Littérature scientifique sur le sujet « Market expectation errors »

This study demonstrates how expectation errors in a credit market generate economic fluctuations. To this end, we employ simulation analysis using a dynamic stochastic general equilibrium model. Our model includes two building blocks that are not included in the standard models: the banking sector and matching friction in the labor market. By introducing the banking sector, we can confirm that if economic agents fallaciously expect a rise in future asset prices, such expectations will cause an economic boom and bust. The variation of this fluctuation is quite large and the recession short-lived, but these drawbacks can be avoided by adding matching friction.

Extrapolations of future market forward rates are a better predictor of the 30-days ahead BRL-USD exchange rate than forecasts from the Central Bank Focus survey of Brazilian market participants. This is puzzling because market participants observe forward rates as they submit predictions, and thus these agents perform biased forecasts even though they have access to a set of unbiased forecasts, consistent with a martingale process for the exchange rate. We argue that this rational conundrum can be explained by a mechanism through which new information enlarges the information set (a filtration), changing the underlying measure and inducing a drift into the martingale process, turning the process into a strict local martingale and generating a forecast bias. Empirical results suggest that Focus survey forecasts indeed display characteristics of a strict local martingale, while spot exchange rates and forward rates are consistent with a martingale process.

This study examined whether a person’s gender influences his or her real estate trading sentiments. Previous studies have suggested that risk aversion, loss aversion, and expectations of probabilities can affect trading sentiments. Thus, this study inferred that a person’s gender can inform these three factors and thus lead to differences in real estate trading preferences between genders. More noticeable expectation adjustment behavior was observed in men than in women. However, no significant expectation errors were observed in both genders. Moreover, this study observed that gender differences in risk aversion were affected by the fear index, whereas gender differences in loss aversion were affected by unemployment rates. Stock market rallies affected only men’s perceptions toward real estate value. Overall, a more noticeable optimism was observed in men, who were significantly influenced by house price changes. JEL Classifications: G10, R30

The most simple rectangular input-output models use two rectangular matrices: R a market coefficient matrix, A* a production coefficient matrix. A given exogenous demand Xo determines the sectorial activity levels X* = [I — RA*]-1Xo. We assume that A* is random with expectation A. We study the distribution of the "error" X* — X with X = [I — RA]-1Xo. (1) For the statistically independent elements of A*, we analytically prove that X < EX*. (2) In the more realistic case of statistically dependent elements of A*. (a) One submatrix of A* with T non zero elements is chosen. The probabilistic model which generates the T coefficients is as follows: a* = (1 — μ)a + μ(S/n) b* où a* is the vector of the T random elements, a is the expectation of a* whose components are observed values of a real input-output model, S is the sum of components of a, μ is a parameter between zero and one, b* is a multinomial random vector with T components and parameters n, number of drawings during an experiment, and a/S, the corresponding probabilities. We control the variability of a* through μ and n. For a given experiment, we get a realisation of A* and we compute X*. K independent experiments allow us to estimate the expectation and the variance-covariance matrix of X*, simultaneous confidence intervals for the expectation of the components of X*, and also a few global measures of errors on X*. The Canadian model for 1961 (16 productive sectors, 40 commodities), is tested with that model. The main result is: the relative errors, measured according to the variation coefficients, are greatly reduced when we pass from the "errors" on a* to the corresponding "errors" on X*. (b) The same random model is also simultaneously applied to 2 or 3 sub-matrices of A*.

Using market covered European call option prices, the Independence Metropolis-Hastings Sampler algorithm for estimating Implied volatility in option pricing was proposed. This algorithm has an acceptance criteria which facilitate accurate approximation of this volatility from an independent path in the Black Scholes Model, from a set of finite data observation from the stock market. Assuming the underlying asset indeed follow the geometric brownian motion, inverted version of the Black Scholes model was used to approximate this Implied Volatility which was not directly seen in the real market: for which the BS model assumes the volatility to be a constant. Moreover, it is demonstrated that, the Implied Volatility from the options market tends to overstate or understate the actual expectation of the market. In addition, a 3-month market Covered European call option data, from 30 different stock companies was acquired from Optionistic.Com, which was used to estimate the Implied volatility. This accurately approximate the actual expectation of the market with low standard errors ranging between 0.0035 to 0.0275.

