Estimated Return Rate Volatility Results Using Garch (1,1) Model

Table 4.4: Results of estimated volatility of return using GARCH (1,1) model

𝛾 0	𝛼 0	𝛼 1	𝛽	𝜃 1	𝜃 2
ACB	-0.0227	0.0406 ***	0.1355 ***	0.8540 ***	0.0002 ***	-0.1544
	(0.3579)	(0.0001)	(0.0000)	(0.0000)	(0.0000)	(0.6559)
CTG	0.0266	2.6799 ***	0.0578	0.5084 ***	-0.0009 ***	-0.1928
	(0.7174)	(0.0046)	(0.1006)	(0.0051)	(0.0000)	(0.9731)
EIB	0.0054	0.2908 ***	0.2552 ***	0.6553 ***	0.0006 ***	-1.0911
	(0.8821)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.2718)
MBB	0.0407	1.8149 ***	0.0719 **	0.5085 ***	-0.0007 ***	-0.1130
	(0.5089)	(0.0030)	(0.0360)	(0.0024)	(0.0000)	(0.9671)
NVB	-0.0794	2.4939 ***	0.2744 ***	0.5384 ***	-0.0013 ***	-5.4401 ***
	(0.2664)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)
SHB	-0.0316	0.1747 ***	0.1006 ***	0.8778 ***	-0.0002 **	-1.2878
	(0.5444)	(0.0000)	(0.0000)	(0.0000)	(0.0202)	(0.1248)
STB	0.01847	1.6004 ***	0.3109 ***	0.3570 ***	-0.0005 ***	-2.5284 ***
	(0.6696)	(0.0000)	(0.0000)	(0.0000)	(0.0010)	(0.0000)
Number of statistically significant cases list	0/7	7/7	6/7	7/7	7/7	2/7

