two motives. As highlighted later in this work, financiers favor explainability over accuracy. Hyperparameter optimization raises the chance of overfitting as well Johnson, Authors disagree on the level at which this threshold should be set. The model should indeed focus on minimizing false positive. In the financial realm, it can easily be argued that not acting on a valid trading signal is preferable to acting on a non-valid one.

This reality makes the use of multicollinearity correction techniques extremely important, in particular in a model involving as many input variables. The potential for multicollinearity is typically positively correlated with the number of variables involved.

To summarize, multicollinearity will be flagged using VIFs and independent variables dropped until none display a VIF value superior to the critical value of Greene, , p. The lack of stationarity in economic and financial time series was first made by Granger and Newbold who highlighted that researchers did not pay enough attention to high serial correlation in the residuals of conventional regression models. They did so by showing how such regression of a random walk on another random walk yields the result of a significant relationship.

Phillips obtains even more aggravating results. The absence of stationarity in time series may have several sources and five typical solutions exist: data transformations, de-trending, de-seasonalizing, de-cycling, and differencing. Differencing hence constitutes the most promising field of inquiry. Cointegration analysis relies on this technique.

As mentioned, the OLS regression technique fails in the presence of nonstationary time series. The results from this regression might however remain reliable in the presence of cointegration. While Baillie and Bollerslev found evidence of cointegration, Septhon and Larsen found those results to be fragile at best and Diebold, Gardeazabal, and Yilmaz, K.

Those contradictory findings may be explained by the exclusion of drift by Baillie and Bollerslev but not by Diebold, Gardeazabal, and Yilmaz, K. The latter highlights that the exclusion of drift should occur only if evidence of its absence exists. In this paper, a constant is employed as it appears to be the most conservative approach. For further information on fractional integration, Baillie provides quite an in-depth overview. This model captures long memory behaviors such as the slow decay of the effects of a shock to exchange rates.

While short time horizons are free from long memory behaviors, cointegration techniques were designed for the purpose of long-term analysis. One may therefore wonder about their relevance over shorter time spans. While Zhou confirms the greater predictive power gains stemming from sample size than from data frequency, she points out that most of the power loss coming from short time spans can be compensated by an increase in data frequency. The use of daily data over a one-year time span provides observations.

Obtaining as many observations using quarterly data would require 63 years of data, which represents a timeframe meaningfully longer than the one used in most of the cointegration literature. The reliance of this paper on cointegration techniques hence seems fairly reasonable. Despite this variety, the Engle-Granger and Johansen procedures remain the most commonly used by practitioners.

The variety of cointegration testing methods can be explained by the fact that not all cointegration tests are created equal. To be precise, comparative analyses found no cointegration test to provide better performance across the board and their performance to be conditional on several parameters Gregory, ; Haug, ; Toda, ; Zhou, ; Zietz, Being multivariate, the model hereby proposed should logically implement a system-based approach.

One may wonder whether a choice between procedures is necessary. The difference in performance of various cointegration procedures for the very same analysis indeed implies a potential to produce mixed signals: one procedure signals the presence of cointegration while another one does not. This drives this paper to rely on two cointegration procedures instead of only one. Since contradictory signals are the strongest between residual-based and system-based techniques, the use of both should provide the most information possible.

The model hence relies on the major technique of each type: the Johansen and the Engle-Granger procedures. This structure needs to be informed by common cointegration drawbacks. Zietz points out three major practical issues of cointegration techniques: simultaneous equations bias, omitted variables, and structural changes.

This paper does not tackle the issue of simultaneous equations bias. Despite, the success of cointegration analysis depends on the inclusion and exclusion of variables to a critical extent Melnick, Theoretical and practical domain knowledge therefore constitutes a prerequisite Zietz, This paper provides an extensive motivation for the selected input data later on.

The remaining issue is the existence of structural breaks in economic and financial time series. This issue motivates the structure of the cointegration analysis taken in this paper. Both tests can either be performed simultaneously or successively. Simultaneous application may sound like the most natural application but this setting might actually combine the drawbacks of both tests. The sequential approach enables the initial test to inform the later one.