This paper summarizes recent empirical research on compliance costs and strategies and on permit market performance under the U.S. acid rain program, the first large-scale, long-term program to use tradeable emissions permits to control pollution. An efficient market for emissions permits developed in a few years, and this program more than achieved its early goals on time, and it cost less than had been projected. Because of expectation errors, however, investment was excessive, and permit prices substantially understate abatement costs. The tradeable permits approach has worked well, but it is not a miracle cure for environmental problems. Coauthors are Paul L. Joskow, A. Denny Ellerman, Juan Pablo Montero, and Elizabeth M. Bailey.

We illustrate empirically the use of return-based style analysis for domestic stock funds. We search the optimal style model according to the tracking errors, investigate the consistency of the fund style for the optimally selected model, and finally investigate the relationship between fund styles and their fund performance. We use weekly fund return data of domestic stock funds from January 2, 2002 to June 30, 2008, and do style analyses based on the various style indices. The major findings are as follows. Firstly, we find that the style index models with constraint which in practice restricts short sale are better than those with no such constraint. Secondly, we find that the style index model which divides stock market with four categorized indices based on the dimension of size and book-to market and includes the bond market index is the most useful if they are evaluated based on the out-of-sample tracking errors. While adding the Fama-French 3 factors to the selected model does not improve the explanation power, adding the industry sector indexes improves the explanation power. Thirdly, we investigate the consistency of the fund style models and find that the better performing funds are more volatile in the change of the fund style. Fourthly, we find that, contrary to the expectation that the growth-oriented funds perform better than the value-oriented one, the fund performance and style are observed to be mixed. This finding shows that the fund styles are frequently changed according to their performances and market conditions.

This study aims to evaluate the effects of the adoption of the International Financial Reporting Standards (IFRS) on the accrual-based managed earnings behavior of firms in Nigeria. The panel corrected standard errors (PCSE) multivariate method was employed to analyze firm-level data for 125 firms and covers the 11 sectors on the Nigerian Stock Exchange (NSE). The results of the Welch–Satterthwaite test show a significant difference between the pre-adoption (2003–2011) and post adoption (2012–2020) discretionary accruals. These variables conformed to the a priori expectation and are all significant in the most parsimonious models. Contrary to some developed countries, the data does not support the idea that leverage, growth, and book-to-market value influence managed earnings for Nigeria. Managed earnings are not solely time-driven but are explained by certain firm characteristics (IFRS adoption, post-adoption firm-size, post-adoption audit firm’s size, returns on equity and asset turnover). Future research could explore opportunities in the areas of limitation we identified.

AbstractResults for a total of 804 double-blind tastes by experienced tasters during nine tasting events are reported. T-test results reject the hypothesis that flight-position bias affects results. The distribution of ranks for a wine is a mixture distribution, and tests concerning the variance of that mixture distribution do not isolate the variance due to the randomness mixture component alone. T-statistics for the mean ranks of high- and low-ranking wines are over several standard deviations from a random expectation. T-tests show that the statistical significance of the difference between wine ranks is positively related to the difference in their mean ranks. At a 95% level of significance, the difference in ranks between the first- and second-place wines appears to be significant in 33% of tastings. At 95%, the difference in ranks between the first- and last-place wines appears to be significant in 100% of tastings. Monte Carlo simulation shows that much of those differences could be illusory and due to ranking procedures that lead to Type I errors. While the mean correlation coefficient between price per bottle and mean preference is a weakly positive 0.23, this may not indicate an inefficient market. (JEL Classifications: A10, C00, C12, D12)