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• Qos Assurance Methods for Multimedia Communications zt2i3t4l5ee zt2a3gs zt2a3ge zc2o3n4t5e6n7ts low. The EF PHB requires a sufficiently large number of output ports to provide low delay, low loss, and low jitter. EF PHBs can be implemented if the output port's bandwidth is sufficiently large, combined with small buffer sizes and other network resources dedicated to EF packets, to allow the router's service rate for EF packets on an output port to exceed the arrival rate λ of packets at that port. This means that packets with PHB EF are considered with a pre-allocated amount of output bandwidth and a priority that ensures minimum loss, minimum delay and minimum jitter before being put into operation. PHB EF is suitable for channel simulation, leased line simulation, and real-time services such as voice, video without compromising on high loss, delay and jitter values. Figure 2.10 Example of EF installation Figure 2.10 shows an example of an EF PHB implementation. This is a simple priority queue scheduling technique. At the edges of the DS domain, EF packet traffic is prioritized according to the values ​​agreed upon by the SLA. The EF queue in the figure needs to output packets at a rate higher than the packet arrival rate λ. To provide an EF PHB over an end-to-end DS domain, bandwidth at the output ports of the core routers needs to be allocated in advance to ensure the requirement μ > λ. This can be done by a pre-configured provisioning process. In the figure, EF packets are placed in the priority queue (the upper queue). With such a length, the queue can operate with μ > λ. Since EF was primarily used for real-time services such as voice and video, and since real-time services use UDP instead of TCP, RED is generally not suitable for EF queues because applications using UDP will not respond to random packet drop and RED will strip unnecessary packets. 2.2.4.2 Assured Forwarding (AF) PHB PHB AF is defined by RFC 2597. The purpose of PHB AF is to deliver packets reliably and therefore delay and jitter are considered less important than packet loss. PHB AF is suitable for non-real-time services such as applications using TCP. PHB AF first defines four classes: AF1, AF2, AF3, AF4. For each of these AF classes, packets are then classified into three subclasses with three distinct priority levels. Table 2.8 shows the four AF classes and 12 AF subclasses and the DSCP values ​​for the 12 AF subclasses defined by RFC 2597. RFC 2597 also allows for more than three separate priority levels to be added for internal use. However, these separate priority levels will only have internal significance. PHB Class PHB Subclass Package type DSCP AF4 AF41 Short 100010 AF42 Medium 100100 AF43 High 100110 AF3 AF31 Short 011010 AF32 Medium 011100 AF33 High 011110 AF2 AF21 Short 010010 AF22 Medium 010100 AF23 High 010110 AF1 AF11 Short 001010 AF12 Medium 001100 AF13 High 001110 Table 2.8 AF DSCPs The AF PHB ensures that packets are forwarded with a high probability of delivery to the destination within the bounds of the rate agreed upon in an SLA. If AF traffic at an ingress port exceeds the pre-priority rate, which is considered non-compliant or “out of profile”, the excess packets will not be delivered to the destination with the same probability as the packets belonging to the defined traffic or “in profile” packets. When there is network congestion, the out of profile packets are dropped before the in profile packets are dropped. When service levels are defined using AF classes, different quantity and quality between AF classes can be realized by allocating different amounts of bandwidth and buffer space to the four AF classes. Unlike EF, most AF traffic is non-real-time traffic using TCP, and the RED queue management strategy is an AQM (Adaptive Queue Management) strategy suitable for use in AF PHBs. The four AF PHB layers can be implemented as four separate queues. The output port bandwidth is divided into four AF queues. For each AF queue, packets are marked with three “colors” corresponding to three separate priority levels. In addition to the 32 DSCP 1 groups defined in Table 2.8, 21 DSCPs have been standardized as follows: one for PHB EF, 12 for PHB AF, and 8 for CSCP. There are 11 DSCP 1 groups still available for other standards. 2.2.5.Example of Differentiated Services We will look at an example of the Differentiated Service model and mechanism of operation. The architecture of Differentiated Service consists of two basic sets of functions: Edge functions: include packet classification and traffic conditioning. At the inbound edge of the network, incoming packets are marked. In particular, the DS field in the packet header is set to a certain value. For example, in Figure 2.12, packets sent from H1 to H3 are marked at R1, while packets from H2 to H4 are marked at R2. The labels on the received packets identify the service class to which they belong. Different traffic classes receive different services in the core network. The RFC definition uses the term behavior aggregate rather than the term traffic class. After being marked, a packet can be forwarded immediately into the network, delayed for a period of time before being forwarded, or dropped. We will see that there are many factors that affect how a packet is marked, and whether it is forwarded immediately, delayed, or dropped. Figure 2.12 DiffServ Example Core functionality: When a DS-marked packet arrives at a Diffservcapable router, the packet is forwarded to the next router based on Per-hop behavior is associated with packet classes. Per-hop behavior affects router buffers and the bandwidth shared between competing classes. An important principle of the Differentiated Service architecture is that a router's per-hop behavior is based only on the packet's marking or the class to which it belongs. Therefore, if packets sent from H1 to H3 as shown in the figure receive the same marking as packets from H2 to H4, then the network routers treat the packets exactly the same, regardless of whether the packet originated from H1 or H2. For