The positioning of each method in the sequence relies on their level of resilience to structural breaks. The Engle-Granger procedure relies on unit root hypothesis testing. Cavaliere logically deduces the lack of robustness of the Engle-Granger procedure in the presence of MS behaviors. The Johansen procedure on the other hand is particularly robust thanks to its different structure.

Its power should therefore not be significantly diminished in the presence of structural breaks. It follows that the Johansen test is performed first. The Engle-Granger test then follows to confirm the presence of cointegration in the relationships flagged by the Johansen procedure. This paper only discusses various specificities and specifications of both models of particular relevance.

The vector-based representation of the multivariate Johansen procedure indeed enables the inference of further information than the univariate Engle-Granger method. Zhou also emphasizes the existence of much stronger size distortion effects in the Johansen procedure than in the Engle-Granger technique for higher lag models. Sephton and Larsen corroborate this sensitivity. The Johansen procedure is hence perfectly fitted to be the initial test as it can inform the model more than the Engle-Granger procedure would.

This position is also consistent with its need for more data points. The initial is indeed performed over a significantly longer time horizon than the second procedure as later explained. The Engle-Granger is not without its benefits though. It noticeably identifies type I errors with higher certainty Zietz, Avoiding spurious relationships is absolutely essential for the model hereby developed.

Positioning the Engle-Granger method as second test enables to provide higher certainty of avoiding this potentially very costly type of error. Besides their specificities, the performance of each procedure relies on their specifications. Both procedures incorporate a constant term but no trend term.

The presence of a constant term cannot be ruled out a priori but the presence of a trend term is. Financial time series typically do not exhibit trends. As discussed, the Engle-Granger method performs a unit root according to a methodology that needs to be specified.

The cointegration test performed in the Johansen procedure may be performed with either trace or eigenvalue. This choice modifies the null hypothesis and leads to slight variations in inferences. No technique is shown to overperform the other though.

This paper arbitrarily selects the trace specification. Both tests imply comparing inferences to the relevant critical values. The Engle-Granger procedure relies on the critical values proposed by MacKinnon They benefit from being more thorough than their older counterparts thanks to the increase in computational power available for their modelling.

They are also adjusted for the number of regressors in multivariate settings, which was initially not considered in this procedure. Gregory, Haug, and Lomuto highlight the array of choices available for lag length in cointegration techniques. Another notable technique is the one employed by Johansen and Juselius , which consists in selecting the lag length eliminating autocorrelation. Being out of the scope of this paper, the theoretical differences between both criteria are not discussed.

As explained further, this paper aims at robustness through the reduction of model complexity whenever possible. The selection of lag length hence relies on the BIC. For consistency purposes, the model should apply the same lag length in both cointegration tests. Model consistency and robustness also dictates the use of an identical lag length across cointegration tests and time. The BIC hence is applied to the Johansen procedure due to its higher sensitivity to lag length specification, and the selected lag is used in the Engle-Granger procedure as well.

Performing lag length selection at each iteration of the algorithm puts the model at risk of significant overfitting. The existence of a unique lag length across time is favored for this motive. BIC designates the optimal lag length to be three. The origin of those breaks needs to be investigated. Mandelbrot , and Taleb highlight that the normality assumption in economic and financial time series understate the actual predictive ability and risk.

Those deviations may be explained by DGP following a non-normal distribution such as a Cauchy-type one. The existence of multiple normally distributed regimes alternating at different points in time constitutes a perfect alternative explanation for such behavior. The structural change model unfortunately merely allows sporadic and exogenous changes. It also aims at very long-term analysis, which makes it inadequate to the purpose of this paper.

Despite higher suitability, the random switching model suffers two major hindrances. Individual observations cannot be identified with a regime in particular as the model only determines the probability that the sample was governed by each regime. Besides, switching events are assumed to be independent of prior regimes, which seems rather far-fetched from a practical viewpoint.

Markov switching is also superior to arbitrary thresholds between regimes, which fail to capture both regime persistence and shifting variance Kritzman, Page, and Turkington, Laverty, Miket, and Kelly provide a straightforward overview of Markov switching. The Markov chain is defined by these two 7? Each distribution is characteristic of the behavior of the time series in a particular state, such as a recession of a bull market.

