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The relevance of financial volatility forecasting in efficient decision making regarding risk-related assets cannot be subdued. In the financial world, asset price volatility plays a pivotal role in investment decision making and portfolio setups. The prediction of these volatilities usually deal with noisy and non-stationary data bearing heteroscedastic nature. This paper introduces the volatilityforecastingpackage for financial volatility modelling, forecasting and visualization using state-of-the art algorithms. This package allows recourse to algorithms through a user friendly interface supported by the Mathematica framework, that provides easy access to models for high and low frequency data, while accessibly generating forecasts, estimating errors and generating plots. The package also allows analysis of user data and based on the results, a set of models appropriate for the data is suggested for eventual use. [ABSTRACT FROM AUTHOR]

In this study, we use daily gold log-returns to analyse the quality of forecasting expected shortfalls (ES) using volatility and models based on the extreme value theory (EVT). ES forecasts were calculated for conditional APARCH models formed on the entire distribution of returns, as well as for EVT models. The results of ES forecasts for each model were verified using the backtesting procedure proposed by Acerbi and Szekely. The results show that EVT models provide more accurate one-day ahead ES forecasts compared to the other models. Moreover, the asymmetric theoretical distributions for innovations of EVT models allow the improvement of the accuracy of ES forecasting. [ABSTRACT FROM AUTHOR]

We analize the Mexican peso-US dollar exchange parity the volatility using three models in order to evaluate the exchange rate risk performance and the these models capability of modeling such risk. The period June 21st, 2017 to June 21st, 2022 is marked by singular events that pressed this exchange rate parity, making it of great interest to study the tools that can be used to measure this uncertainty besides the study of the exchange rate uncertainty itself. The arrival of a leftwing politician to the presidency was an unprecedented event. At the beginning of 2020 the price war in the oil market and the emergence of the COVID-19 pandemic had important effects in the world economy. Finally, the war in Ukraine added pressures to the international trade of raw materials, inducing an inflationary episode with bad expectations about the economic performance around the world. The exchange rate scenarios posed by the aforesaid events provide us with a natural context to test which of the posited volatility models offers a better fit. Unanimously, the results under the three models suggest that during the first year of the President López Obrador period the exchange rate uncertainty was reduced compared to previous levels, returning again to those minor levels after the forced closure of economic activities due to the emergence of COVID-19, even though the exchange fluctuation band has been slightly higher than before the pandemic and the war on East Europe. [ABSTRACT FROM AUTHOR]

Forecasting salmon market volatility is crucial for reducing future uncertainty for market participants. This study explores the efficacy of the Long Short-term Memory (LSTM) network, a deep learning technique, in forecasting multi-step ahead salmon market volatility. The performance of the LSTM is assessed against a constructed volatility proxy and the Autoregressive Moving Average (ARMA) model, a traditional benchmark in time-series analysis. Evaluation is performed across various forecasting horizons using different forecast error measures. Our findings indicate that the ARMA model outperforms the LSTM in predicting salmon market volatility, suggesting that any non-linear patterns in the salmon market volatility might be too insignificant for an LSTM model to exploit effectively. However, we observed a significant discrepancy between the actual volatility values and the forecasts obtained by both models, indicating the complexity of accurately predicting salmon market volatility. [ABSTRACT FROM AUTHOR]

This study explores the returns and volatility patterns of currency portfolios (CP) in seven major Asian markets during noncrisis and crisis periods. The CP is developed by investing or divesting in the top three forward premiums or discount currencies in these markets and is rebalanced quarterly. The main findings are as follow: (1) Stochastic volatility models perform better than generalized autoregressive conditional heteroskedasticity models. (2) Spillover effects from the stock market on CP are observed for the examined periods. (3) The stochastic dominance analysis revealed that CP outperformed most stock markets. [ABSTRACT FROM AUTHOR]