example, R3 does not distinguish between packets from h1 and H2 when forwarding packets to R4. Therefore, the Differentiated Service architecture avoids the need to maintain router state about separate source-destination pairs, which is important for network scalability. Chapter Conclusion Chapter 2 has presented and clarified two main models of deploying and installing quality of service in IP networks. While the traditional best-effort model has many disadvantages, later models such as IntServ and DiffServ have partly solved the problems that best-effort could not solve. IntServ follows the direction of ensuring quality of service for each separate flow, it is built similar to the circuit switching model with the use of the RSVP resource reservation protocol. IntSer is suitable for services that require fixed bandwidth that is not shared such as VoIP services, multicast TV services. However, IntSer has disadvantages such as using a lot of network resources, low scalability and lack of flexibility. DiffServ was born with the idea of ​​solving the disadvantages of the IntServ model. DiffServ follows the direction of ensuring quality based on the principle of hop-by-hop behavior based on the priority of marked packets. The policy for different types of traffic is decided by the administrator and can be changed according to reality, so it is very flexible. DiffServ makes better use of network resources, avoiding idle bandwidth and processing capacity on routers. In addition, the DifServ model can be deployed on many independent domains, so the ability to expand the network becomes easy. Chapter 3: METHODS TO ENSURE QoS FOR MULTIMEDIA COMMUNICATIONS In packet-switched networks, different packet flows often have to share the transmission medium all the way to the destination station. To ensure the fair and efficient allocation of bandwidth to flows, appropriate serving mechanisms are required at network nodes, especially at gateways or routers, where many different data flows often pass through. The scheduler is responsible for serving packets of the selected flow and deciding which packet will be served next. Here, a flow is understood as a set of packets belonging to the same priority class, or originating from the same source, or having the same source and destination addresses, etc. In normal state when there is no congestion, packets will be sent as soon as they are delivered. In case of congestion, if QoS assurance methods are not applied, prolonged congestion can cause packet drops, affecting service quality. In some cases, congestion is prolonged and widespread in the network, which can easily lead to the network being "frozen", or many packets being dropped, seriously affecting service quality. Therefore, in this chapter, in sections 3.2 and 3.3, we introduce some typical network traffic load monitoring techniques to predict and prevent congestion before it occurs through the measure of dropping (removing) packets early when there are signs of impending congestion. 3.1. DropTail method DropTail is a simple, traditional queue management method based on FIFO mechanism. All incoming packets are placed in the queue, when the queue is full, the later packets are dropped. Due to its simplicity and ease of implementation, DropTail has been used for many years on Internet router systems. However, this algorithm has the following disadvantages: − Cannot avoid the phenomenon of “Lock out”: Occurs when 1 or several traffic streams monopolize the queue, making packets of other connections unable to pass through the router. This phenomenon greatly affects reliable transmission protocols such as TCP. According to the anti-congestion algorithm, when locked out, the TCP connection stream will reduce the window size and reduce the packet transmission speed exponentially. − Can cause Global Synchronization: This is the result of a severe “Lock out” phenomenon. Some neighboring routers have their queues monopolized by a number of connections, causing a series of other TCP connections to be unable to pass through and simultaneously reducing the transmission speed. After those monopolized connections are temporarily suspended, Once the queue is cleared, it takes a considerable amount of time for TCP connections to return to their original speed. − Full Queue phenomenon: Data transmitted on the Internet often has an explosion, packets arriving at the router are often in clusters rather than in turn. Therefore, the operating mechanism of DropTail makes the queue easily full for a long period of time, leading to the average delay time of large packets. To avoid this phenomenon, with DropTail, the only way is to increase the router's buffer, this method is very expensive and ineffective. − No QoS guarantee: With the DropTail mechanism, there is no way to prioritize important packets to be transmitted through the router earlier when all are in the queue. Meanwhile, with multimedia communication, ensuring connection and stable speed is extremely important and the DropTail algorithm cannot satisfy. The problem of choosing the buffer size of the routers in the network is to “absorb” short bursts of traffic without causing too much queuing delay. This is necessary in bursty data transmission. The queue size determines the size of the packet bursts (traffic spikes) that we want to be able to transmit without being dropped at the routers. In IP-based application networks, packet dropping is an important mechanism for indirectly reporting congestion to end stations. A solution that prevents router queues from filling up while reducing the packet drop rate is called dynamic queue management. 