By switching between several dynamic models, this model has the ability to capture convoluted and dynamic patterns. One shall note that the probabilities in the transition matrix may either be constant or time-varying. The former depends exclusively on the prior regime while the latter allows for the influence of exogenous variables. Constant transition probabilities are practically as implausible as random switching.

This paper will therefore favor time- varying transition probabilities. The relationships between cointegrated variables within a regime actually are inferred through a multivariate OLS regression. The MLE methodology is however unsuitable to infer the parameters of the Markov process itself in this paper. MLE is only optimal asymptotically so strong biases may appear in small samples. Besides, MLE frequently fails to converge and has high computational cost in regime switching models Quandt, Since this paper relies on relatively small samples and nested GARCH and MS modelling, a rules-based approach is favored over a probabilistic one.

Besides tremendously cutting the computational cost, this approach should increase the robustness of the trading model. Taleb , , who predicted the global financial crisis, advocates for the implementation of non-probabilistic decision techniques due to their robustness. They build their regimes based on forward-looking economic forecasts rather than backward-looking asset returns to avoid overfitting.

When regimes are identified by Kritzman, Page, and Turkington through financial market turbulence, inflation, and economic growth, our approach favors breaks from cointegration relationships. One may actually see both filters as two sides of the same coin. The underlying logic implies searching for robust relationships between exchange rate and various economic or financial variables as well as for market stress, which would disrupt such relationships.

The choice for a filter based on cointegration is motivated by the behavior of G10 currencies during the global financial crisis. The global financial crisis however constituted a structural break in those cointegrated relationships, which led such carry trades to heavily crash. The same logic applies across the board of exchange rate drivers.

This failure of cointegration analysis drives the application of a daily filter for structural breaks in relationships between exchange rates and their cointegrated drivers. One may explain this logic through alpha- and beta-generation mechanisms. Beta may be understood as transforming an exposure to various risks into an exposure to volatility risk while alpha builds on superior information.

Beta provides returns through time and alpha through timing. The latter stems from the event-driven characteristic of information. Predicting an exchange rate based on its relationships with factors constitutes a fairly beta-seeking investment strategy. The actualization of cointegration relationships may be seen as information-seeking but is actually about optimizing the beta-seeking property of the strategy. Structural breaks on the other hand are events.

Information on their occurrence hence contains potential for alpha-generation. The Markov switching model hereby developed intends to benefit from both beta and alpha. Beta collection occurs through the regimes, which rely on the existence of cointegration relationships; alpha generation arises by timing structural breaks and through this information determining the most relevant regime. These procedures require the assumption of a specific number of regimes.

Bazdresch and Werner find evidence of two regimes in Mexican peso, one with insignificant trend and low volatility and one with depreciation trend and high volatility. Boinet, Napolitano, and Spagnolo obtain similar findings for the Argentine peso with a much stronger trend in its devaluation regime and validated the number of states using the Hansen method Hansen, This model relies on the assumption of the existence of two regimes.

The first regime is characterized by stability. Under this regime, the levels of variables are expected to vary consistently to each other. The second regime on the other hand is an unstable state. Under this hypothesis, the consistency of levels across variables break down as stress rises in the markets. The regime hence is characterized by a relative consistency of changes across variables.

Despite its success, MS modelling may not fully capture changes in variance in financial time series Cai, This paper further investigates volatility modelling, whose major recent contributions relate to volatility clustering. This fact constitutes an additional cause for the choice of MS as workhorse of this paper.

Cheung and Miu however highlight that because it may be confounded for regime switching, ignoring volatility clustering increases the likelihood to falsely identify the existence of regime switching behaviors. Cheung and Erlandsson hence recommend testing for volatility clustering and if present accounting for it. Stochastic volatility and ARCH-based techniques primarily intend to model volatility clustering Cont, This choice is ascertained by findings from the fixed income world.

Naik and Lee found that regime switching better represents time-varying volatility than does stochastic volatility. Smith confirms that MS models have higher out-of- sample forecasting power than stochastic volatility models do. Smith emphasizes that either model adequately fit the data but not both concurrently since a nested MS stochastic volatility model underperforms a non-nested MS model.

Consequently, ARCH methods must be preferred to avoid nesting MS and stochastic volatility, which would lead to a loss of forecasting power. This paper adopts ARCH techniques in two manners. It aims at not only forecasting exchange rates but also at establishing the potential of MS models for trading.