The aim of this research is to investigate the long-run co-integrating relationships in the Asian markets. Our research focuses on 4 areas; pair trading, out-of-sample forecasting, testing the unbiased forward exchange rate hypothesis and testing the expectation hypothesis of the term structure of interest rates. The introduction is provided in chapter one. In chapter two, we develop a pairs trading strategy using individual stocks listed in the Stock Exchange of Thailand. Engle and Granger approach is used to identify the potential pairs that are cointegrated. The results show that pairs trading strategy is profitable in this market. Chapter three examines the forecasting performance of the error correction model on daily share price series from the Stock Exchange of Thailand. The disequilibrium term is classified into “correct” and “mix” sign based on Alexander (2008)’s criterion; the results indicate that the error correction component can help to improve the predictability in the long run. Chapter four tests the unbiased forward rate hypothesis of 11 Asian exchange rates using linear conventional regression, ECM and logistic smooth transition regression with the forward premium as the transition variable. Out-of-sample forecasting results also suggest that inferior forecasting performance could be obtained as a result of using linear models. In chapter five, we investigate the expectation hypothesis of the term structure of interest rate for four Asian countries. We employ linear models and nonlinear approaches that allow to capture asymmetric and symmetric adjustments. The result also indicates that the term structure can be better modeled by means of LSTR models. The forecasting exercise also confirms these findings.

AbstractSpeech recognition can be formulated as the problem of guessing a sequence of words that produces a sequence of sounds. The human brain is remarkably good at solving this problem, even though the same words correspond to many different sounds, because of accents or characteristics of the voice. Moreover, the environment is always noisy, to that the listeners hear a corrupted version of the speech.Computers are getting much better at speech recognition and voice command systems are now common for smartphones (Siri), automobiles (GPS, music, and climate control), call centers, and dictation systems. In this chapter, we explain the main ideas behind the algorithms for speech recognition and for related applications.The starting point is a model of the random sequence (e.g., words) to be recognized and of how this sequence is related to the observation (e.g., voice). The main model is called a hidden Markov chain. The idea is that the successive parts of speech form a Markov chain and that each word maps randomly to some sounds. The same model is used to decode strings of symbols in communication systems.Section 11.1 is a general discussion of learning. The hidden Markov chain model used in speech recognition and in error decoding is introduced in Sect. 11.2. That section explains the Viterbi algorithm. Section 11.3 discusses expectation maximization and clustering algorithms. Section 11.4 covers learning for hidden Markov chains.

The primary objective of this study was to examine the causal relationships between the S&P 500, the Dow Jones Industrial, the NASDAQ Composite, and the VIX indices using the VECM method. The findings demonstrated a strong mutual causal relationship between indices. This relationship strongly indicated that other indices tend to follow the VIX index. The direction of this relationship is most valid for American stock markets. Considering the causal relationships between other country indices and the VIX, it can be said that this relationship will remain weak because the VIX index only represents the American markets. Furthermore, even if the relationships between the VIX index and other country indices are accepted as econometrically correct, the mutual causal relationships found cannot go beyond illusion. A foreign investor should not build a portfolio based solely on the VIX index which reflects future expectations only in the American market. However, a foreign investor can use the VIX index as a great metric in the process of minimizing portfolio risk.

China's gold futures market has been in market for more than four years, is the risk transfer function fully realized? How the performance of hedging? Based on the data of futures prices and spot prices from January 9th of 2008 to December 31st of 2010, we use the following four statistical models such as traditional regression model (OLS), two-variable vector auto regression model (B-VAR), error correction hedging model (ECM), and error correction GARCH model (EC-GARCH) to perform stationarity and cointegration test On the basis of minimum risk hedge ratio estimated, the following conclusions are made based on the study: (1) As China's gold futures market has run for more than three years, hedge is effective through the gold futures market, which can significantly reduce the participants ‘ risk of price fluctuation; (2)In practice, hedging ratio should be rationally determined by different models according to different hedging length and different expectations. Based on these conclusions, this paper also made corresponding policy recommendations.