3.2. Random elimination method – RED 3.2.1 Overview RED (Random Early Detection of congestion; Random Early Drop) is one of the first AQM algorithms proposed in 1993 by Sally Floyd and Van Jacobson, two scientists at the Lawrence Berkeley Laboratory of the University of California, USA. Due to its outstanding advantages compared to previous queue management algorithms, RED has been widely installed and deployed on the Internet. The most fundamental point of their work is that the most effective place to detect congestion and react to it is at the gateway or router. Source entities (senders) can also do this by estimating end-to-end delay, throughput variability, or the rate of packet retransmissions due to drop. However, the sender and receiver view of a particular connection cannot tell which gateways on the network are congested, and cannot distinguish between propagation delay and queuing delay. Only the gateway has a true view of the state of the queue, the link share of the connections passing through it at any given time, and the quality of service requirements of the traffic flows. The RED gateway monitors the average queue length, which detects early signs of impending congestion (average queue length exceeding a predetermined threshold) and reacts appropriately in one of two ways: − Drop incoming packets with a certain probability, to indirectly inform the source of congestion, the source needs to reduce the transmission rate to keep the queue from filling up, maintaining the ability to absorb incoming traffic spikes. − Mark “congestion” with a certain probability in the ECN field in the header of TCP packets to notify the source (the receiving entity will copy this bit into the acknowledgement packet). Figure 3. 1 RED algorithm The main goal of RED is to avoid congestion by keeping the average queue size within a sufficiently small and stable region, which also means keeping the queuing delay sufficiently small and stable. Achieving this goal also helps: avoid global synchronization, not resist bursty traffic flows (i.e. flows with low average throughput but high volatility), and maintain an upper bound on the average queue size even in the absence of cooperation from transport layer protocols. To achieve the above goals, RED gateways must do the following: − The first is to detect congestion early and react appropriately to keep the average queue size small enough to keep the network operating in the low latency, high throughput region, while still allowing the queue size to fluctuate within a certain range to absorb short-term fluctuations. As discussed above, the gateway is the most appropriate place to detect congestion and is also the most appropriate place to decide which specific connection to report congestion to. − The second thing is to notify the source of congestion. This is done by marking and notifying the source to reduce traffic. Normally the RED gateway will randomly drop packets. However, if congestion If congestion is detected before the queue is full, it should be combined with packet marking to signal congestion. The RED gateway has two options: drop or mark; where marking is done by marking the ECN field of the packet with a certain probability, to signal the source to reduce the traffic entering the network. − An important goal that RED gateways need to achieve is to avoid global synchronization and not to resist traffic flows that have a sudden characteristic. Global synchronization occurs when all connections simultaneously reduce their transmission window size, leading to a severe drop in throughput at the same time. On the other hand, Drop Tail or Random Drop strategies are very sensitive to sudden flows; that is, the gateway queue will often overflow when packets from these flows arrive. To avoid these two phenomena, gateways can use special algorithms to detect congestion and decide which connections will be notified of congestion at the gateway. The RED gateway randomly selects incoming packets to mark; with this method, the probability of marking a packet from a particular connection is proportional to the connection's shared bandwidth at the gateway. − Another goal is to control the average queue size even without cooperation from the source entities. This can be done by dropping packets when the average size exceeds an upper threshold (instead of marking it). This approach is necessary in cases where most connections have transmission times that are less than the round-trip time, or where the source entities are not able to reduce traffic in response to marking or dropping packets (such as UDP flows). 3.2.2 Algorithm This section describes the algorithm for RED gateways. RED gateways calculate the average queue size using a low-pass filter. This average queue size is compared with two thresholds: minth and maxth. When the average queue size is less than the lower threshold, no incoming packets are marked or dropped; when the average queue size is greater than the upper threshold, all incoming packets are dropped. When the average queue size is between minth and maxth, each incoming packet is marked or dropped with a probability pa, where pa is a function of the average queue size avg; the probability of marking or dropping a packet for a particular connection is proportional to the bandwidth share of that connection at the gateway. The general algorithm for a RED gateway is described as follows: [5] For each packet arrival Caculate the average queue size avg If minth ≤ avg < maxth div.maincontent .s1 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 15pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s3 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s7 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s8 { color: black; font-family:Arial, sans-serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .s9 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s10 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 6pt; } div.maincontent .s11 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s12 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; } div.maincontent .s13 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-d
• Rate of Tourist Satisfaction, Return and Recommendation of Ninh Thuan
• The impact of interest rate and exchange rate fluctuations on profitability and stock return fluctuations at Vietnamese commercial banks - 9
• Research Results on the Relationship Between Exchange Rate Level and FDI Capital in Vietnam and Discussion
• Regression Results of Bank Profitability Model from Interest Rate and Other Profit Determinants