In doing so, trading rules are set in places and the major one relies on volatility estimation, which is performed through a GARCH model. The most orthodox approach for parameters inference in finance is the OLS method. As a result, not only are the deficiencies of least squares corrected, but a prediction is computed for the variance of each error term.

This prediction turns out often to be of interest, particularly in applications in finance. Engle, , p. Greene highlights the typical presence of heteroskedasticity in daily financial data. Huber first proposed to rely on heteroskedasticity-consistent standard errors when fitting OLS models. More details on those techniques may be found in standard econometrics textbooks.

An extremely noteworthy development of the Eicker—Huber— White standard errors is offered in MacKinnon and White This variation benefits from being unbiased even when the underlying data is actually homoskedastic. The model offered in this paper is systematic yet aims at minimizing model complexity. The MacKinnon and White standard errors perfectly fits this aim by being unbiased when data exhibit heteroskedasticity as well as when it does not. This characteristic might indeed vary across time.

Greene however precises that while correcting standard errors improves the quality of estimators, it might still not be considered a panacea. Such corrections indeed forego the insightful information included in the ARCH effets. The desire to integrate this information led to the development of ARCH models. To dive deeper into ARCH effects, one should first fully understand the meaning of the acronym. While heteroskedasticity was explained earlier, the AR and conditional components were not mentioned.

Heteroskedasticity is said to be conditional because the volatility in one period is conditional on information available at the previous period. Moreover, this model is AR in its squared means. Engle coined the term ARCH by developing a stationary non-linear model for the dependent variable in which the conditional variance of this very dependent variable follows an AR process.

Those models are further discussed later in this paper. The following subsections successfully tackle the elements of the model sensitive to ARCH effects: the Markov switching model, the cointegration techniques, and the volatility-based trade decision rule. The pertinence of this approach goes way beyond the fixed income world. Since it typically exhibits high persistence, high-frequency exchange rate data constitutes one such prime target Cai, Neither the filter nor the regimes chosen in our model involve volatility though.

In short, this model incorporates ARCH effects into mean prediction and not only volatility prediction. Bauwens dichotomizes those extensions into direct generalizations, linear combinations, or non-linear combinations of univariate GARCH models. Those models however typically exhibit two undesirable traits. Bera and Higgins note the requirement for the conditional variance covariance matrix to be positive definitive. This constraint is typically sufficient but not necessary.

Nelson and Cao for example show that some of the variables involved may take negative values without challenging the positivity of the conditional variance. Imposing such an unnecessary constraint may lead to suboptimal results. Besides its highly complex optimization, this model also suffers from strong instability.

None however bring this complexity to a level suitable to the purpose of this paper. While ARCH effects cannot be taken into consideration in the MS model itself, their presence, can be in the cointegration analysis and the trading rule. Hamori and Tokihisa show that heteroskedasticity might, but does not necessarily, create size distortions for standard unit root tests. ARCH effects however have the potential to disturb the power of unit root tests.

Both the Johansen and the Engle-Granger procedures need to be investigated. Gonzalo nevertheless states that this method exhibits substantial robustness to deviations from standard assumptions, including the presence of ARCH effects. The Engle-Granger method should not be expected to display such robustness though. The procedure indeed relies on the OLS regression.

As previously discussed, OLS-based hypothesis testing can made robust by relying on MacKinnon and White standard errors. They remain unbiased even in the absence of heteroskedasticity as well. Since the presence of either heteroskedasticity or homoskedasticity might be expected to vary over time, rolling window-based models require robustness under both hypotheses.

The Engle- Granger cointegration method applied in this paper hence relies on such standard errors. The specification of this deviation is therefore essential to the performance of the algorithm. I select a two-fold measure. The first element involved in this measure is the exchange rate volatility. The second is a multiplier applied to this volatility. The major benefit of this multiplier is customization. The multiplier can be set based on various preferences of the investor such as his level of risk aversion or his sensitivity to trading cost.

The ability of setting multiplier levels specific to each regime even increases the customization possibilities. Since the trade decision rule relies on volatility and not on hypothesis testing, ARCH correction techniques are not applicable. The volatility modelling involved in the trade decision rule does not require a multivariate setting though.