This chapter describes a novel simulation model (InventSim) of the process of product invention. Invention is conceptualized as a process of directed search on a landscape of product design possibilities, by a population of profit-seeking inventors. The simulator embeds a number of real-world search heuristics of inventors, including anchoring, election, thought experiments, fitness sharing, imitation, and trial and error. A series of simulation experiments are undertaken to examine the sensitivity of the populational rate of advance in product sophistication to changes in the choice of search heuristics employed by inventors. The key finding of the experiments is that if search heuristics are confined to those that are rooted in past experience, or to heuristics that merely generate variety, limited product advance occurs. Notable advance occurs only when inventors’ expectations of the relative payoffs for potential product inventions are incorporated into the model of invention. The results demonstrate the importance of human direction and expectations in invention. They also support the importance of formal product/project evaluation procedures in organizations, and the importance of market information when inventing new products.

The second Bush administration, this chapter reveals, marked a notable shift in attitudes and style in its approach to dealing with China. Noteworthy changes in personnel presaged greater support for more engagement with Beijing as Condoleezza Rice became secretary of state. Strategic priorities in the ongoing War on Terror, the wars in Afghanistan and Iraq, and the threat of nuclear proliferation in North Korea made for significant anchors in this paradigm shift. At the same time, there was also reason to believe that important changes in China’s behavior and makeup could develop significantly along the lines of the theory that more engagement, and not less, would work to soften authoritarian rule on the mainland as economic integration reinforced the liberal status quo. What comes to fore in the analysis of these security relationships is the misconception in the White House that the “constructive” partnership was interpreted analogously on each side and in terms of American priorities and challenges. By the end of the Bush years, it was too late to correct the error that in the lingering anticipation of cooperative outcomes, the administration had accepted progress in process as the measurement of positive results. The shortfall in expectations would come to bear directly on the Obama administration’s expectations and policies for China.

An overview is provided of multiple book themes. A critical one is explaining how and where conscious states of seeing, hearing, feeling, and knowing arise in our minds, why they are needed to choose effective actions, yet how unconscious states also critically influence behavior. Other themes include learning, expectation, attention, imagination, and creativity; differences between illusion and reality, and between conscious seeing and recognizing, as embodied within surface-shroud resonances and feature-category resonances, respectively; roles of visual boundaries and surfaces in understanding visual art, movies, and TV; different legacies of Helmholtz and Kanizsa towards understanding vision; how stable opaque percepts and bistable transparent percepts are explained by the same laws; how solving the stability-plasticity dilemma enables brains to learn quickly without catastrophically forgetting previously learned but still useful knowledge; how we correct errors, explore novel experiences, and develop individual selves and cumulative cultural accomplishments; how expected vs. unexpected events are regulated by interacting top-down and bottom-up processes, leading to either adaptive resonances that support fast and stable new learning, or hypothesis testing whereby to learn about novel experiences; how variations of the same cooperative and competitive processes shape intelligence in species, cellular tissues, economic markets, and political systems; how short-term memory, medium-term memory, and long-term memory regulate adaptation to changing environments on different time scales; how processes whereby we learn what events are causal also support irrational, superstitious, obsessional, self-punitive, and antisocial behaviors; how relaxation responses arise; and how future acoustic contexts can disambiguate conscious percepts of past auditory and speech sequences that are occluded by noise or multiple speakers.