Note: ***, **, * results are statistically significant at the 1%, 5%, 10% significance level

Source: Regression results from Eviews 8.0 software

Table 4.4 shows the estimated coefficient results from the GARCH (1,1) model showing the relationship between fluctuations in bank stock returns and fluctuations in interest rates and exchange rates.

The regression coefficient of ARCH parameter (α 1 ) has a smaller value than GARCH parameter (β) and is statistically significant in most of the research cases including 7 bank stock returns (only the case of TCMP Vietnam Joint Stock Commercial Bank for Industry and Trade CTG is not statistically significant), the coefficient α 1 with a small value shows that the presence of previous period shocks in the fluctuations of bank stock returns has little impact on the conditional variance in the current period. The research results also show that the GARCH parameter in the model is larger and has very strong statistical significance (significant at the 1% significance level) in all research variables, showing strong evidence of previous period shocks, that is, fluctuations in bank stock returns in the previous period will significantly impact fluctuations in stock returns in the current period. This is also easily explained when the volatility of a stock is often continuous over a certain period of time, so the volatility of the stock return rate of the current period will be affected by the volatility of previous periods.

The estimated coefficient 𝜃 1 shows the impact of interest rate fluctuations on the volatility of bank stock returns. The estimated coefficient is statistically significant in all the studied cases including 7 bank stock returns. The coefficients 𝜃 1 are negative in most of the cases, namely 5/7 significant cases and positive in 2/7 significant cases, namely ACB and EIB stocks. This shows that when the average interbank interest rate fluctuates, it will be inversely correlated with the profitability of bank stocks. One of the explanations for the increase in interest rate fluctuations in the volatility of bank stock returns is that when the average interbank interest rate changes, it leads to changes in the cost of capital. In Vietnam, banks

cannot control interest rate risk even though banks have derivative financial instruments but they are not used effectively to prevent risks, or the derivative financial market is not yet developed so banks do not have derivative financial instruments suitable for the maturity of assets and liabilities to hold, leading to fluctuations in interest rates increasing interest rate risk, causing fluctuations in interest rates applied in the daily operations of the bank, thereby affecting the bank's profits, as demonstrated by the rate of return on the bank's own stocks. The results of the study are consistent with the findings of Elyasiani and Mansur (2003) and Saadet Kasman, Gulin Vardar and Gokce Tunc (2011).

The estimated coefficient 𝜃 2 shows the impact of exchange rate fluctuations on the volatility of bank stock returns, the coefficient 𝜃 2 is found to be statistically significant in two of the seven research cases, NVB stocks and STB stocks, which means that fluctuations in exchange rates lead to fluctuations in bank stock returns but at a lower level than the impact of interest rates. Along with the globalization of the economy, especially the increasingly integrated financial and banking sector, in order to increase profits and meet customer needs, foreign exchange trading is essential in the operations of commercial banks. Besides, exchange rate risk is a risk that commercial banks cannot avoid when foreign exchange transactions increase. Vietnamese banks may not be able to fully hedge their exposure to exchange rate risk with derivative instruments as currency derivative instruments in Vietnam are currently underdeveloped, which is one of the risks that could lead to heavier exposure to increasing exchange rate risk.

The results of interest rate and exchange rate fluctuations affecting the fluctuations of bank stock returns are one of the new points of the research in the Vietnamese market. Interest rate and exchange rate factors not only affect the stock returns but also affect the fluctuations of stock returns. The results of the paper

The study shows that the return on bank stocks can change suddenly in both directions, either suddenly increasing or suddenly decreasing due to fluctuations in exchange rates and interest rates of monetary policy. Vietnam's exchange rate in the research period was almost unchanged, especially in the period 2011 - 2015 (only fluctuated when the State Bank adjusted the exchange rate), so the impact of exchange rate fluctuations on the return on bank stocks was weaker than that of interest rate fluctuations.

Chapter 5. Conclusion and recommended solutions

5.1. Conclusion

In the period of economic globalization, financial liberalization, monetary policy changes, free capital flows, the development of derivative financial instruments, the study of the impact of interest rates and exchange rates on profitability and fluctuations in profitability of banking stocks is especially important and necessary for joint stock commercial banks in Vietnam.

The study uses price data of eight bank stocks listed on the HOSE and HNX stock exchanges during the period from November 1, 2011 to November 30, 2017. By using the OLS model and the GARCH model, the study investigates the impact of market returns represented by the VNINDEX index (MRK), fluctuations in the average overnight interbank interest rate (INT) and fluctuations in the exchange rate (FX) on the returns of eight commercial joint stock bank stocks listed on the HOSE and HNX stock exchanges, including the following stock codes: ACB, CTG, EIB, MBB, NVB, SHB, STB and VCB. Initially, the study used the OLS method to estimate the regression results, however, due to the phenomenon of heteroscedasticity in the OLS model through the ARCH test, the GARCH model was used to estimate more effectively than the OLS method. With the GARCH model, the study also showed the impact of market returns, interest rate fluctuations, exchange rate fluctuations and past return shocks on the fluctuations of bank stock returns.