Abstract Background SARS-CoV-2 (Covid-19 virus) infection exposed the unpreparedness of African countries to health-related issues, South Africa included. Africa recorded more than 211 853 deaths as a consequence of Covid-19. When rare and deadly diseases require urgent hospitalisation strikes, governments and healthcare providers are usually caught unprepared, resulting in huge loss of lives. Usually, at the beginning of such pandemics, there is no rich data for health practitioners and academics to be able to forecast the number of patients or deaths related to the pandemic. This study aims to predict the number of deaths associated with Covid-19 infection. With the availability of the number of deaths on a daily basis, the results stemming from this study are important to inform and plan health policy. Methods This study uses the daily number of deaths due to Covid-19 infection. Exploratory data analysis reveals that the data exhibits non-normality, three structural breaks and volatility clustering characteristics. The Markov switching (MS)-generalized autoregressive conditional heteroscedasticity (GARCH)-type model combined with heavy-tailed distributions is fitted to the returns of the data. Using available daily reported Covid-19-related deaths up until 26 August 2021, we report 10-day ahead forecasts of deaths. All forecasts are compared to the actual observed values in the forecasting period. Results The Anderson–Darling Goodness of fit test confirms that the fitted models are adequate for the data. The Kupiec likelihood ratio test and the root mean square error (RMSE) were used to select the robust model at different risk levels. At 95% the MS(3)-GARCH(1,1) combined with Pearson’s type IV distribution (PIVD) is the best model. This indicates that the proposed best-fitting model is reasonable and can be used for predicting the daily number of deaths due to Covid-19. Conclusion The MS(3)-GARCH(1,1)-PIVD model provides a reliable and accurate method for predicting the minimum number of death due to Covid-19. The accuracy of the proposed model will assist policymakers, academics and health practitioners in forecasting the volatility of future health-related deaths in which the predictability of volatility plays an integral role in health risk management.

Research background: The Russian invasion on Ukraine of February 24, 2022 sharply raised the volatility in commodity and financial markets. This had the adverse effect on the accuracy of volatility forecasts. The scale of negative effects of war was, however, market-specific and some markets exhibited a strong tendency to return to usual levels in a short time. Purpose of the article: We study the volatility shocks caused by the war. Our focus is on the markets highly exposed to the effects of this conflict: the stock, currency, cryptocurrency, gold, wheat and crude oil markets. We evaluate the forecasting accuracy of volatility models during the first stage of the war and compare the scale of forecast deterioration among the examined mar- kets. Our long-term purpose is to analyze the methods that have the potential to mitigate the effect of forecast deterioration under such circumstances. We concentrate on the methods designed to deal with outliers and periods of extreme volatility, but, so far, have not been investigated empiri- cally under the conditions of war. Methods: We use the robust methods of estimation and a modified Range-GARCH model which is based on opening, low, high and closing prices. We compare them with the standard maximum likelihood method of the classic GARCH model. Moreover, we employ the MCS (Model Confidence Set) procedure to create the set of superior models. Findings & value added: Analyzing the market specificity, we identify both some common patterns and substantial differences among the markets, which is the first comparison of this type relating to the ongoing conflict. In particular, we discover the individual nature of the cryptocurrency markets, where the reaction to the outbreak of the war was very limited and the accuracy of forecasts remained at the similar level before and after the beginning of the war. Our long-term contribution are the findings about suitability of methods that have the potential to handle the extreme volatility but have not been examined empirically under the conditions of war. We reveal that the Range-GARCH model compares favorably with the standard volatility models, even when the latter are evaluated in a robust way. It gives valuable implication for the future research connected with military conflicts, showing that in such period gains from using more market information outweigh the benefits of using robust estimators. [ABSTRACT FROM AUTHOR]