Predictive design analytics is a new paradigm to enable design engineers to extract knowledge from large-scale, multi-dimensional, unstructured, volatile data, and transform the knowledge and its trend into design decision making. Predictive, data-driven family design (PDFD) is proposed as one of the predictive design analytics methods to tackle some issues in family design. First, a number and specifications of product architectures are determined by data (not by pre-defined market segments) in order to maximize expected profit. A trade-off between price and cost in terms of the quantity and specifications of architectures helps to set the target in the enterprise level. k-means clustering is used to find architectures that minimize within architecture sum of squared errors. Second, a price prediction method as a function of product performance and deviations between performance and customer requirements is suggested with exponential smoothing based on innovations state space models. Regression coefficients are treated as customer preferences over product performance, and analyzed as a time series. Prediction intervals are proposed to show market uncertainties. Third, multiple values for common parameters in family design can be identified using the expectation maximization clustering so that multiple-platform design can be explored. Last, large-scale data can be handled by the PDFD algorithm. A data set which contains a total of 14 million instances is used in the case study. The design of a family of universal electronic motors demonstrates the proposed approach and highlights its benefits and limitations.

Information in various markets constitutes the primary basis for making the right decisions in a modern and globalized world. Therefore, opportunities grow based on the availability of data and how the data is structured to obtain information that supports decision-making processes, Ogrean (2018) and Neubert (2018), and even more so when business dynamics revolve around satisfying the demand for the products or services offered, Jacobs and Chase (2009). This article proposes the analysis of the new vehicle market, through operational research techniques, addressing the behavior of vehicle sales for medium and long-term projections for business management. The analysis is developed through Markov Chains and time series analysis techniques, so a complementary approach is used to obtain predictions in future scenarios such as analysis in sales levels related to market shares. Choi et al (2018), indicate that one of the important applications of Big Data in business management is in the field of demand forecasts, becoming one of the common alternatives in prediction for data series over time. The data is taken from Statistics of the National Association of Sustainable Mobility, from 2016 to 2019 for new vehicles in the Colombian market, Andemos (2021). Merkuryeba (2019) proposes procedures between techniques that allow a comprehensive approach to forecasts and where the methods complement each other, it is through the use of the methodology in Markov chain models (Kiral and Uzun 2017), plus the methodology of the time series analysis (Stevenson et al 2015), which with a complementary approach, can reach a more detailed and comprehensive level of analysis for the statement about the future of the variable of interest: vehicle market sales for business management.The results showed that Markov chains were very useful in long-term analysis for sales forecasting and their analysis by market segmentation, for this the sales level is ranked according to the technique of Pareto. Another important contribution to the Markov chain in business management corresponds to the analysis disaggregated by sales rankings, for example in ranking 1 (first 5 brands), was obtained an expectation of value defined at 67.1% of the total sales level, also an internal analysis of this percentage ranking was carried out. Complementarily, for the alternative of times series analysis; we start from the analysis of the demand, where a seasonal behavior of vehicle sales is detected. Rockwell and Davis (2016) and Stevenson et al (2015), establish a procedure for estimating and eliminating seasonal components by using the seasonal index. Additionally, Weller and Crone (2012) and Lau et al (2018), recommend two common alternatives to measure forecast error and making decisions to selected the technique more adequate for business management: mean absolute deviation (MAD) and mean absolute percentage error (MAPE), finally, the result of the three techniques developed: moving average, exponential smoothing, and weighted moving average, the simple exponential smoothing, optimized through MAPE minimization is the selected technique, with which short and medium-term forecasts are defined.This study contributes directly to decision-making in the context of the marketing of new vehicles, as well as in academic settings in relation to research processes in data series under the configuration of big data. In this sense, it was demonstrate that the behavior of sales, segmented by market levels according to the participating brands, can be transformed into estimates of future behavior that establishes an orienting mapping of business objectives with respect to the possible level of participation in quotas of market. Finally, the methodological scheme under an epistemological perspective supported by technical decisions, represent an academic contribution of great relevance for business management, where is recommended to use the time series techniques for short and medium-term forecasts, while Markov chains for the prediction and analysis of the sales structure in medium to long term forecasts.