Overall, the study shows that changes in interest rates have a negative impact on bank stock returns. In addition, the study shows that there is no evidence of the impact of changes in exchange rates on bank stock returns. In addition, the study shows that

The results show that the market index return has a strong and positive impact on the bank stock return, and this impact is stronger than interest rate and exchange rate. This shows that the market return is the main factor affecting the bank stock return, and its ability to explain the impact is stronger than the fluctuations in exchange rate and interest rate.

Evidence is found for emerging financial markets such as Vietnam, which do not provide hedging opportunities in the market with financial derivatives on interest rates and exchange rates for banks.

The study also shows that fluctuations in exchange rates and interest rates not only affect bank stock returns but also affect the fluctuations in bank stock returns, in which interest rate fluctuations have a stronger impact than exchange rate fluctuations. In addition, the results also show that fluctuations in bank stock returns are affected by past shocks.

5.2. Policy recommendations

The results of the paper provide important information for investors re-evaluating bank stocks, bank managers, risk managers in banks and provide information for monetary policy makers in Vietnam.

For investors: need to closely monitor monetary policy to make the right choice of investments in the context of unstable interest rates and exchange rates affecting stock returns, need to increase monitoring and change portfolio, diversify portfolio to prevent well when risks can occur at any time.

Commercial banks need to develop risk prevention procedures for banking activities related to exchange rates and interest rates, and strengthen and effectively use them.

Measures to hedge exchange rate and interest rate risks using derivative financial instruments available on the market.

The State Bank closely monitors macroeconomic developments and domestic and foreign currency markets to proactively and promptly implement appropriate management solutions. Synchronously and flexibly manage monetary policy tools to stabilize the currency market, facilitate the restructuring of the stock market and capital market, stabilize the foreign exchange market, control the growth rate of total means of payment and credit according to the set orientation, and avoid negative impacts on stock prices. The State Bank manages interest rates in accordance with macroeconomic developments, inflation and the currency market to stabilize interest rates. Continue to direct credit institutions to save costs and improve business efficiency to stabilize mobilization interest rates and create conditions to strive to reduce lending interest rates to share difficulties with borrowers while ensuring financial safety in operations. In addition, the State Bank needs to flexibly manage the central exchange rate, implement a reasonable monetary policy to support exchange rate stability, intervene in the foreign exchange market when necessary, especially in cases where foreign currency supply and demand are imbalanced, causing instability in the foreign exchange market, and prevent foreign currency speculation that affects exchange rate stability and the macro economy. Finally, the State Bank needs to develop a derivatives market to prevent risks, fully meeting the maturity and type requirements for risk management.

5.3. Limitations of the topic and recommendations for future research

Currently, there are 31 joint stock commercial banks operating in Vietnam. Due to the lack of data, the study uses a limited sample of eight Vietnamese joint stock commercial banks listed on the HOSE and HNX stock markets. This makes the research results representative and not really covering the entire market, including banks with smaller asset sizes. Regarding the research period, the study covers the period from November 1, 2011 to November 30, 2017.

While the first bank in the research sample was first listed on July 12, 2006 on the HOSE stock exchange, the research conclusion is only valid for the above period.

In addition, the study is using the average overnight interbank interest rate, ignoring risk-free rates or longer-term interest rates, and the exchange rate in the study has only had a short period of using the central rate, which fluctuated more frequently in the period 2016-2017, the period 2011-2015, the exchange rate almost did not fluctuate much when using the average interbank rate, except for the State Bank's exchange rate adjustments.

Future studies should use a longer sample size and a larger sample size including listed banks on the stock market to better assess the economic situation of banks in the past, which is more general for the banking market in Vietnam.

In the future, when the central exchange rate period is longer, the following research paper may provide evidence of the impact of exchange rate on the profitability of bank stocks in Vietnam. In addition, psychological factors and investor behavior can be used to include in the research model.