Background: SARS-CoV-2 (Covid-19 virus) infection exposed the unpreparedness of African countries to health-related issues, South Africa included. Africa recorded more than 211 853 deaths as a consequence of Covid-19. When rare and deadly diseases require urgent hospitalisation strikes, governments and healthcare providers are usually caught unprepared, resulting in huge loss of lives. Usually, at the beginning of such pandemics, there is no rich data for health practitioners and academics to be able to forecast the number of patients or deaths related to the pandemic. This study aims to predict the number of deaths associated with Covid-19 infection. With the availability of the number of deaths on a daily basis, the results stemming from this study are important to inform and plan health policy.Methods: This study uses the daily number of deaths due to Covid-19 infection. Exploratory data analysis reveals that the data exhibits non-normality, three structural breaks and volatility clustering characteristics. The Markov switching (MS)-generalized autoregressive conditional heteroscedasticity (GARCH)-type model combined with heavy-tailed distributions is fitted to the returns of the data. Using available daily reported Covid-19-related deaths up until 26 August 2021, we report 10-day ahead forecasts of deaths. All forecasts are compared to the actual observed values in the forecasting period.Results: The Anderson-Darling Goodness of fit test confirms that the fitted models are adequate for the data. The Kupiec likelihood ratio test and the root mean square error (RMSE) were used to select the robust model at different risk levels. At 95% the MS(3)-GARCH(1,1) combined with Pearson's type IV distribution (PIVD) is the best model. This indicates that the proposed best-fitting model is reasonable and can be used for predicting the daily number of deaths due to Covid-19.Conclusion: The MS(3)-GARCH(1,1)-PIVD model provides a reliable and accurate method for predicting the minimum number of death due to Covid-19. The accuracy of the proposed model will assist policymakers, academics and health practitioners in forecasting the volatility of future health-related deaths in which the predictability of volatility plays an integral role in health risk management. [ABSTRACT FROM AUTHOR]

This paper analyzes the dependence and the impact of oil (WTI) and gold (XAU) prices, in addition to stock market volatility (VIX) over Bitcoin prices (BTC) from February 2012 to September 2021 through a multivariate GARCH model type diagonal BEKK, with the intention to prove the existence of a volatility spillover between such variables. Under the grouping of three pairs (BTC-VIX, BTC-WTI y BTC-XAU), findings confirm the presence of such effect and for its current determination, certain period lagging shocks have a special relevance in the relationship BTC - XAU. On the other hand, the dynamical conditional correlation is negative between the BTC and VIX index since investors seek to hedge against risk with the purchase of other assets in the face of uncertainty in a market. [ABSTRACT FROM AUTHOR]

Investigating the volatility of financial assets is fundamental to risk management. This study used generalized Autoregressive Conditional Heteroscedastic Volatility models to evaluate the volatility of the long term interest rate of Nigeria's financial market. We also incorporated three innovations distributions viz: the Gaussian, the student-t, and the Generalized Error Distribution (GED) in the modeling process under the maximum likelihood estimation method. The results show that GARCH (GED) is the most performing model for describing the volatility of three and twenty-year interest rate returns while TARCH (GED) is the most suitable model for describing the volatility of five and ten-year interest rate returns in Nigeria. The preferred models will help in the development of tools for effective risk management by monitoring the behavior of long term interest rates.

Black-Scholes model plays a very significant role in the world of quantitative finance. In this paper, the focus are on both nonlinear and linear Black-Scholes (BS) equations with numerical approximations. We aim to find an effective numerical approximations for Black-Scholes model. Several models from the most relevant class of nonlinear Black-Scholes equations with European option are analyzed in this study. The problem is approached by transforming the problem into a convection-diffusion equation and later it is approximated with the help of finite difference method (Crank-Nicolson). The result of finite difference schemes (Crank-Nicolson) for several volatility models are presented, including the Risk Adjusted Pricing Methodology (RAPM), Leland's model and the Barles'-Soner's Model. At the same time, it is attempted to illustrate a comparison of different volatility models. In the case of linear Black-Scholes model, we approximate the model with finite difference method (FDM) and finite element method (FEM) and compare the results. All the numerical schemes are implemented in MATLAB and corresponding graphs are also presented here. [ABSTRACT FROM AUTHOR]

Basel penalties originate from VaR violations, based on the type of variance models and the estimation of VaR thresholds employed by the bank, which leads to excess regulatory capital charge held and can affect banks’ risk taking ability and thus its profitability. Different approaches are used to evaluate the performance of variance models and forecasts of the VaR thresholds i.e. hypothesis tests, back-testing procedures and Basel Accord regulatory calculations for penalty zones. A multi-criteria performance measure named as analytical hierarchy process has been introduced in this study. The approach helps in evaluating and selection of the optimal internal model based on performance evaluation techniques objectively which, in turn, may help in the selection of best volatility internal model for reduction in the VaR violations and thus leaving more capital in the hands of the banks for profitable ventures. [ABSTRACT FROM AUTHOR]