Efficient and truthful mechanisms to price time on remote servers/machines have been the subject of much work in recent years due to the importance of the cloud market. This paper considers online revenue maximization for a unit capacity server, when jobs are non preemptive, in the Bayesian setting: at each time step, one job arrives, with parameters drawn from an underlying distribution. We design an efficiently computable truthful posted price mechanism, which maximizes revenue in expectation and in retrospect, up to additive error. The prices are posted prior to learning the agent's type, and the computed pricing scheme is deterministic. We also show the pricing mechanism is robust to learning the job distribution from samples, where polynomially many samples suffice to obtain near optimal prices.

The time required to get a device to market is critical to a successful design, development, and manufacturing process [1]. Achieving status of the first device to market is often a priority for manufacturers and developers. Upon market introduction, it is well known that device performance must meet at least minimum standards in order to provide consumer satisfaction and be a successful product to prevent competitive devices from taking over the market [1]. However, if a design only meets minimum expectations, it may struggle to maintain market control. This demonstrates the tradeoffs of speed-to-market and performance, for which optimization has not been clearly defined [2]. Product performance and usability can be designed in, evaluated and enhanced in order to avoid user errors and achieve optimal profitability. For medical devices, clinical trials are a key step in preparing to take a device to market. Clinical trials can allow for analyses of the effectiveness of the device in the patient care process. For wearable medical devices, patient usability is crucial to patient adherence and safety since the device will be operated by non-medically trained individuals [3,4]. Without adequate usability, adherence and continuity of care are greatly reduced which will reduce the overall effectiveness of the device [5,6]. Human factors principles can best be incorporated in the design process to improve the usability of medical devices and patient safety [5,6,7], specifically for those used in telemedicine [4] and cardiovascular treatment [3]. The objective of this project was to evaluate a telemedicine heart rate monitoring device for patient usability in order to improve the next device’s performance and lead to greater patient adherence for the current version via an improved user manual.

The efficiency of the design process in companies today is largely dependent on the skills of individual members of project teams, the capacity of the software available on the market and any assistance provided by the tools and methods found scattered around the industrial world. Within this context, there have been myriad attempts to optimize the different stages in the design process by using computerization, but a growing proportion of current research work on design tends to agree that the crucial point in the act of designing lies in the phase where inventive ideas are sought and that without such ideas being generated, there is no successful future for the product. In the framework of a large number of research activities it has been proven that the TRIZ (the Russian acronym of Theory of Inventive Problem Solving) contribution to the science of design has a promising potential [1–2–3–4]. On the other hand, the added value and spectrum of problem typology where TRIZ could help remains unclear for most researchers for various reasons. Originally, since the theory has penetrated the so-called occidental world, every individual took from TRIZ what he thought was right according to his own knowledge about design, problem solving process, creativity or needs forgetting that 95% of scientific literature about TRIZ’s fundamentals was written either in Cyrillic or in unpublished manuscripts. In most cases, theses multitudes of uses did not see that the original idea of TRIZ was to formulate a theory as strong basement for building methods and tools to satisfy designers expectations. This fact led nowadays TRIZ’s image to fuzziness since available literature on the subject only presents case studies and uses of tools (mostly the matrix) although TRIZ’s scope is much wider. To avoid further aggravation of this fact, this paper aims at presenting the first axiom describing TRIZ’s objectives as a basement for our research activities. Two methodological approaches are then presented : the “trial and error” type approach, which is still very much in use everywhere in industry and which aims to explore an area of solution-seeking, following a structural approach or not, through a series of attempts, which often end up in failure. The second approach is the “convergent” approach proposed by the TRIZ body of knowledge and developed as a contribution to design activity’s efficiency in our research center. This approach aims at limiting the sphere of research during a larger and longer problem statement stage to converge towards a small number of solutions in opposition to traditional approaches which consists in generating a large amount of ideas and select the best suitable ones to be implemented. A case study on the internal ventilation of a car will also be presented to illustrate the quality of the results that could be obtained when applying this convergent approach.