This makes the objections previously raised in this paper mute in this particular case. Hansen and Lunde show the difficulty for volatility models to outperform GARCH 1,1 , which constitutes the most typical setting. This model specification unfortunately ignores a stylized fact of the volatility of financial time series: the leverage effect.

Volatility does not behave symmetrically. Negative shocks tend to induce higher volatility than their positive counterparts. It does so through the presence of an additional coefficient specific to negative shocks. Zivot finds the asymmetric GARCH 1,1 model with t-distributed errors to be the best fitting model specification. If the optimization algorithm gets stuck in a local maximum, the produced estimate is not optimal and may even be totally unrealistic. I decide to bind the predicted volatility within a range considered reasonable to discard such irrelevant estimates.

The size of the rolling window employed indeed induces recurrent lack of convergence in this model. Babbage, if you put into the machine wrong figures, will the right answers come out? I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question. Babbage, , p. The data input, frequency and preprocessing are therefore thoroughly investigated.

This subsection focuses on sample size, frequency and preprocessing while the following do on the choice of input data itself. Cheung and Erlandsson state that the higher the sample size and the frequency, the more information is available about exchange rates dynamics. The sample size is set from December to July This period is limited by the lack of existing data for some of the dependent variables before December I however believe that the inclusion of crises would only improve the results of this paper.

The model is built to thrive irrespective of market regimes but would gain a particular edge in volatile markets. Besides the sample size, the frequency matters as well: it needs to be selected as high as possible to obtain further information. The daily frequency however is the highest available for multiple predictor variables. The data fed to the model hence exhibits daily frequency. Financiers for instance typically use daily log returns of stock prices.

Our paper mostly does not rely on preprocessing for two motives. Some of the input data is provided normalized, which makes additional preprocessing unnecessary and potentially detrimental. Besides, since a regime relies on levels and the other one on changes, any preprocessing based on changes in the data levels cannot be implement without putting the unstable regime at risk.

Spot exchange rates are chosen because MS provides higher predictive accuracy for the former than for forward rates. This choice is driven by pragmatic considerations: the input data required for the model to be updated is more realistically available by the New York 4PM WMR fixing than the London one.

Exchange rates might however also be considered as independent variables in two ways. On the one hand, a currency pair A may be set as predictor variable of another currency pair B. On the other hand, the previous observations of a currency pair A can as well be set as predictor of the future value of that very currency pair.

Diebold, Gardeazabal, and Yilmaz strongly refute the first hypothesis. The second conjecture is not consistent with the efficient market hypothesis, which entails an absence of AR property in financial time series. The literature corroborates this statement for exchange rates. It follows that spot exchange rates are not considered suitable predictor variables in this paper. Unfortunately, inflation data such as the Consumer Price Index CPI exhibits monthly frequency and is unsuitable for this paper.

The Taylor rule however emphasizes the greater influence of expectations over current levels: real interest rates are set by monetary authorities based on the difference between actual and target inflation and output gap levels Rossi, Expectations, which benefit from a forward logic nature, can typically be inferred from market data. Subtracting the yield of Treasury inflation-protected securities TIPS to the yield of Treasuries provides such implied inflation, or breakeven inflation, with daily frequency.

TIPS do not exist for the countries issuing those currencies. German Treasuries are used as a proxy for EUR. A fundamental component of the yield curve is the term premium, i. Two key aspects of such risk are inflation and changes in demand or supply of bonds. Clark and West show the predictive power of the UIP over the short-term in macroeconomic models.

One may however measure the term structure in numerous ways such as the one taken by Nelson and Siegel I decide to adopt the approach taken by Clarida, Davis, and Pedersen : the yield curve level resp. This approach is adopted for all currencies except NOK. Since Norway do not issue two-year Treasuries, the equally weighted average of the yields of the one-year and three-year Treasuries is used as proxy for the yield of the two-year Treasury.

Germany Treasuries are once again used as proxy for EUR. One may legitimately wonder whether the previous success of Taylor rule models faded over the last decade. While they underline it via different market dynamics, those indicators all rely on the same underlying logic: stress in financial markets induces significant currency movements. Amen as cited in Osler, indeed emphasizes that market participants are on the lookout for signals of market fragility, upon which their trading activity is partially based.