We combine machine learning tree-based algorithms with the usage of low and high prices and suggest a new approach to forecasting currency covariances. We apply three algorithms: Random Forest Regression, Gradient Boosting Regression Trees and Extreme Gradient Boosting with a tree learner. We conduct an empirical evaluation of this procedure on the three most heavily traded currency pairs in the Forex market: EUR/USD, USD/JPY and GBP/USD. The forecasts of covariances formulated on the three applied algorithms are predominantly more accurate than the Dynamic Conditional Correlation model based on closing prices. The results of the analyses indicate that the GBRT algorithm is the bestperforming method. [ABSTRACT FROM AUTHOR]

Background: A study on the dengue daily counting in São Paulo city in a fixed period of time is assumed considering a new regression model approch. Methods: Under a Bayesian approach, it is introduced a polynomial linear regression model in presence of some covariates which could affect the counts of dengue in São Paulo city considered in the logarithm scale, combined with existing stochastic volatility models usually assumed in financial data analysis. Markov Chain Monte Carlo methods are used to get the posterior summaries of interest. Results: The new model approach showed some advantages when compared to other existing times series models usually used to model epidemics data. Conclusion: The use of the polynomial regression model combined with existing volatility models under a Bayesian approach showed that it is possible to get very accurate fit for the counting dengue data in São Paulo city where it is possible to jointly model the means and volatilities (variances) of the epidemiological dengue time series.

This study analyzes the spillover effects of volatility in the Russian stock market. The paper applies the Diebold–Yilmaz connectedness methodology to characterize volatility spillovers between Russian assets. The spectral representation of the forecast variance decomposition proposed by Baruník and Křehlik is used to describe the connectivity in short-term (up to 5 days), medium-term (6–20 days) and long-term (more than 20 days) time frequencies. Additionally, two new augmented models are developed and applied to evaluate conditional spillover effects in different sectors of the Russian economy for the period from January 2012 to June 2021. It is shown that spillover effects increase significantly during political and economic crises and decrease during periods of relative stability. The rising of the overall level of spillovers in the Russian stock market coincides in time with the political crisis of 2014, the intensification of anti-Russian sanctions in 2018 and the fall in oil prices and the start of the pandemic in 2020. With the consideration of the augmented models it can be argued that a significant part of the long-term spillover effects on the Russian stock market may be caused by the influence of external economic and political factors. However, volatility spillovers generated by internal Russian idiosyncratic shocks are short-term. Thus, the proposed approach provides new information on the impact of external factors on volatility spillovers in the Russian stock market.

In this paper we investigate the oil spot price dynamics and volatility in Brazilian Reais (the local currency). The current policy on price adjustments of refined products caused enormous turmoil in May 2018 due to a trucking strike over high diesel prices. The current price adjustments follow variations of oil prices and exchange rate. This issue opens a discussion of the price transfers of imported oil products. The classic volatility analysis is difficult to carry out because price series in local currency are based on data with non-homogeneous frequency. We use a Gaussian factor model to estimate the oil spot price and then follow two different theoretical approaches to infer the dynamics and volatility in local currency. From these approaches, one can infer how high the volatility is compared to the first future contract taken as a benchmark. Furthermore, one can also monitor the short-term price forecasts once the conditional distribution is known from the dynamics established. This analysis can be applied to any type of refined product depending on the existence of a liquid term structure of future prices. [ABSTRACT FROM AUTHOR]

We provide a novel approach of estimating a regime-switching nonlinear and non-Gaussian state-space model based on a particle learning scheme. In particular, we extend the particle learning method in Liu, J., and M. West. 2001. "Combined Parameter and State Estimation in Simulation-Based Filtering." In Sequential Monte Carlo Methods in Practice, 197–223. Springer. by constructing a new proposal distribution for the latent regime index variable that incorporates all available information contained in the current and past observations. The Monte Carlo simulation result implies that our approach categorically outperforms a popular existing algorithm. For empirical illustration, the proposed algorithm is used to analyze the underlying dynamics of US excess stock return. [ABSTRACT FROM AUTHOR]