We develop a novel characterization of participants’ forecasts with a mixture of normal variables arising from a Markov component. Using this characterization, we formulate five behavioral specifications, including four implied by the diagnostic expectations approach, as well as three implied by REH, and derive several new predictions for Coibion and Gorodnichenko.s regression of forecast errors on forecast revisions. Predictions of all eight specifications are inconsistent with the observed instability of individual CG regressions’ coefficients, based on inflation forecasts from 24 professionals. Our findings suggest how to build on key insights of the REH and behavioral approaches in specifying individuals’ forecasts.

In the second quarter, annual inflation (9.67%), the technical staff’s projections and its expectations continued to increase, remaining above the target. International cost shocks, accentuated by Russia's invasion of Ukraine, have been more persistent than projected, thus contributing to higher inflation. The effects of indexation, higher than estimated excess demand, a tighter labor market, inflation expectations that continue to rise and currently exceed 3%, and the exchange rate pressures add to those described above. High core inflation measures as well as in the producer price index (PPI) across all baskets confirm a significant spread in price increases. Compared to estimates presented in April, the new forecast trajectory for headline and core inflation increased. This was partly the result of greater exchange rate pressure on prices, and a larger output gap, which is expected to remain positive for the remainder of 2022 and which is estimated to close towards yearend 2023. In addition, these trends take into account higher inflation rate indexation, more persistent above-target inflation expectations, a quickening of domestic fuel price increases due to the correction of lags versus the parity price and higher international oil price forecasts. The forecast supposes a good domestic supply of perishable foods, although it also considers that international prices of processed foods will remain high. In terms of the goods sub-basket, the end of the national health emergency implies a reversal of the value-added tax (VAT) refund applied to health and personal hygiene products, resulting in increases in the prices of these goods. Alternatively, the monetary policy adjustment process and the moderation of external shocks would help inflation and its expectations to begin to decrease over time and resume their alignment with the target. Thus, the new projection suggests that inflation could remain high for the second half of 2022, closing at 9.7%. However, it would begin to fall during 2023, closing the year at 5.7%. These forecasts are subject to significant uncertainty, especially regarding the future behavior of external cost shocks, the degree of indexation of nominal contracts and decisions made regarding the domestic price of fuels. Economic activity continues to outperform expectations, and the technical staff’s growth projections for 2022 have been revised upwards from 5% to 6.9%. The new forecasts suggest higher output levels that would continue to exceed the economy’s productive capacity for the remainder of 2022. Economic growth during the first quarter was above that estimated in April, while economic activity indicators for the second quarter suggest that the GDP could be expected to remain high, potentially above that of the first quarter. Domestic demand is expected to maintain a positive dynamic, in particular, due to the household consumption quarterly growth, as suggested by vehicle registrations, retail sales, credit card purchases and consumer loan disbursement figures. A slowdown in the machinery and equipment imports from the levels observed in March contrasts with the positive performance of sales and housing construction licenses, which indicates an investment level similar to that registered for the first three months of the year. International trade data suggests the trade deficit would be reduced as a consequence of import levels that would be lesser than those observed in the first quarter, and stable export levels. For the remainder of the year and 2023, a deceleration in consumption is expected from the high levels seen during the first half of the year, partially as a result of lower repressed demand, tighter domestic financial conditions and household available income deterioration due to increased inflation. Investment is expected to continue its slow recovery while remaining below pre-pandemic levels. The trade deficit is expected to tighten due to projected lower domestic demand dynamics, and high prices of oil and other basic goods exported by the country. Given the above, economic growth in the second quarter of 2022 would be 11.5%, and for 2022 and 2023 an annual growth of 6.9% and 1.1% is expected, respectively. Currently, and for the remainder of 2022, the output gap would be positive and greater than that estimated in April, and prices would be affected by demand pressures. These projections continue to be affected by significant uncertainty associated with global political tensions, the