Brunnermeier, Nagel, and Pedersen for instance argue that market volatility leads investors to raise cash buffers by liquidating carry trade positions. Repo, deposit and bill rates however all represent strong indicators of the current level of liquidity in the markets.

In a related manner, the Libor-OIS spread display the healthiness of the banking system. By measuring the accumulation of data surprises for a given country relative to Bloomberg median expectations in a rolling three-month window, the ESI informs on the economic over- or under-performance of this country, which should logically drive exchange rate movements.

The first two gauges stress in traditionally risk- sensitive implied volatilities for FX options, equity options and swaptions, three-month TED spread, corporate CDS, and EM sovereign spread over the past 20 or weekdays. It also incorporates correlations between high-beta and low-beta EM currencies. This paper integrates the prices of Brent oil futures and of gold spot. The future price is preferred to spot due to the much higher liquidity in the former: the future contract is the preferred instrument in oil markets since the Gulf oil crisis.

Being considered a safe haven, gold represents a hedge against various stress situations such as inflation or currency devaluation. This paper however takes a less orthodox approach to integrate commodity prices into exchange rate predictions as well. The CTOT gauge the impact of raw commodity prices on the terms of trade for a given country. By including 46 commodities across energy, metals and soft commodities, the CToT is expected to provide broader information than commodity predictor variables used in previous literature.

This approach also benefits from linking currency prices to the traditional macroeconomic variable that is trade balance. The gradual nature of this process can be observed through the serial correlation in order flow time series, which has been observed by Breedon and Vitale as well as Rime, Sarno, and Sojli While trading frictions is a quite self-explanatory phenomenon, limits to arbitrage and asymmetric information are not.

The following three characteristics are especially noteworthy. The indicator is normalized by the medium 6-month volume for the currency, is calculated by currency rather than currency pair, and is distinguished between four client types: corporates, leveraged accounts, real money accounts, and banks excluding market-making flows among dealers. As previously mentioned, preprocessing, such as normalization, impact the out-of-sample forecasting power of models Rossi, Chinn and More however emphasize that the normalization of order flow data does not lead to changes in the results of the model.

The normalization of the data by Citi should therefore not be a source of concern. One may argue that considering the overall demand for a currency constitutes a superior approach. Each currency can indeed be traded through a very large number of crosses. The model developed further in this paper only gives trading signals for USD crosses but it should not be considered an issue since triangular arbitrage guarantees modest pricing differences in the G10 space.

The distinction between client types is acutely encouraging because a significant part of the literature relies on aggregated order flow. Having their own particular goals, each client type informs exchange rates in its own way. This fact leads to the choice of incorporating market microstructure through a less traditional approach: the positioning of currency hedge fund managers. While microstructure typically focused on trading activity, I believe active positioning informs the market microstructure as well through alpha-generation expectation, overcrowded- ness indication, and signal of market fragility.

Active managers take positions with the expectation to generate above-the-market returns. Melvin and Shand show that currency hedge fund managers successfully generate alpha, alpha being defined as returns not explained by momentum, trend and carry risk premia.

The positioning of currency managers may therefore reveal their superior information. While one may argue that the efficient market hypothesis does hold, one cannot refute that positions can reveal overcrowded-ness of trades. An overcrowded trade means the upcoming end of its overperformance, which in returns implies an imminent selling pressure from currency managers exiting that very position.

Last, Amen as cited in Osler, explains that market participants rely on daily FX positioning as an indicator of market fragility, which constitutes a clear determinant of upcoming trading activity. Large positive resp. Volatility will however not be considered globally but rather by currency pair as Melvin, Prins, and Shand did. The choice of implied volatility differs from the GARCH models adopted in the prediction model and trading rule.

While MS modelling and trading rule adoption make use of information regarding data in previous periods, input data does not need such backward-looking property. On the contrary, the forward-looking nature of implied volatility should yield higher predictive power than historical or realized volatility as an input. Peterson and Tucker actually stress the presence of information in currency option markets that is not yet available in the respective spot markets.

Besides, using the same GARCH-generated volatility as both an input and a characteristic of the regression model threatens to generate significant model misspecification. This risk remains deeply toned down when using two distinct volatility types. The calculation of implied volatility may rely on risk- neutral or stochastic volatility models.