Abstract International visitor arrivals are considered to be a major source of foreign exchange, tourism-related employment and other tourism-related activities. This study used SARIMAX/(E)GARCH volatility models to forecast visitor arrivals by air transport to New Zealand from its eight key tourist source markets (Australia, Canada, China, Japan, South Korea, Germany, the United Kingdom (UK) and the United States (US)) and control macroeconomic factors together with global and regional structural changes. The empirical models reveal that the macroeconomic factors contributed at various levels to different markets, and the models we provided made accurate and reliable forecasts for visitor arrivals by air transport from all studied markets. The results from the markets for Germany, Japan, South Korea and the UK showed that significantly negative tourism demand shocks increased the volatility of tourism arrivals, more than positive tourism demand shocks of equal magnitude. Accordingly, the findings of this study will allow policy-makers in the New Zealand tourism sector and other stakeholders (e.g. airline management) to better understand the impacts on the volatility of visitor arrivals to New Zealand. Highlights • The empirical models reveal that the macroeconomic factors contributed at various levels to different markets. • Negative tourism demand shocks increased the volatility of tourism arrivals more than the positive shocks. • Exogenous shocks are highly important in explaining the volatility of tourism demand for New Zealand. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we introduce a new specification of the BEKK model, where its parameters are estimated with the use of closing and additionally low and high prices. In an empirical application, we show that the use of additional information related to low and high prices in the formulation of the BEKK model improved the estimation of the covariance matrix of returns and increased the accuracy of covariance and variance forecasts based on this model, compared with using closing prices only. This analysis was performed for the following three most heavily traded currency pairs in the Forex market: EUR/USD, USD/JPY, and GBP/USD. The main result obtained in this study is robust to the applied forecast evaluation criterion. This issue is important from a practical viewpoint, because daily low and high prices are available with closing prices for most financial series. [ABSTRACT FROM AUTHOR]

Copyright of Muhasebe ve Vergi Uygulamalari Dergisi (MUVU) / Journal of Accounting & Taxation Studies (JATS) is the property of Ankara Serbest Muhasebeci Mali Musavirler Odasi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper revisits several existing volatility models by the light of extremal dependence, that is, serial dependence in extreme returns. First, we investigate the extremal properties of different high-frequency-based volatility processes and show that only a subset of them can generate dependence in the extremes. Second, we corroborate the empirical evidence on extremal dependence in financial returns, showing that extreme returns present strong and persistent correlation and that extreme negative returns are much more correlated than positive ones. Finally, a large empirical analysis suggests that only models exhibiting extremal dependence and endowed with a leverage component can appropriately explain extreme events. [ABSTRACT FROM AUTHOR]

After interest rate hikes by the FED, the market value of long-duration assets has declined, which in March of 2023 led to a distress of banks subject to a sudden increase in deposit withdrawals. First, Silicon Valley Bank and Signature Bank were subject to such runs and were taken over by the FDIC, while First Republic Bank by JPMorgan Chase in May of 2023. We date the crisis-period and show that increased Twitter-based attention is related to an increased price variation of banks at risk. Our results imply that during crisis periods, pricing can be improved by incorporating attention-based measures. • On the 10th of March 2023, Silicon Valley Bank suffered an unexpected bank run. • We model asset price variation of six banks during the banking crisis of March 2023. • To capture investor attention, we use high-frequency Twitter-based attention data. • We date the crisis to 15:30 on 8th of March to 12:00 on 21st of March 2023. • Volume of tweets related to the six banks at risk led to increased price variation. [ABSTRACT FROM AUTHOR]

This paper empirically examines short- and long-run relationships between foreign direct investments (FDI) and volatility of foreign portfolio investments (FPI) in 12 Central and Eastern European (CEE) countries. We use the Generalized Autoregressive Conditional Heteroskedasticity models to calculate volatility of the FPIs. We utilize the second generation panel unit root test, panel-Wald causality test procedure and panel cointegration analysis allowing for structural breaks, and cross-sectional dependence. The results strongly suggest that a decrease in FPI volatility is followed by an increase in FDI in the long-run, and this indicates economies that advance in capital liberalization benefit from increases in FDI. However, the relationship in opposite direction in the long-run is valid in only half of the countries studied. In short-run, we observe that the former relationship is valid only in Turkey, the Czech Republic, and Lithuania; where the latter is valid only in Latvia.