expected adjustment of monetary policy in developed countries, external demand behavior, changes in country risk outlook, and the future developments in domestic fiscal policy, among others. The high inflation levels and respective expectations, which exceed the target of the world's main central banks, largely explain the observed and anticipated increase in their monetary policy interest rates. This environment has tempered the growth forecast for external demand. Disruptions in value chains, rising international food and energy prices, and expansionary monetary and fiscal policies have contributed to the rise in inflation and above-target expectations seen by several of Colombia’s main trading partners. These cost and price shocks, heightened by the effects of Russia's invasion of Ukraine, have been more prevalent than expected and have taken place within a set of output and employment recovery, variables that in some countries currently equal or exceed their projected long-term levels. In response, the U.S. Federal Reserve accelerated the pace of the benchmark interest rate increase and rapidly reduced liquidity levels in the money market. Financial market actors expect this behavior to continue and, consequently, significantly increase their expectations of the average path of the Fed's benchmark interest rate. In this setting, the U.S. dollar appreciated versus the peso in the second quarter and emerging market risk measures increased, a behavior that intensified for Colombia. Given the aforementioned, for the remainder of 2022 and 2023, the Bank's technical staff increased the forecast trajectory for the Fed's interest rate and reduced the country's external demand growth forecast. The projected oil price was revised upward over the forecast horizon, specifically due to greater supply restrictions and the interruption of hydrocarbon trade between the European Union and Russia. Global geopolitical tensions, a tightening of monetary policy in developed economies, the increase in risk perception for emerging markets and the macroeconomic imbalances in the country explain the increase in the projected trajectory of the risk premium, its trend level and the neutral real interest rate1. Uncertainty about external forecasts and their consequent impact on the country's macroeconomic scenario remains high, given the unpredictable evolution of the conflict between Russia and Ukraine, geopolitical tensions, the degree of the global economic slowdown and the effect the response to recent outbreaks of the pandemic in some Asian countries may have on the world economy. This macroeconomic scenario that includes high inflation, inflation forecasts, and expectations above 3% and a positive output gap suggests the need for a contractionary monetary policy that mitigates the risk of the persistent unanchoring of inflation expectations. In contrast to the forecasts of the April report, the increase in the risk premium trend implies a higher neutral real interest rate and a greater prevailing monetary stimulus than previously estimated. For its part, domestic demand has been more dynamic, with a higher observed and expected output level that exceeds the economy’s productive capacity. The surprising accelerations in the headline and core inflation reflect stronger and more persistent external shocks, which, in combination with the strength of aggregate demand, indexation, higher inflation expectations and exchange rate pressures, explain the upward projected inflation trajectory at levels that exceed the target over the next two years. This is corroborated by the inflation expectations of economic analysts and those derived from the public debt market, which continued to climb and currently exceed 3%. All of the above increase the risk of unanchoring inflation expectations and could generate widespread indexation processes that may push inflation away from the target for longer. This new macroeconomic scenario suggests that the interest rate adjustment should continue towards a contractionary monetary policy landscape. 1.2. Monetary policy decision Banco de la República’s Board of Directors (BDBR), at its meetings in June and July 2022, decided to continue adjusting its monetary policy. At its June meeting, the BDBR decided to increase the monetary policy rate by 150 basis points (b.p.) and its July meeting by majority vote, on a 150 b.p. increase thereof at its July meeting. Consequently, the monetary policy interest rate currently stands at 9.0% . 1 The neutral real interest rate refers to the real interest rate level that is neither stimulative nor contractionary for aggregate demand and, therefore, does not generate pressures that lead to the close of the output gap. In a small, open economy like Colombia, this rate depends on the external neutral real interest rate, medium-term components of the country risk premium, and expected depreciation. Box 1: A Weekly Indicator of Economic Activity for Colombia Juan Pablo Cote Carlos Daniel Rojas Nicol Rodriguez Box 2: Common Inflationary Trends in Colombia Carlos D. Rojas-Martínez Nicolás Martínez-Cortés Franky Juliano Galeano-Ramírez Box 3: Shock Decomposition of 2021 Forecast Errors Nicolás Moreno Arias