As previously discussed, nesting stochastic volatility and MS leads to a loss of predictive power. While implying volatility from a stochastic volatility model may not have such effect on the MS model, risk-neutral option pricing is preferred to circumvent any such potential drawback. The choice of option maturity matters significantly due to the term structure of volatility. Since short-dated volatility tends to whip around more than long-dated volatility in response to immediate news and changes in market sentiment, the short end of the curve if favored.

If a country finds itself running a current account imbalance, the required adjustment might flow through currency depreciation, which occasions a wealth transfer to the rest of the world. Coeurdacier, Kollmann, and Martin highlight that changes in asset prices are conspicuously led by changes in net foreign equity and that equity constitutes a poor exchange rate hedge. If equity is not used as exchange rate hedge, equity may instead be the source of changes in exchange rate as conjectured by Hau, Massa, and Peress Froot and Ramadorai corroborate these findings and argue such effect is even persistent over the long-term.

Persistent or not, equity is likely to exhibit predictive power over short time exchange rates. By applying cointegration on a rolling-window basis, the model hereby developed can learn from such correlation whenever it appears. Melvin, Prins, and Shand corroborate the predictive power of stock market returns and volatility over exchange rates. Integrating the implied volatility of the relevant benchmark is not as straightforward though.

While the theoretical statistical background on which the model rests upon has been thoroughly discussed, the precise implementation has not yet been. The model relies on the assumption of the existence of two Markov states: a stable regime and an unstable regime.

The stable regime characteristically displays cointegration relationships between the levels of exchange rate and of predictor variables such as the order flow of leveraged investors.

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It is enough to answer two questions for successful trading on Forex: what trend is relevant now and when it will reverse. While even the novice can handle the first task, an overbought indicator will be useful to handle the second. Method of Volume Spread Analysis, which reveals manipulations and "smart money" transactions, is rapidly gaining popularity, but while years ago the traders studied the market on their own, now the VSA indicator can take some of the functions.

Medium-term strategies are considered to be the most stable and simple, but many experts teach beginners to give preference to levels and volumetric analysis, and for some reason ignore time-tested reliable indicators. Special modules for the analysis of the dynamics of balance and funds in the account are added to many modern terminals.

Unfortunately, the MT4 terminal is outdated for this purpose, but the balance indicator can easily compensate for these shortcomings. Rubicon indicator is not just another custom indicator designed to perform some specific function, it can be considered a full-fledged trading strategy that is suitable for any timeframe and instruments.

Indicator QQE belongs to a class of rare algorithms that consistently work on any trading tools for decades. Traders buy and sell assets for currency in financial markets, and while there is no problem in assessing the value of the instrument on the commodity or stock exchange, Forex has got special cluster indicators for this purpose. You can often hear that the ultra-precise indicators are fantasy of the beginners who are searching for the "Holy Grail".

In part, this is a true remark, because even an accurate indicator sometimes fails, but critics are not telling the whole story here, as such indicators do exist. When educating new traders, many teachers and experienced speculators forget to mention that the indicators for beginners must meet several criteria, the most important of which being ease of setting and simplicity of formulas.

Technical indicators do not lose their relevance after first calculations on a sheet of paper and are used in our age of IT technologies in all terminals, which is quite natural, because they greatly simplify the process of finding trading signals. Novice traders often ask whether there are efficient indicators that can generate reliable signals to open orders.

Of course, such algorithms exist, and today's publication will be dedicated to indicators like these. Cyclical fluctuations are an integral feature of many physical and social processes, and the foreign exchange market, where thousands of traders and investors commit certain actions every second, is no exception to this rule.

The price of any currency depends on the situation in the economy, and if in the 20th century the dynamics of price was mainly affected by the monetary policy of central banks, now you have to keep track of events around the world, and news indicator helps to do so. The main goal of any trading strategy is not even searching for a particular signal to open a position, but rather filtering out the excess market noise that can "break" reliable patterns, and ZZ indicator is very good at handling this task.

Join us:. Forex About the site. Forex indicators Forex indicators are auxiliary tools necessary for decision-making in difficult moments, when the behavior of a currency pair is ambiguous.

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