We study intraday stochastic volatility for four liquid stocks traded on the New York Stock Exchange using a new dynamic Skellam model for high-frequency tick-by-tick discrete price changes. Since the likelihood function is analytically intractable, we rely on numericalmethods for its evaluation. Given the high number of observations per series per day (1000 to 10,000), we adopt computationally efficient methods including Monte Carlo integration. The intraday dynamics of volatility and the high number of trades without price impact require nontrivial adjustments to the basic dynamic Skellam model. In-sample residual diagnostics and goodness-of-fit statistics show that the final model provides a good fit to the data. An extensive dayto- day forecasting study of intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling approaches. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Volatility estimation in financial markets has always been a challenge especially in time of crisis. Once asset prices and investment decisions are highly sensitive to such variable, many different models have been proposed in literature. This article estimates the volatility from a new family of stochastic volatility models called non-Gaussian State Space Models, a subclass of state space models where it is possible to compute exact likelihood. Volatilities of important Asian and Oceanian stock market indexes have been estimated and compared to APARCH model estimates. Results showed that non-Gaussian State Space Models outperformed significantly in both in-sample and forecasting cases. [ABSTRACT FROM AUTHOR]

Copyright of Institute of Business Administration-Management Journal / Isletme Iktisadi Enstitüsü Yönetim Dergisi is the property of Istanbul University, Institute of Business Administration and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this paper is to develop a general, unified approach, based on some partial estimation functions which we call ' Z-process', to some change point problems in mathematical statistics. The method proposed can be applied not only to ergodic models but also to some models where the Fisher information matrix is random. Applications to some concrete models, including a parametric model for volatilities of diffusion processes are presented. Simulations for randomly time-transformed Brownian bridge process appearing as the limit of the proposed test statistics are performed with computer intensive use. [ABSTRACT FROM AUTHOR]

Turkish economy has been suffering from rises in financial flows since the last two decades that these flows have raised financial stability challenges across emerging economies including Turkey. Regarding the ability of the central banks to decrease the financial risks including volatile exchange rate, the Central Bank of the Republic of Turkey has designed and implemented a new policy mix. In this study, we investigated the effect of new policy instruments (IRC, RRR and ROM) on the volatilities of US dollar, euro, British pound and basket rate for Turkish economy between January 2, 2002 and December 9, 2014 by using ARMA-GARCH, ARMA-EGARCH and SWARCH models. From the estimation results, we could not reach enough evidence that the IRC and RRR instruments could decrease the volatilities of exchange rates under investigation while the ROM instrument was successful, especially on US dollar and basket rate. We also found strong evidence in favour of asymmetric volatility, indicating that the positive shocks led to greater exchange rate volatility than negative ones. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of volatility in the world energy price on aggregate economic activity in an unbalanced panel data framework for 10 developing Asian countries: Bangladesh, PR China, India, Indonesia, Malaysia, Pakistan, the Philippines, Thailand, Turkey and Vietnam. We use both the realized volatility and the generalized autoregressive conditional heteroskedasticity models to measure the volatility in the world energy price. Empirical findings from dynamic panel data estimations show that the volatility in world energy price is negatively associated with the aggregate economic activity. Using the common correlated effects panel estimation techniques, we also systematically examine uncertain transmission channel of the world energy price volatility on the aggregate economic activity in each economy and obtain the most impressive negative effects in Turkey, PR China and India, respectively. [ABSTRACT FROM AUTHOR]

The onset of the Russo–Ukrainian crisis has led to the rapid depreciation of the Russian ruble. In this study, we model intraday price fluctuations of the USD/RUB and the EUR/RUB exchange rates from the 1st of December 2021 to the 7th of March 2022. Our approach is novel in that instead of using daily (low-frequency) measures of attention and investor's expectations, we use intraday (high-frequency) data: google searches and implied volatility to proxy investor's attention and expectations. We show that both approaches are useful in predicting intraday price fluctuations of the two exchange rates, although implied volatility encompasses intraday attention. • Ruble's price variation was extreme on the onset of the Russian invasion to Ukraine. • We model intraday realized variance of the Ruble to USD and EUR. • Intraday google searches explain future intraday variation of Ruble. • Intraday implied volatility also explains future intraday variation of the Ruble. • Implied volatility is more informative as subsumes information from google searches. [ABSTRACT FROM AUTHOR]