scholarly journals On the Gains of Using High Frequency Data in Portfolio Selection

2018 ◽  
Vol 65 (4) ◽  
pp. 365-383
Author(s):  
Rui Pedro Brito ◽  
Helder Sebastião ◽  
Pedro Godinho
Keyword(s):  
Relative Risk ◽  
Risk Aversion ◽  
Portfolio Selection ◽  
High Frequency ◽  
Low Frequency ◽  
High Frequency Data ◽  
Relative Risk Aversion ◽  
Frequency Data ◽  
Crra Preferences ◽  
Mean Variance

Abstract This paper analyzes empirically the performance gains of using high frequency data in portfolio selection. Assuming Constant Relative Risk Aversion (CRRA) preferences, with different relative risk aversion levels, we compare low and high frequency portfolios within mean-variance, mean-variance-skewness and mean-variance-skewness-kurtosis frameworks. Using data on fourteen stocks of the Euronext Paris, from January 1999 to December 2005, we conclude that the high frequency portfolios outperform the low frequency portfolios for every out-of-sample measure, irrespectively to the relative risk aversion coefficient considered. The empirical results also suggest that for moderate relative risk aversion the best performance is always achieved through the jointly use of the realized variance, skewness and kurtosis. This claim is reinforced when trading costs are taken into account.

scholarly journals Does the high-frequency data is helpful for forecasting Russian inflation?

2021 ◽  
Vol 37 (2) ◽  
pp. 318-343
Author(s):  
Dmitriy Tretyakov ◽  
◽  
Nikita Fokin ◽  
Keyword(s):  
Monetary Policy ◽  
Central Bank ◽  
High Frequency ◽  
Inflation Targeting ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Policy Regime ◽  
The Future ◽  
Forecasting Inflation

Due to the fact that at the end of 2014 the Central Bank made the transition to a new monetary policy regime for Russia — the inflation targeting regime, the problem of forecasting inflation rates became more relevant than ever. In the new monetary policy regime, it is important for the Bank of Russia to estimate the future inflation rate as quickly as possible in order to take measures to return inflation to the target level. In addition, for effective monetary policy, the households must trust the actions of monetary authorities and they must be aware of the future dynamics of inflation. Thus, to manage inflationary expectations of economic agents, the Central Bank should actively use the information channel, publish accurate forecasts of consumer price growth. The aim of this work is to build a model for nowcasting, as well as short-term forecasting of the rate of Russian inflation using high-frequency data. Using this type of data in models for forecasting is very promising, since this approach allows to use more information about the dynamics of macroeconomic indicators. The paper shows that using MIDAS model with weekly frequency series (RUB/USD exchange rate, the interbank rate MIACR, oil prices) has more accurate forecast of monthly inflation compared to several basic models, which only use low-frequency data.

scholarly journals Shake, Rattle, and Roll

2001 ◽  
Vol 123 (11) ◽  
pp. 56-58
Author(s):  
John DeGaspari
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Low Frequencies ◽  
Mems Accelerometer ◽  
Running Shoes ◽  
Low Costs ◽  
New Applications ◽  
Thin Reservoir

This article highlights that airbag has been a boon to MEMS business. Sales of the tiny accelerometers that sense when the bags should deploy have helped to drive down prices significantly since the devices were first implemented. Now, the high volumes, low costs, and dependable performance of micro devices are opening the way to new applications. Sneaker companies are looking at MEMS accelerometers in running shoes to act as speedometers of sorts. Advantage of the MEMS accelerometer is that it has a wide bandwidth, capable of reading high as well as low frequencies. High-frequency data provides information about thin reservoir zones, faults, and changes that are taking place as fluids are being drained from pores in the rock, said Denver. Higher frequency signals are critical to accurate interpretation. Low-frequency signals are useful in identifying the type of rock, be it sandstone, shale, or carbonate, for example. The VectorSeis is as rugged as a conventional geophone and can be successfully deployed in down-hole environments to get a closer reading of a reservoir.

scholarly journals Non-parametric upper bounds on risk aversion with mean-variance utility

2021 ◽  
Author(s):  
Kevin Denny
Keyword(s):  
Relative Risk ◽  
Risk Aversion ◽  
Upper Bounds ◽  
Relative Risk Aversion ◽  
Mean And Variance ◽  
The Mean ◽  
Mean Variance ◽  
Non Parametric

Based on a simple prior, this note derives upper bounds for the coefficient of absolute & relative risk aversion if utility can be written as depending linearly on the mean and variance of income.

scholarly journals Spatial price transmission analysis in Ghanaian agricultural markets: Does the data frequency improve our estimation?

2011 ◽  
Vol 4 (2) ◽  
pp. 301-316
Author(s):  
Joseph Amikuzuno
Keyword(s):  
Developing Countries ◽  
High Frequency ◽  
Low Frequency ◽  
Price Transmission ◽  
Price Adjustment ◽  
High Frequency Data ◽  
Agricultural Markets ◽  
Frequency Data ◽  
Monthly Data ◽  
Data Frequency

Unavailability of high frequency weekly or daily data compels most studies of price transmission in developing countries to use low frequency monthly data for their analyses. Analysing price dynamics, especially in agricultural markets, with monthly data may however yield imprecise price adjustment parameters and lead to wrong inferences on price dynamics. This is because agricultural markets in developing countries usually operate daily or weekly, not monthly, as implied by the market analysts who use low frequency data. This paper investigates the relevance of data frequency in price transmission analysis by using a standard and a threshold vector error correction model to estimate and compare price adjustment parameters for high frequency semi-weekly data and low frequency monthly data obtained from five major fresh tomato markets in Ghana. The results reveal that adjustment parameters estimated from the low frequency data are higher in all cases than those estimated from the high frequency data. There is reason to suspect that using low frequency data, as confirmed in some literature, leads to an overestimation of the price adjustment parameters. More research involving a large number of observations is however needed to enhance our knowledge about the usefulness of high frequency data in price transmission analysis.

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